analysing case study data

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Qualitative case study data analysis: an example from practice

Affiliation.

1 School of Nursing and Midwifery, National University of Ireland, Galway, Republic of Ireland.
PMID: 25976531
DOI: 10.7748/nr.22.5.8.e1307

Aim: To illustrate an approach to data analysis in qualitative case study methodology.

Background: There is often little detail in case study research about how data were analysed. However, it is important that comprehensive analysis procedures are used because there are often large sets of data from multiple sources of evidence. Furthermore, the ability to describe in detail how the analysis was conducted ensures rigour in reporting qualitative research.

Data sources: The research example used is a multiple case study that explored the role of the clinical skills laboratory in preparing students for the real world of practice. Data analysis was conducted using a framework guided by the four stages of analysis outlined by Morse ( 1994 ): comprehending, synthesising, theorising and recontextualising. The specific strategies for analysis in these stages centred on the work of Miles and Huberman ( 1994 ), which has been successfully used in case study research. The data were managed using NVivo software.

Review methods: Literature examining qualitative data analysis was reviewed and strategies illustrated by the case study example provided. Discussion Each stage of the analysis framework is described with illustration from the research example for the purpose of highlighting the benefits of a systematic approach to handling large data sets from multiple sources.

Conclusion: By providing an example of how each stage of the analysis was conducted, it is hoped that researchers will be able to consider the benefits of such an approach to their own case study analysis.

Implications for research/practice: This paper illustrates specific strategies that can be employed when conducting data analysis in case study research and other qualitative research designs.

Keywords: Case study data analysis; case study research methodology; clinical skills research; qualitative case study methodology; qualitative data analysis; qualitative research.

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Data Analytics Case Study: Complete Guide in 2024

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

With data analyst case questions, you will need to answer two key questions:

What metrics should I propose?
How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

TRY CHECKING: Marketing Analytics Case Study Guide

Data Analytics Case Study Question: Sample Solution

Data Analytics Case Study Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

Column	Type
	VARCHAR
	INTEGER
	INTEGER
	INTEGER

search_events table

Column	Type
	INTEGER
	VARCHAR
	BOOLEAN

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

Who uses this social network? How do they interact with each other?
Has there been any performance issues that might be causing the problem?
What are the goals of this particular launch?
Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

The decreasing comments issue isn’t a result of a declining user base.
The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

January: 10000 users, 30000 comments, 3 comments/user
February: 20000 users, 50000 comments, 2.5 comments/user
March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses. We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

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Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering.

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience.

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics.

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used.

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato.

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time.

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

Interview transcripts
Surveys with open-ended questions
Contact center transcripts
Texts and documents
Audio and video recordings
Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

Gathering and collecting your qualitative data
Organizing and connecting into your qualitative data
Coding your qualitative data
Analyzing the qualitative data for insights
Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues. It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope, helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered. Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis. Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them. It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations. The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Methods of qualitative data analysis; approaches and techniques to qualitative data analysis

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend, and review analysis is a great place to start. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

G2.com reviews of the product Airtable. You could pull reviews from G2 for your analysis.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.

The benefits of using computer-assisted qualitative data analysis software:

Assists in the organizing of your data
Opens you up to exploring different interpretations of your data analysis
Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

The user interface of CAQDAS software 'NVivo'

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:

Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations ( conversational analytics ), and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Some of qualitative data integrations offered by Thematic

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably. For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
Keep repeating step 2, adding new codes and revising the code description as often as necessary. Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Example of a hierarchical coding frame in qualitative data analysis

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy. Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

Demographic
And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

Calculate overall NPS
Calculate NPS in the subset of responses that do not contain that theme
Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

Visualizing qualitative data: Calculating the impact of a code on your score

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

Analyzing qualitative data: Calculate which codes are linked to increases or decreases in my score

The visualization could look like this:

Visualizing qualitative data trends over time

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Codes displayed by volume within Thematic. You can 'manage themes' to introduce human input.

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs. This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Two examples of qualitative data visualizations within Thematic

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable. Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding. Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places. And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Data Analytics Case Study Guide 2024

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Data Collection and Validation in case study

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Data Preprocessing and Transformation in case study

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Data Modeling and Method Selection in case study

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Interpretation and Contextual Understanding

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

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Data Analysis Case Study: Learn From Humana’s Automated Data Analysis Project

Lillian Pierson, P.E.

Playback speed:

Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.

If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…

Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.

But how you’re in the right place to find out..

As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis.

In the post below, we’ll look at:

A shining data success story;
What went on ‘under-the-hood’ to support that successful data project; and
The exact data technologies used by the vendor, to take this project from pure strategy to pure success

If you prefer to watch this information rather than read it, it’s captured in the video below:

Here’s the url too: https://youtu.be/xMwZObIqvLQ

3 Action Items You Need To Take

To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:

Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck

Step 1: Reflect Upon Your Organization

Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.

Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:

What is the business vision for our organization?
What industries do we primarily support?
What data technologies do we already have up and running, that we could use to generate even more value?
What team members do we have to support a new data project? And what are their data skillsets like?
What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?

Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)

Step 2: Review Data Case Studies

Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies (starting with the one I’m sharing below). I dentify 5 that seem the most promising for your organization given its current set-up.

Humana’s Automated Data Analysis Case Study

The key thing to note here is that the approach to creating a successful data program varies from industry to industry .

Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.

Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.

Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.

Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).

Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer ).

In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.

Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.

The AI listens to cues like the customer’s voice pitch.

If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.

Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.

The Outcome

Customers were happier, and customer service representatives were more engaged..

This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.

The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.

What does this mean for you and your business?

Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.

Humana’s Business Use Cases

Humana’s data analysis case study includes two key business use cases:

Analyzing customer sentiment; and
Suggesting actions to customer service representatives.

Analyzing Customer Sentiment

First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.

In the case of Humana, the actors were:

The health insurance system itself
The customer, and
The customer service representative

As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.

Humana focused on collecting the key data points, shown in the image below, from their customer service operations.

By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’ manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.

Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.

Suggesting actions to customer service representatives.

The second use case for the Humana data program follows on from the data gathered in the first case.

In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.

In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time , to make use of incoming data and help improve customer satisfaction on the spot.

The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:

The tone of voice is too tense
The speed of speaking is high
The customer representative and customer are speaking at the same time

These alerts allowed the Humana customer service representatives to alter their approach immediately , improving the quality of the interaction and, subsequently, the customer satisfaction.

The preconditions for success in this use case were:

The call-related data must be collected and stored
The AI models must be in place to generate analysis on the data points that are recorded during the calls

Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.

Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.

The Technology That Supports This Data Analysis Case Study

I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.

Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.

For cloud data management Cogito uses AWS, specifically the Athena product
For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
They utilize MapReduce, for processing their data
And Cogito also has traditional systems and relational database management systems such as PostgreSQL
In terms of analytics and data visualization tools, Cogito makes use of Tableau
And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)

These data science skill sets support the effective computing, deep learning , and natural language processing applications employed by Humana for this use case.

If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.

Step 3: S elect The “Quick Win” Data Use Case

Still there? Great!

It’s time to close the loop.

Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…

YES ▶ Excellent!

Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.

NO , Lillian – It’s not applicable. ▶ No problem.

Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.

More resources to get ahead...

Get income-generating ideas for data professionals, are you tired of relying on one employer for your income are you dreaming of a side hustle that won’t put you at risk of getting fired or sued well, my friend, you’re in luck..

This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise. Learn more here!

We love helping tech brands gain exposure and brand awareness among our active audience of 530,000 data professionals. If you’d like to explore our alternatives for brand partnerships and content collaborations, you can reach out directly on this page and book a time to speak.

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Education and Communications

How to Analyse a Case Study

Last Updated: April 13, 2024 Fact Checked

This article was co-authored by Sarah Evans . Sarah Evans is a Public Relations & Social Media Expert based in Las Vegas, Nevada. With over 14 years of industry experience, Sarah is the Founder & CEO of Sevans PR. Her team offers strategic communications services to help clients across industries including tech, finance, medical, real estate, law, and startups. The agency is renowned for its development of the "reputation+" methodology, a data-driven and AI-powered approach designed to elevate brand credibility, trust, awareness, and authority in a competitive marketplace. Sarah’s thought leadership has led to regular appearances on The Doctors TV show, CBS Las Vegas Now, and as an Adobe influencer. She is a respected contributor at Entrepreneur magazine, Hackernoon, Grit Daily, and KLAS Las Vegas. Sarah has been featured in PR Daily and PR Newswire and is a member of the Forbes Agency Council. She received her B.A. in Communications and Public Relations from Millikin University. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 413,431 times.

Case studies are used in many professional education programs, primarily in business school, to present real-world situations to students and to assess their ability to parse out the important aspects of a given dilemma. In general, a case study should include, in order: background on the business environment, description of the given business, identification of a key problem or issue, steps taken to address the issue, your assessment of that response, and suggestions for better business strategy. The steps below will guide you through the process of analyzing a business case study in this way.

Step 1 Examine and describe the business environment relevant to the case study.

Describe the nature of the organization under consideration and its competitors. Provide general information about the market and customer base. Indicate any significant changes in the business environment or any new endeavors upon which the business is embarking.

Step 2 Describe the structure and size of the main business under consideration.

Analyze its management structure, employee base, and financial history. Describe annual revenues and profit. Provide figures on employment. Include details about private ownership, public ownership, and investment holdings. Provide a brief overview of the business's leaders and command chain.

Step 3 Identify the key issue or problem in the case study.

In all likelihood, there will be several different factors at play. Decide which is the main concern of the case study by examining what most of the data talks about, the main problems facing the business, and the conclusions at the end of the study. Examples might include expansion into a new market, response to a competitor's marketing campaign, or a changing customer base. [3] X Research source

Step 4 Describe how the business responds to these issues or problems.

Draw on the information you gathered and trace a chronological progression of steps taken (or not taken). Cite data included in the case study, such as increased marketing spending, purchasing of new property, changed revenue streams, etc.

Step 5 Identify the successful aspects of this response as well as its failures.

Indicate whether or not each aspect of the response met its goal and whether the response overall was well-crafted. Use numerical benchmarks, like a desired customer share, to show whether goals were met; analyze broader issues, like employee management policies, to talk about the response as a whole. [4] X Research source

Step 6 Point to successes, failures, unforeseen results, and inadequate measures.

Suggest alternative or improved measures that could have been taken by the business, using specific examples and backing up your suggestions with data and calculations.

Community Q&A

Always read a case study several times. At first, you should read just for the basic details. On each subsequent reading, look for details about a specific topic: competitors, business strategy, management structure, financial loss. Highlight phrases and sections relating to these topics and take notes. Thanks Helpful 0 Not Helpful 0
In the preliminary stages of analyzing a case study, no detail is insignificant. The biggest numbers can often be misleading, and the point of an analysis is often to dig deeper and find otherwise unnoticed variables that drive a situation. Thanks Helpful 0 Not Helpful 0
If you are analyzing a case study for a consulting company interview, be sure to direct your comments towards the matters handled by the company. For example, if the company deals with marketing strategy, focus on the business's successes and failures in marketing; if you are interviewing for a financial consulting job, analyze how well the business keeps their books and their investment strategy. Thanks Helpful 0 Not Helpful 0

Do not use impassioned or emphatic language in your analysis. Business case studies are a tool for gauging your business acumen, not your personal beliefs. When assigning blame or identifying flaws in strategy, use a detached, disinterested tone. Thanks Helpful 16 Not Helpful 4

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Thanks for reading our article! If you’d like to learn more about business writing, check out our in-depth interview with Sarah Evans .

↑ https://www.gvsu.edu/cms4/asset/CC3BFEEB-C364-E1A1-A5390F221AC0FD2D/business_case_analysis_gg_final.pdf
↑ https://bizfluent.com/12741914/how-to-analyze-a-business-case-study
↑ http://www.business-fundas.com/2009/how-to-analyze-business-case-studies/
↑ https://writingcenter.uagc.edu/writing-case-study-analysis
http://college.cengage.com/business/resources/casestudies/students/analyzing.htm

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Top 10 real-world data science case studies.

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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12 Data Science Case Studies: Across Various Industries

Home Blog Data Science 12 Data Science Case Studies: Across Various Industries

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI. An excellent way to achieve this is through reading industry data science case studies. I recommend checking out Data Science With Python course syllabus to start your data science journey. In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this Data Science course content .

Let’s look at the top data science case studies in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more.

List of Data Science Case Studies 2024

Hospitality: Airbnb focuses on growth by analyzing customer voice using data science. Qantas uses predictive analytics to mitigate losses
Healthcare: Novo Nordisk is Driving innovation with NLP. AstraZeneca harnesses data for innovation in medicine
Covid 19: Johnson and Johnson use s d ata science to fight the Pandemic
E-commerce: Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction
Supply chain management: UPS optimizes supp l y chain with big data analytics
Meteorology: IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''
Entertainment Industry: Netflix u ses data science to personalize the content and improve recommendations. Spotify uses big data to deliver a rich user experience for online music streaming
Banking and Finance: HDFC utilizes Big D ata Analytics to increase income and enhance the banking experience
Urban Planning and Smart Cities: Traffic management in smart cities such as Pune and Bhubaneswar
Agricultural Yield Prediction: Farmers Edge in Canada uses Data science to help farmers improve their produce
Transportation Industry: Uber optimizes their ride-sharing feature and track the delivery routes through data analysis
Environmental Industry: NASA utilizes Data science to predict potential natural disasters, World Wildlife analyzes deforestation to protect the environment

Top 12 Data Science Case Studies

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing, tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science. A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.

How Qantas uses predictive analytics to mitigate losses

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.

2. Data Science in Healthcare

The Healthcare sector is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision, Natural Language Processing (NLP) is frequently used in the healthcare domain to study the published textual research data. 

A. Pharmaceutical

Driving innovation with NLP: Novo Nordisk. Novo Nordisk uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.

How AstraZeneca harnesses data for innovation in medicine. AstraZeneca is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis. 

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses data science and AI to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%. 

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

C. Wearable Technology

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different pharmaceutical companies across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.

How Johnson and Johnson uses data science to fight the Pandemic 

The data science team at Johnson and Johnson leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in E-commerce

In the e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.

Amazon uses data science to personalize shopping experiences and improve customer satisfaction. Amazon is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Data science case studies reveal how Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.   

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management, drive better performance, and more. 

Optimizing supply chain with big data analytics: UPS

UPS is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.

6. Data Science in Meteorology

Weather prediction is an interesting application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department. 

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani'' 

Most d ata scientist’s responsibilities rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area, IMD (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science. 

7. Data Science in the Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience. 

How Netflix uses data science to personalize the content and improve recommendations

Netflix is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users. 

Spotify uses big data to deliver a rich user experience for online music streaming

Personalized online music streaming is another area where data science is being used. Spotify is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience

One of the major private banks in India, HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.

9. Data Science in Urban Planning and Smart Cities

Data Science can help the dream of smart cities come true! Everything, from traffic flow to energy usage, can get optimized using data science techniques. You can use the data fetched from multiple sources to understand trends and plan urban living in a sorted manner.

The significant data science case study is traffic management in Pune city. The city controls and modifies its traffic signals dynamically, tracking the traffic flow. Real-time data gets fetched from the signals through cameras or sensors installed. Based on this information, they do the traffic management. With this proactive approach, the traffic and congestion situation in the city gets managed, and the traffic flow becomes sorted. A similar case study is from Bhubaneswar, where the municipality has platforms for the people to give suggestions and actively participate in decision-making. The government goes through all the inputs provided before making any decisions, making rules or arranging things that their residents actually need.

10. Data Science in Agricultural Prediction

Have you ever wondered how helpful it can be if you can predict your agricultural yield? That is exactly what data science is helping farmers with. They can get information about the number of crops they can produce in a given area based on different environmental factors and soil types. Using this information, the farmers can make informed decisions about their yield and benefit the buyers and themselves in multiple ways.

Data Science in Agricultural Yield Prediction

Farmers across the globe and overseas use various data science techniques to understand multiple aspects of their farms and crops. A famous example of data science in the agricultural industry is the work done by Farmers Edge. It is a company in Canada that takes real-time images of farms across the globe and combines them with related data. The farmers use this data to make decisions relevant to their yield and improve their produce. Similarly, farmers in countries like Ireland use satellite-based information to ditch traditional methods and multiply their yield strategically.

11. Data Science in the Transportation Industry

Transportation keeps the world moving around. People and goods commute from one place to another for various purposes, and it is fair to say that the world will come to a standstill without efficient transportation. That is why it is crucial to keep the transportation industry in the most smoothly working pattern, and data science helps a lot in this. In the realm of technological progress, various devices such as traffic sensors, monitoring display systems, mobility management devices, and numerous others have emerged.

Many cities have already adapted to the multi-modal transportation system. They use GPS trackers, geo-locations and CCTV cameras to monitor and manage their transportation system. Uber is the perfect case study to understand the use of data science in the transportation industry. They optimize their ride-sharing feature and track the delivery routes through data analysis. Their data science case studies approach enabled them to serve more than 100 million users, making transportation easy and convenient. Moreover, they also use the data they fetch from users daily to offer cost-effective and quickly available rides.

12. Data Science in the Environmental Industry

Increasing pollution, global warming, climate changes and other poor environmental impacts have forced the world to pay attention to environmental industry. Multiple initiatives are being taken across the globe to preserve the environment and make the world a better place. Though the industry recognition and the efforts are in the initial stages, the impact is significant, and the growth is fast.

The popular use of data science in the environmental industry is by NASA and other research organizations worldwide. NASA gets data related to the current climate conditions, and this data gets used to create remedial policies that can make a difference. Another way in which data science is actually helping researchers is they can predict natural disasters well before time and save or at least reduce the potential damage considerably. A similar case study is with the World Wildlife Fund. They use data science to track data related to deforestation and help reduce the illegal cutting of trees. Hence, it helps preserve the environment.

Where to Find Full Data Science Case Studies?

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.

What Are the Skills Required for Data Scientists?

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.

Opt for the best business analyst training elevating your expertise. Take the leap towards becoming a distinguished business analysis professional

Conclusion

These were some interesting data science case studies across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc. 

Almost all the companies looking to leverage the power of big data begin with a SWOT analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue. Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study:

Defining the problem statement and strategy to solve it 
Gather and pre-process the data by making relevant assumptions 
Select tool and appropriate algorithms to build machine learning /deep learning models
Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary.

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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Top 20 Analytics Case Studies in 2024

We adhere to clear ethical standards and follow an objective methodology . The brands with links to their websites fund our research.

Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making , and enables the launching of more personalized products.

In this article, our research covers:

How to measure analytics success?

What are some analytics case studies.

According to Gartner CDO Survey, the top 3 critical success factors of analytics projects are:

Creation of a data-driven culture within the organization,
Data integration and data skills training across the organization,
And implementation of a data management and analytics strategy.

The success of the process of analytics depends on asking the right question. It requires an understanding of the appropriate data required for each goal to be achieved. We’ve listed 20 successful analytics applications/case studies from different industries.

During our research, we examined that partnering with an analytics consultant helps organizations boost their success if organizations’ tech team lacks certain data skills.

Enterprise	Industry of End User	Business Function	Type of Analytics	Description	Results	Analytics Vendor or Consultant
Fitbit	Health/ Fitness	Consumer Products	IoT Analytics		Better lifestyle choices for users.	Bernard Marr&Co.
Dominos	Food	Marketing	Marketing Analytics		Increased monthly revenue by 6%. Reduced ad spending cost by 80% y-o-y.	Google Analytics 360 and DBI
Brian Gravin Diamond	Luxury/ Jewelry	Sales	Sales Analytics	Improving their online sales by understanding user pre-purchase behaviour.	New line of designs in the website contributed to 6% boost in sales. 60% increase in checkout to the payment page.	Google Analytics Enhanced Ecommerce
*	Marketing Automation	Marketing	Marketing Analytics		Conversions improved by the rate of 10x	Google Analytics and Marketo
Build.com	Home Improvement Retail	Sales	Retail Analytics	Providing dynamic online pricing analysis and intelligence	Increased sales & profitability Better, faster pricing decisions	Numerator Pricing Intel and Numerator
Ace Hardware	Hardware Retail	Sales	Pricing Analytics		Increased exact and ‘like’ matches by 200% across regional markets.	Numerator Pricing Intel and Numerator
SHOP.COM	Online Comparison in Retail	Supply Chain	Retail Analytics	increased supply chain and onboarding process efficiencies.	57% growth in drop ship orders $89K customer serving support savings Improved customer loyalty	SPS Commerce Analytics and SPS Commerce
Bayer Crop Science	Agriculture	Operations	Edge Analytics/IoT Analytics		Faster decision making to help farmers optimize growing conditions	AWS IoT Analytics AWS Greengrass
Farmers Edge	Agriculture	Operations	Edge Analytics	Collecting data from edge in real-time	Better farm management decisions that maximize productivity and profitability.	Microsoft Azure IoT Edge
Lufthansa	Transportation	Operations	Augmented Analytics/Self-service reporting		Increase in the company’s efficiency by 30% as data preparation and report generation time has reduced.	Tableau
Walmart	Retail	Operations	Graph Analytics		Increased revenue by improving customer experience	Neo4j
Cerved	Risk Analysis	Operations	Graph Analytics			Neo4j
Nextplus	Communication	Sales/ Marketing	Application Analytics	With Flurry, they analyzed every action users perform in-app.	Boosted conversion rate 5% in one month	Flurry
Telenor	Telco	Maintenance	Application Analytics		Improved customer experience	AppDynamics
Cepheid	Molecular diagnostics	Maintenance	Application Analytics		Eliminating the need for manual SAP monitoring.	AppDynamics
*	Telco	HR	Workforce Analytics	Finding out what technical talent finds most and least important.	Improved employee value proposition Increased job offer acceptance rate Increased employee engagement	Crunchr
Hostelworld	Vacation	Customer experience	Marketing Analytics		500% higher engagement across websites and social 20% Reduction in cost per booking	Adobe Analytics
Phillips	Retail	Marketing	Marketing Analytics		Testing ‘Buy’ buttons increased clicks by 20%. Encouraging a data-driven, test-and-learn culture	Adobe
*	Insurance	Security	Behavioral Analytics/Security Analytics	Identifying anomalous events such as privileged account logins from a machine for the first time, rare time of day logins, and rare/suspicious process runs.		Securonix
Under Armour	Retail	Operations	Retail Analytics			IBM Watson

*Vendors have not shared the client name

For more on analytics

If your organization is willing to implement an analytics solution but doesn’t know where to start, here are some of the articles we’ve written before that can help you learn more:

AI in analytics: How AI is shaping analytics
Edge Analytics in 2022: What it is, Why it matters & Use Cases
Application Analytics: Tracking KPIs that lead to success

Finally, if you believe that your business would benefit from adopting an analytics solution, we have data-driven lists of vendors on our analytics hub and analytics platforms

We will help you choose the best solution tailored to your needs:

Next to Read

14 case studies of manufacturing analytics in 2024, iot analytics: benefits, challenges, use cases & vendors [2024].

Your email address will not be published. All fields are required.

Related research

Predictive Process Mining in '24: Top 3 use cases & case studies

What is Analytics? How is it Evolving in 2024?

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ACIS 2002 Proceedings

Four steps to analyse data from a case study method.

John Atkinson , Charles Sturt University

Four steps are proposed to assist the novice researcher analyse their data that has been collected using a case study method. The first step proposes the creation of a data repository using basic relational database theory. The second step involves creating codes to identify the respective ‘chunks’ of data. These resulting codes are then analysed and rationalised. The third step involves analysing the case study data by generating a variety of reports. The fourth step generates the final propositions by linking the rationalised codes back to the initial propositions and where appropriate new propositions are generated. The outcome of these steps is a series of propositions that reflect the nature of the data associated with the case studies data.

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Atkinson, John, "Four Steps to Analyse Data from a Case Study Method" (2002). ACIS 2002 Proceedings . 38. https://aisel.aisnet.org/acis2002/38

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The Business Case for Human Behavior Research

Researchers can gain greater insights into consumer preferences by combining traditional research methods with tools that measure subjects’ physical and neurological responses to stimuli.
When business schools add behavioral research initiatives, they promote interdisciplinary collaboration, solidify corporate connections, and develop novel research ideas.
Companies are looking for business graduates who can make use of massive amounts of physiological data and understand the challenges of using it ethically.

What do you focus on when you see a new ad or spot a new product on the shelf? How do you react when you’re trying to navigate an unfamiliar web page or manage a stressful situation? If researchers were taking certain physiological measurements, they could track your body’s cues to understand what captures and holds your attention, how you make decisions, and how you interact with the people around you.

The study of human behavior is often referred to as “neuroeconomics,” “neuromarketing,” or “organizational neuroscience.” In this field, researchers combine principles from psychology, neuroscience, and economics to better understand how physiological factors reflect the way subjects behave, make decisions, and interact. These physiological metrics enable organizations to design more effective advertising, optimize work environments, improve ergonomics, prevent accidents, and enhance user experiences.

Previously, the field mainly focused on neurophysiology measurements (such as EEGs and fMRIs ) to track measurements of the brain for insights into the mind. Today, we have tools that are more accessible, both in terms of cost and technical ability. These biometric tools—often referred to as biosensors or wearable sensors—can analyze eye movements, facial expressions, heart activity, galvanic skin responses (also known as electrodermal activity), respiration rates, and voice patterns to measure a subject’s attention level, stress, cognitive workload, arousal, and engagement.

Researchers can combine this information with the subject’s self-reported responses, gathered through traditional research methods such as surveys, focus groups, interviews, and observation. By analyzing both the conscious and subconscious ways the participant processes stimuli, researchers can make more nuanced inferences about the participant’s decision-making processes, emotions, perceptions, preferences, and biases.

Across most industries today, more organizations are turning to physiological metrics to track and predict customer behavior. At the same time, individual consumers are relying on biosensor tools in their daily lives as they use smartphones, smartwatches, and fitness trackers to count their steps, assess their sleep, or monitor their overall health.

Because physiological research has become so important to companies and consumers, it’s essential for business schools to address it in their scholarship activities and in their classrooms.

Adding Up the Advantages

Schools that incorporate human behavior research into their teaching and research will find that it offers a host of benefits:

It promotes interdisciplinary collaboration. Economics, marketing, psychology, medicine, and even art offer different lenses through which analysts can examine human behavior. Information systems, computer science, and engineering departments provide methods for producing, processing, and analyzing physiological data. As representatives from various departments gather in the same spaces, they engage in constructive collaborations and generate ideas that can be leveraged to attract students, media attention, and funding opportunities.

“This perpetuates a cycle of fundraising, grants, and rankings, all of which contribute to the ultimate mission of advancing education and research,” says Mike Breazeale, director of the Market Innovation Lab & Observatory ( MILO ) at Mississippi State University in Starkville.

When human behavior research is combined with traditional research methods, analysts can develop a deeper understanding of the scenarios they’re studying.

It facilitates corporate connections. Large and small businesses in many industries can benefit from human behavior research. A local restaurant might use such research to test menu layouts , while a major music producer might use it to predict whether a song will become popular . Because companies might not have the necessary equipment or expertise to gather physiological data on their own, many will look to partner with business schools to conduct the research.

It produces novel insights. When human behavior research is combined with traditional research methods, analysts can develop a deeper understanding of the scenarios they’re studying. For instance, student research projects have looked at gender bias in sports viewership and how subtitles affect comprehension and perception.

It gives students a competitive edge in the job market. As companies seek to gain better insights into their customers, CEOs will look for graduates with a knowledge of physiological metrics.

“Companies need to build their own expertise with those who know how to apply these new methodologies,” explains Mike Mickunas, former global vice president of insights and analytics at Kellogg’s and current professor of marketing at Michigan State University’s Broad College of Business in Lansing.

It provides added insights for students in certain fields. Human behavior research will be particularly valuable for students passionate about careers that revolve around these critical business topics:

Sustainability and environmental, social, and governance (ESG) issues. Business graduates who have a deep understanding of human behavior will be able develop strategies that guide consumers toward making more environmentally conscious decisions.
Diversity, equity, and inclusion. Physiological metrics can help supervisors uncover the unconscious biases and assumptions that employees hold about their co-workers. This in turn enables business leaders to design DEI policies that will be effective.
Trust. In a world rife with misinformation, trust is a major factor in how consumers relate to brands, employees relate to management, and people relate to stories. Using biometric tools, researchers can see how subjects respond viscerally to brands or public figures, and they can compare this information to how subjects self-report their levels of trust or distrust. Such research gives business leaders a more nuanced understanding of how people react to various sources.

Bringing Biometrics Into the Classroom

Schools that want to integrate physiological research into their curricula often rely on tools provided by third-party vendors. One such company is iMotions , which makes software that supports a wide variety of hardware for various physiological metrics.

Some schools start by simply bringing biometric tools into their classrooms. Because web cameras can be used to track eye movements and facial expressions, students in large classes can design projects that combine these physiological metrics with traditional research methods such as surveys. Because students can design studies and analyze results on their own laptops, this approach is also suitable for remote learning.

Some students have said that if they had not been able to study subconscious responses, they would have had to choose completely different topics for their projects.

As an example, in a recent one-week marketing course at VIA University College in Horsens, Denmark, students conducted 35 distinct studies using browser-based software. Students designed and distributed A/B split test studies of social media content they had created. By the end of the week, they had gained hands-on experience in learning how small changes affected participants’ engagement, and they had analyzed and presented their findings.

When more specialized hardware is needed, professors can conduct classes in labs that contain equipment for tracking eye movements, galvanic skin response, heart rate, and respiration. For instance, at High Point University’s Phillips School of Business in North Carolina, marketing students work in BEACON Lab to collaborate on real-world research with leading brands. Students have looked at logo design in social media ads, brand recognition in the fast-food industry, and sports-viewing experiences on virtual screens versus traditional flat-screen media.

“This is more than just a class, it’s a résumé builder,” notes Miguel Sahagun, the Charles T. Ingram Associate Professor of Marketing and director of neuromarketing at BEACON Lab. “Students start from scratch having nothing but an idea. And by the end of the semester, they end up writing a manuscript and presenting it.”

Still other schools, such as the Quinlan School of Business at Loyola University Chicago, take a mixed approach. There, assistant professor Dinko Bačić teaches Analytical Decision-Making, a course where undergraduates first learn about physiological research through a classroom tool. Then students use the school’s User Experience and Biometrics Lab (UX & B) to conduct studies they design themselves.

In a recent semester, Quinlan students conducted seven studies that were approved by an institutional review board. Students had the opportunity to present their work at a campus event that showcases university-based research—an event that typically is dominated by research in the STEM fields of science, technology, engineering, and math. Students also drafted manuscripts to submit to academic journals.

Some of Bačić’s students have said that if they had not been able to study subconscious responses, they would have had to choose completely different topics for their projects. One student added that doing “research such as this has provided me invaluable insights into today’s data-driven landscape.”

Confronting the Challenges

It’s important for students not only to gain hands-on experience with biometric tools, but also to understand the two main challenges facing companies when it comes to physiological research:

Dealing with data. There’s a lot of it, which leads to a risk of overload. Many metrics are recorded per second, or even per millisecond, meaning that one person watching a 30-second video one time with one sensor can produce tens of thousands of data points.

In addition, the data is easy to collect, so it accumulates quickly. Some physiological metrics—such as analysis of eye movements, facial expressions, voice levels, and respiration—can be recorded remotely with the cameras and microphones many people have on their laptops. Add in smartwatches and other wearables, and the opportunities to gather data seem infinite.

Graduates who have designed, executed, and analyzed physiological data will develop the creativity and critical thinking skills needed to use big data to drive business strategies.

“I believe that the big data we see now is nothing compared to what will come in the future when we start harnessing physiological data and working with it,” observes Bačić.

Companies are still figuring out how to use all that information . While artificial intelligence tools that help users organize and visualize data are increasingly available, business leaders often are unsure of how to gain useful insights from the information they’ve collected. It’s like having the freshest ingredients and the finest cooking tools, but no recipe.

To turn out the analysts that companies need, business schools should provide their students with firsthand experience in hypothesis testing, data analysis, and interdisciplinary collaboration. Graduates who have had opportunities to design, execute, and analyze physiological data will develop the creativity and critical thinking skills needed to use big data to drive business strategies.

Evaluating ethics. Many ethical implications surround data in the digital age. To prevent the misuse of physiological data, executives need to understand how it is acquired, anonymized, and analyzed. Because AI often is used to extract useful insights from physiological data, CEOs also must be prepared to engage in the discussion about the responsible use of AI . This means students must have these same discussions while they’re still in business school.

Facing the Future

Tomorrow’s business leaders will help shape future policies regarding the hardware and software that generate and handle physiological data. For this reason, today’s business students need to gain firsthand experience with these tools while participating in ethical discussions about the use of this technology. They will need to understand how to balance the need for personal privacy with the imperative to leverage technology to improve society.

By showing students how to navigate the way forward with physiological data, schools will ensure that companies have access to even deeper insights into their customer bases and their businesses.

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Open access
Published: 09 August 2024

Factors associated with incomplete tuberculosis preventive treatment: a retrospective analysis of six-years programmatic data in Cambodia

Yom An 1 , 2 &
Kim Eam Khun 1 , 3

Scientific Reports volume 14 , Article number: 18458 ( 2024 ) Cite this article

160 Accesses

Metrics details

Health care
Infectious diseases
Medical research
Respiratory tract diseases

Tuberculosis (TB) preventive treatment (TPT) effectively prevents the progression from TB infection to TB disease. This study explores factors associated with TPT non-completion in Cambodia using 6-years programmatic data (2018–2023) retrieved from the TB Management Information System (TB-MIS). Out of 14,262 individuals with latent TB infection (LTBI) initiated with TPT, 299 (2.1%) did not complete the treatment. Individuals aged between 15–24 and 25–34 years old were more likely to not complete the treatment compared to those aged < 5 years old, with aOR = 1.7, p = 0.034 and aOR = 2.1, p = 0.003, respectively. Individuals initiated with 3-month daily Rifampicin and Isoniazid (3RH) or with 6-month daily Isoniazid (6H) were more likely to not complete the treatment compared to those initiated with 3-month weekly Isoniazid and Rifapentine (3HP), with aOR = 2.6, p < 0.001 and aOR = 7, p < 0.001, respectively. Those who began TPT at referral hospitals were nearly twice as likely to not complete the treatment compared to those who started the treatment at health centers (aOR = 1.95, p = 0.003). To improve TPT completion, strengthen the treatment follow-up among those aged between 15 and 34 years old and initiated TPT at referral hospitals should be prioritized. The national TB program should consider 3HP the first choice of treatment.

Mortality during tuberculosis treatment in South Africa using an 8-year analysis of the national tuberculosis treatment register

The effect of empirical and laboratory-confirmed tuberculosis on treatment outcomes

Tuberculosis treatment outcomes and associated factors among tuberculosis patients treated at healthcare facilities of Motta Town, Northwest Ethiopia: a five-year retrospective study

Introduction.

Latent tuberculosis infection (LTBI) is a condition in which a person has been infected with the tuberculosis (TB) bacteria but does not have any symptoms of TB disease 1 . It is estimated that about 25% of the world’s population is infected with TB 2 . Tuberculosis preventive treatment (TPT), a course of antibiotics, is an effective intervention for preventing the progression from LTBI to TB disease and it is one of the key interventions recommended by the World Health Organization (WHO) to achieve the End TB Strategy targets 3 . The United Nations High-Level Meeting on Tuberculosis in 2018 set a target to initiate TPT for 30 million people between 2018 and 2022. However, as of 2022, only 15.5 million people, or 52% of the target, had received TPT 4 .

Non-completion of TB prophylaxis among people with LTBI is influenced by a number of factors. A case control study in Brazil found that drug use, TB treatment default by the index case and drug intolerance were significantly associated with TPT non-completion 5 . In Portugal, treatment failure of TB infection was associated with those older than 15 years old, born outside Portugal, having a chronic disease, alcohol drinking, intravenous drug user, and using 3RH 6 . For children and adolescents, failure to complete 9-month TB prophylaxis with isoniazid included those aged 15–18 years old, non-Hispanic participants, development hepatitis, and isoniazid side effects 7 . Drug side effects, transportation issues and conflicts with working schedule or travel plan were found as factors associated with TPT non-completion among Vietnamese immigrants in Southern California 8 . In Japan, drug side effects and non-Japanese born LTBI were found as risk factors for LTBI treatment failure 9 .

Cambodia is a country with a high burden of TB, with an estimated of 320 new cases of TB cases per 100,000 people in 2022 10 . The estimation of LTBI’s proportion in Cambodia is higher than global average 11 . The National LTBI guidelines in Cambodia prioritizes several high-risk groups for LTBI management. These include people living with human immunodeficiency virus (HIV) irrespective of CD4 cell count and antiretroviral therapy status, all HIV-negative close contacts of individuals with bacteriologically confirmed pulmonary tuberculosis (TB) following an exposure risk assessment, and immunocompromised persons 12 . Based on the guideline, the national TB program recommends three TPT regiments including six months of daily Isoniazid (6H), three months of weekly Isoniazid and Rifapentine (3HP), and three months of daily Rifampicin and Isoniazid (3RH) 13 . Eligibility criteria for TPT include the absence of TB disease and the absence of contraindications (acute and chronic hepatitis, regular and heavy alcohol consumption, and symptoms of peripheral neuropathy) 14 . Those eligible for TPT are assessed by trained healthcare providers in accordance with the national guideline. TB disease screening are done based on the following TB symptoms: current cough, fever, unexplained weight loss, night sweats, lymph nodes and Pott sign 12 . If a person exhibited any of these symptoms, further investigations will be done before TPT initiation. TPT is prescribed and monitored by healthcare providers at referral hospitals and at health centers.

Despite efforts by the national TB program, TPT coverage among key target population, such as newly diagnosed HIV positive and household contacts of bacteriologically confirmed pulmonary TB were only 53% and 34% respectively 10 . In addition to this low TPT initiation, no study has been conducted to understand the magnitude of TPT non-completion and its associated factors. A recent published qualitative research focused only on the challenges in TPT implementation among children 15 . Given this limited scientific evidence, it is necessary to explore the magnitude and factors associated with TPT non-completion in the country. The aim of this study is to identify factors associated with TPT non-completion in Cambodia using 6-years programmatic data (2018–2023).

Study design

It was a retrospective analysis of six-years programmatic data between 2018 and 2023, retrieved from TB management information system (TB-MIS). It is a web-based platform developed by the National Center for Tuberculosis and Leprosy Control that captures data across key aspects of TB control, including TPT from public health facilities in the whole country.

This study included all TPT eligible individuals, irrespective of age. All participants were initiated with a treatment regimen containing any type of TPT. The analysis encompassed data on both individuals who successfully completed the prescribed TPT regimen and those classified as TPT non-completion due to loss to follow-up or discontinuation of the treatment. Those who had not started TPT, died during TPT course, were diagnosed with TB during the TPT course, or were undergoing TPT, were excluded from the study. (Fig. 1 ).

Flow chart of subjects included in the data analysis

Data management and statistical analysis

We retrieved programmatic data between 2018 and 2023 from TB-MIS. Sociodemographic data were included such as age, sex, nationality, TPT regimens, and TPT initiation health facilities. Dependent variable is TPT completion status. Those who complete and incomplete TPT regimens were coded as 0 and 1 respectively. Explanatory variables were age in years (< 5, 5–14, 15–24, 25–34, 35–44, 45–54, 55–64, and ≥ 65), sex (male, female), nationality (Cambodian and others), TPT regimens (3HP, 3RH, 6H), and TPT initiation places (health centers or referral hospitals). Chi-square or Fisher’s exact test was computed to describe characteristics of study participants. Bivariate analysis was conducted to assess initial associations between TPT non-completion and explanatory variables. Any p-value < 0.10 identified in bivariate model were included in the multivariate logistic regression analysis. Based on literature reviews, age and sex were automatically included in the regression model, regardless of their significance level. We calculated Odds Ratios (OR) to measure the magnitude of the association between TPT non-completion and explanatory variables. Any explanatory variables with p-value < 0.05 in multivariate model were considered as factors significantly associated with TPT non-completion.

This study looked back at existing data from TB-MIS to identify factors associated with TPT non-completion. No patients were directly involved in the study. Instead, researchers at the national tuberculosis program analyzed anonymized data extracted from TB-MIS as part of their routine surveillance and data analysis activities. This research followed the ethical guidelines of the Helsinki Declaration. Since the data was already anonymized, informed consent from individual patients and ethical clearance were not required from the National Ethics Committee for Health Research (NECHR).

In total, 14,262 individuals were included in the analysis, of whom 299 (2.1%) did not complete TPT. As shown in Table 1 , 40% of participants was those aged less than 15 years old, and more than half (56.7%) were female. Almost all study population was Cambodian. Among the three TPT regimens, almost half of LTBI cases (47.4%) were initiated with 3RH, followed by 6H (33.5%). The majority of TPT initiation was made at health centers (95.4%).

Factors potentially associated with TPT non-completion were shown in Table 2 . In bivariate logistic regression, those aged 25–34 years old, initiated with 3RH and 6H, and initiated TPT at referral hospitals were the factors potentially associated with TPT non-completion.

Multivariate logistic regression is shown in Table 3 to identify factors independently associated with TPT non-completion. The following potential confounders were included in the model: age, sex, treatment regimens, and place of TPT initiation. After adjusting with other potential confounders, the likelihood of TPT non-completion among aged 15–24 and 25–34 years old were higher compared to the reference group (aged < 5 years old), with an adjusted odds ratios (aOR) 1.70 (95% CI 1.04–2.78), p = 0.034 and 2.09 (95% CI 1.29—3.40), p = 0.003, respectively.

TPT regimens were also found to be factors associated with TPT non-completion. Compared to 3HP, the likelihood of TPT non-completions were higher among those initiated with 3RH and 6H, with aOR 2.61 (95% CI 1.53—4.46), p < 0.001 and 7.01 (95% CI 4.19—11.72), p < 0.001, respectively.

Places of TPT initiation also affected TPT completion. LTBI individuals who initiated the treatment at referral hospitals were almost twice more likely to not complete the treatment compared to those initiated TPT at health centers, with aOR 1.95, 95% CI 1.25–3.03, p = 0.003.

Based on our best knowledge, this is the first study to explore factors associated with TPT non-completion in Cambodia using programmatic data from the national TB program. The present study found that TPT non-completion was only 2.1%. This proportion is lower than TPT non-completion rates in other studies 5 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 .

After adjustment for other confounding variables, individuals aged of 15–34 years were found to be factor associated with TPT non-completion. This finding is consistent with a study in Portugal 6 . A Systematic review and meta-analysis of TPT adverse events found that incidence of all types of adverse events leading to TPT discontinuation was higher in adults than in children, with proportion of 3.7% and 0.4% respectively. The same review also found that TPT non-completion due to TPT hepatotoxicity was 1.1% in adults, compared to only 0.02% in children 24 . This failure to complete treatment could also be due to competing obligations from school and family life. Conversely, the stronger focus on TPT completion in younger age groups, particularly those under 5, might be due to increased support from families and healthcare providers. This could be because families are more involved in their children’s treatment, and healthcare professionals might perceive TPT as more critical for younger people with LTBI. This focus may stem from Cambodia’s initial implementation of TPT only for children under 5 25 .

While other studies found women had higher risk of TPT non-completion and vice-versa 26 , 27 , our study did not find any sex difference on TPT non-completion. The national TB program should continue this good practice to ensure gender balance in TPT implementation.

Regarding treatment regimens, TPT initiation with 3RH and 6H was found to be associated with increase treatment non-completion, compared to 3HP. This finding is in line with other studies 6 , 28 . A study in the United State also found that initiation with longer TPT regimen (9 months of Isoniazid) had lower treatment completion rate than shorter regimens (3HP or four months of Rifampin) 29 . In Cambodia, three TPT regimens were adopted. 3RH and 6H are the daily treatment regimens for durations of 3 and 6 months respectively whereas 3HP is a 3-month weekly treatment regimen. Compared to 3RH and 6H, 3HP has a lowest pill burden which could be a factor influencing a completed TPT course. This study did not capture adverse events of the three TPT regimens which could potentially affect treatment non-completion. Several studies in other countries have been conducted to investigate adverse events of different TPT regimens. A meta-analysis by Winters N et al. and a systematic review and meta-analysis by Luca Melnychuk et al. found that TPT discontinuation due to treatment-related adverse events was higher among those initiated with 3HP than with 4 months of daily Rifampicin (4R) 24 , 28 .

Our research identified a significant association between health facility levels where TPT was initiated and treatment completion rates. People who began TPT at referral hospitals were nearly twice as likely to not complete the treatment compared to those who started at health centers. This finding aligns with observations from other studies exploring healthcare access and completion to treatment regimens. For example, a Ugandan study investigating TPT completion among HIV-positive individuals noted similar trends 30 . Those initiating treatment at urban facilities demonstrated lower completion rates compared to those starting in rural health centers 30 . This could be attributed to several factors, including proximity to healthcare facilities as health centers typically represent the most accessible level within the Cambodian healthcare system. This geographical convenience allows for easier follow-up appointments, medication refills, and potentially reduces transportation burdens, all of which contribute to improved adherence 31 . Furthermore, referral hospitals, while equipped to manage intricate medical cases, might lack of dedicated personnel or targeted programs specifically tailored for TPT management compared to health centers where often deliver more focused healthcare programs, including TPT management. Additionally, social support networks might be stronger in local communities surrounding health centers, fostering better treatment adherence 31 . Therefore, the national TB program should decentralize TPT initiation, which is in line with WHO recommendations to decentralize TB preventive service 32 .

While secondary data analysis is convenient and cost-effective, it has some drawbacks. The data may not perfectly address this specific research question. It was collected as part of routine TB program. So, a comprehensive analysis of factors associated with TPT non-completion may be missing. Even though, potential factors such as age, sex, TPT regimens were included in the analysis. Data quality is also a concern as we have limited control over the data’s quality during recording and entering into the system. Despite this, different data quality control mechanisms were applied. Healthcare providers who were responsible for completing data collection forms were trained on LTBI, TPT, data completion, and data entry into TB-MIS. Importantly, TB-MIS was designed to control quality of entered data through several key features. These features include the provision of clear instructions, a logical data entry flow, and drop-down menus to avoid typing errors. In addition, data validation rules were set up in the system to restrict invalid or outliers such as age, weight and so on by limiting the entries to a specific range within a certain minimum and maximum values. Logic to skip was also designed to skip irrelevant questions based on previous answers. To ensure data completeness, the system was built with programs to alert before exiting each question to ensure that each data element was completed. Importantly, a multi-tiered supervisory framework was implemented. Supervisors at national, provincial, and district levels provided routine support to health facilities where data were recorded and entered into the system. This support included verifying the use of appropriate data collection forms, ensuring data completeness, and identifying and correcting errors before data entry. Finally, retrieved data was cleaned and managed before analysis.

This study offers several key strengths, including the use of nationally representative data, although TPT has not been implemented consistently across health facilities within the country. It allows us to draw conclusions about factors associated with TPT non-completion that can be applied to the whole country. This is particularly valuable for informing national public health policies and interventions. Even while generalizability to the whole nation, nationally representative data allows us to examine TPT non-completion within subgroups such as age, treatment regimens, and place of TPT initiation. Overall, using nationally representative data provides a strong foundation for developing effective public health strategies to improve TPT completion rates and tuberculosis control efforts.

Conclusions

In conclusion, this study is the first to utilize nationally representative data to identify factors associated with discontinuation of TPT. Individuals aged 15–34 years, those treated with 3RH and 6H regimens, and those who initiated treatment at referral hospitals were more likely to not complete TPT. To improve TPT completion rates, priorities should include strengthening treatment follow-up for young adults aged 15–34 years and tailoring treatment to individual needs. Additionally, the national TB program should consider making the 3HP regimen the first-line treatment option. Furthermore, strengthening treatment follow-up at referral hospitals and exploring the decentralization of TPT initiation to health centers or even the community level should be considered.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Y. A. performed data acquisition and was responsible for study conception, data analysis and interpretation, manuscript content and writing. K.E.K was responsible for study conception, data interpretation, manuscript content and writing. All authors reviewed and have approved the final version of the manuscript.

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An, Y., Khun, K.E. Factors associated with incomplete tuberculosis preventive treatment: a retrospective analysis of six-years programmatic data in Cambodia. Sci Rep 14 , 18458 (2024). https://doi.org/10.1038/s41598-024-67845-6

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DOI : https://doi.org/10.1038/s41598-024-67845-6

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Research on quantitative analysis methods for the spatial characteristics of traditional villages based on three-dimensional point cloud data: a case study of liukeng village, jiangxi, china.

1. Introduction

2. literature review, 2.1. research on spatial characteristics of traditional villages, 2.2. new developments in the study of spatial characteristics in traditional villages brought by 3d point cloud data, 3. materials and methods, 3.1. study area, 3.2. methods, 3.2.1. step i: field data collection: obtaining 3d point cloud data through low-altitude uav aerial photography and handheld laser scanners, 3.2.2. step ii: data processing: point cloud preprocessing and object classification, 3.2.3. step iii: data analysis and application: extraction of quantitative indicators of village spatial characteristics and interpretation of construction wisdom, 4.1. topographic environment: visualization and interpretation of feng shui concepts and site selection wisdom, 4.1.1. interpretation of the connotation and wisdom in feng shui forest, 4.1.2. micro-topography-based quantification of water management, 4.2. street spaces: spatial scale and hierarchical division, 4.2.1. quantification of street space scale, 4.2.2. hierarchy of street spaces, 4.3. building elements: characteristics and morphology of residential courtyards, 4.3.1. quantitative analysis of residential courtyard scale, 4.3.2. summary of the form of residential courtyards, 5. discussion, 5.1. strategy for utilizing 3d point cloud data, 5.2. innovations, 5.3. limitations, 5.4. future application scenarios, 5.4.1. village spatial basic information data layer, 5.4.2. village historical and cultural knowledge application layer, 5.4.3. village public display and tour service layer, 6. conclusions, author contributions, data availability statement, conflicts of interest.

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Click here to enlarge figure

Hierarchy	Spatial Characteristics	Quantitative Indicators	Analysis Software/Explanation
Topographic Environment	Interpretation of Feng Shui Forest	Topographic elevation, Feng Shui forest protection range	Arcgis 10.8
Topographic Environment	Village Water Management	Water source, water volume, irrigation paths	Grasshopper 7.0 Rainwater runoff
Street Spaces	Street Scale	Street width	Direct calculation from point cloud
Street Spaces	Street Hierarchy	Distribution of streets Placement of House Doors in Streets	Combination of point cloud model statistics and field surveys
Individual Buildings	Courtyard Space Area	Building footprint area	Direct calculation from point cloud
	Saturation	Ratio of courtyard area to the area of the circumscribed rectangle	Higher value indicates more saturated courtyard space
	Boundary Coefficient	Ratio of courtyard perimeter to the perimeter of the circumscribed rectangle	The value closer to 1 indicates a more regular courtyard shape
	Courtyard Form	Combination and expansion relationships of courtyards	Point cloud model statistics

Hierarchy		Basic Features	Specific Content	Computational/Analytical Methods
Terrain environment	Ground	Slope, direction, elevation	Mountain and ground slope, slope direction, elevation	In combination with gis
	Forest	Shape	Shape index of tree crown projection in forest land	In combination with gis
		Area	Area of tree crown projection in forest land	Direct calculation from point cloud
		Height	Average height and height variation in forest land	Direct calculation from point cloud
	Water	Length, width	Length and width of the minimum bounding rectangle of pond contours	Direct calculation from point cloud
		Perimeter, area	Perimeter and area of pond contours	Direct calculation from point cloud
		Volume	Water storage capacity of ponds	Physical simulation based on 3D data
Street hierarchy	Space	Width	Width of streets, i.e., distance between outer walls of buildings on both sides	Direct calculation from point cloud
		Height	Height of streets, i.e., height of buildings along the street and variations along the road	Direct calculation from point cloud
		Height-to-width ratio	Height-to-width ratio of streets and variations along the road	Direct calculation from point cloud
		Visibility analysis	Visibility analysis of landmark features	Physical simulation based on 3D data
	Interface	Slope, direction	Slope and direction of street surfaces	In combination with gis
		Sinuosity	Ratio of actual length of street centerline to endpoint length	Direct calculation from point cloud
		Distribution density	Public space ratio: ratio of public space area to total base area	Direct calculation from point cloud
Building hierarchy	Space	Building height	Height from ground to flat/sloping roofs, ridge height and eave height of sloping roofs (individual/average)	Direct calculation from point cloud
	Space	Building height	Overall height distribution and height differences of building clusters	Direct calculation from point cloud
	Roof	Number of buildings	Number of buildings divided by roof type	Direct calculation from point cloud
		Slope, direction	Slope and direction of roofs (individual/average)	Direct calculation from point cloud
		Shape	Length-to-width ratio of roofs (individual/average)	Direct calculation from point cloud
		Length, width	Length and width of roofs (individual/average)	Direct calculation from point cloud
	Ground	Shape	Length-to-width ratio of building footprints (individual/average)	Direct calculation from point cloud
	Ground	Area	Building footprint area = roof projection area—eave area (individual/average)	Direct calculation from point cloud

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Share and Cite

Li, Z.; Wang, T.; Sun, S. Research on Quantitative Analysis Methods for the Spatial Characteristics of Traditional Villages Based on Three-Dimensional Point Cloud Data: A Case Study of Liukeng Village, Jiangxi, China. Land 2024 , 13 , 1261. https://doi.org/10.3390/land13081261

Li Z, Wang T, Sun S. Research on Quantitative Analysis Methods for the Spatial Characteristics of Traditional Villages Based on Three-Dimensional Point Cloud Data: A Case Study of Liukeng Village, Jiangxi, China. Land . 2024; 13(8):1261. https://doi.org/10.3390/land13081261

Li, Zhe, Tianlian Wang, and Su Sun. 2024. "Research on Quantitative Analysis Methods for the Spatial Characteristics of Traditional Villages Based on Three-Dimensional Point Cloud Data: A Case Study of Liukeng Village, Jiangxi, China" Land 13, no. 8: 1261. https://doi.org/10.3390/land13081261

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Published: 07 August 2024

Impact of neonatal sepsis on neurocognitive outcomes: a systematic review and meta-analysis

Wei Jie Ong ORCID: orcid.org/0000-0001-8244-2977 1 na1 ,
Jun Jie Benjamin Seng ORCID: orcid.org/0000-0002-3039-3816 1 , 2 , 3 na1 ,
Beijun Yap 1 ,
George He 4 ,
Nooriyah Aliasgar Moochhala 4 ,
Chen Lin Ng 1 ,
Rehena Ganguly ORCID: orcid.org/0000-0001-9347-5571 5 ,
Jan Hau Lee ORCID: orcid.org/0000-0002-8430-4217 6 &
Shu-Ling Chong ORCID: orcid.org/0000-0003-4647-0019 7

BMC Pediatrics volume 24 , Article number: 505 ( 2024 ) Cite this article

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Introduction

Sepsis is associated with neurocognitive impairment among preterm neonates but less is known about term neonates with sepsis. This systematic review and meta-analysis aims to provide an update of neurocognitive outcomes including cognitive delay, visual impairment, auditory impairment, and cerebral palsy, among neonates with sepsis.

We performed a systematic review of PubMed, Embase, CENTRAL and Web of Science for eligible studies published between January 2011 and March 2023. We included case–control, cohort studies and cross-sectional studies. Case reports and articles not in English language were excluded. Using the adjusted estimates, we performed random effects model meta-analysis to evaluate the risk of developing neurocognitive impairment among neonates with sepsis.

Of 7,909 studies, 24 studies ( n = 121,645) were included. Majority of studies were conducted in the United States ( n = 7, 29.2%), and all studies were performed among neonates. 17 (70.8%) studies provided follow-up till 30 months. Sepsis was associated with increased risk of cognitive delay [adjusted odds ratio, aOR 1.14 (95% CI: 1.01—1.28)], visual impairment [aOR 2.57 (95%CI: 1.14- 5.82)], hearing impairment [aOR 1.70 (95% CI: 1.02–2.81)] and cerebral palsy [aOR 2.48 (95% CI: 1.03–5.99)].

Neonates surviving sepsis are at a higher risk of poorer neurodevelopment. Current evidence is limited by significant heterogeneity across studies, lack of data related to long-term neurodevelopmental outcomes and term infants.

Peer Review reports

Sepsis is a major cause of mortality and morbidity among neonates [ 1 , 2 , 3 , 4 ]. Young infants especially neonates, defined by age < 28 days old, have a relatively immature immune system and are susceptible to sepsis [ 5 , 6 ]. Annually, there are an estimated 1.3 to 3.9 million cases of infantile sepsis worldwide and up to 700,000 deaths [ 7 ]. Low-income and middle-income countries bear a disproportionate burden of neonatal sepsis cases and deaths [ 7 , 8 ]. While advances in medical care over the past decade have reduced mortality, neonates who survive sepsis are at risk of developing neurocognitive complications, which affect the quality of life for these children and their caregivers [ 9 ].

Previous reviews evaluating neurocognitive outcomes in neonates with infections or sepsis have focused on specific types of pathogens (e.g., Group B streptococcus or nosocomial infections [ 10 ]), or are limited to specific populations such as very low birth weight or very preterm neonates [ 11 ], and there remains paucity of data regarding neurocognitive outcomes among term and post-term neonates. There remains a gap for an updated comprehensive review which is not limited by type of pathogen or gestation. In this systematic review, we aim to provide a comprehensive update to the current literature on the association between sepsis and the following adverse neurocognitive outcomes (1) mental and psychomotor delay (cognitive delay (CD)), (2) visual impairment, (3) auditory impairment and (4) cerebral palsy (CP) among neonates [ 11 ].

We performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [ 12 ]. This study protocol was registered with Open Science Framework ( https://doi.org/10.17605/OSF.IO/B54SE ).

Eligibility criteria

We identified studies which evaluated neurocognitive outcomes in neonates less than 90 days old (regardless of gestational age) with sepsis. While the neonatal period is traditionally defined to be either the first 28 days postnatally for term and post-term infants, or 27 days after the expected date of delivery for preterm infants [ 13 ], serious late onset infections in the young infant population can present beyond the neonatal period [ 14 ], hence we defined the upper age limit as 90 days old to obtain a more complete picture of the burden of young infantile sepsis [ 15 ]. Post-term neonates was defined as a neonate delivered at > = 42 weeks of gestational age in this study [ 16 ]. We included studies that either follow international sepsis definitions such as Surviving Sepsis Campaign guidelines definitions [ 17 ], or if they fulfilled clinical, microbiological and/or biochemical criteria for sepsis as defined by study authors. The primary outcome of interest was impaired neurocognitive outcome defined by the following domains of neurodevelopmental impairment (NDI) [ 11 ]: (1) CD, (2) visual impairment, (3) auditory impairment and (4) CP. We selected these domains because they were highlighted as key neurocognitive sequelae after intrauterine insults in a landmark review by Mwaniki et al. [ 18 ]. The authors’ definitions of these outcomes and their assessment tools were captured, including the use of common validated instruments (e.g., a common scale used for CD is the Bayley Scales of Infant Development (BSID) [ 19 ] while a common instrument used for CP was the Gross Motor Function Classification System (GMFCS) [ 20 ]. Specifically for BSID, its two summative indices score – Mental Development Index (MDI) and Psychomotor Development Index (PDI) were collected. The MDI assesses both the non-verbal cognitive and language skills, while PDI assess the combination of fine and gross motor skills. The cut-off points for mild, moderate and severe delay for MDI and PDI were < 85 or < 80, < 70 and < 55 respectively [ 21 ]. There were no restrictions on duration of follow-up or time of assessment of neurocognitive outcomes to allow capturing of both short- and long-term neurocognitive outcomes.

Case–control, cohort studies and cross-sectional studies published between January 2011 and March 2023 were included. Because the definition and management of sepsis has evolved over the years [ 22 ], we chose to include studies published from 2011 onwards. Case reports, animal studies, laboratory studies and publications that were not in English language were excluded. Hand-searching of previous systematic reviews were performed to ensure all relevant articles were included. To avoid small study effects, we also excluded studies with a sample size of less than 50 [ 23 ].

Information sources and search strategy

Four databases (PubMed, Cochrane Central, Embase and Web of Science) were used to identify eligible studies. The search strategy was developed in consultation with a research librarian. The first search was conducted on 4 December 2021 and an updated search was conducted on 3 April 2023. The detailed search strategy can be found in Supplementary Tables 1A and B.

Study selection process

Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) [ 24 ] was utilized during this review. Five reviewers (WJO, BJY, NM, CLN and GH) independently conducted the database search and screened the title and abstracts for relevance. Following training on inclusion and exclusion eligibility, 4 reviewers (WJO, NM, CLN and GH) subsequently assessed the full text of shortlisted articles for eligibility. All full texts were independently assessed by at least 2 reviewers. Any conflict related to study eligibility were resolved in discussion with the senior author (S-LC). We recorded the reason(s) for exclusion of each non-eligible article.

Data collection process and data items

Four reviewers (WJO, NM, CLN and GH) independently carried out the data extraction using a standardized data collection form, and any conflict was resolved by discussion, or with input from the senior author (S-LC). A pilot search was performed for the first 200 citations to evaluate concordance among reviewers and showed good concordance among reviewers of 94%. For studies with missing data required for data collection or meta-analyses, we contacted the corresponding authors of articles to seek related information. If there was no reply from the authors, the data were labelled as missing.

Study risk of bias assessment

Three reviewers (BJY, GH and WJO) independently carried out the assessment of risk of bias using the Newcastle–Ottawa Scale (NOS) for all observational studies [ 25 ]. Studies were graded based on three domains namely, selection, comparability and outcomes. Studies were assigned as low, moderate and high risk of bias if they were rated 0–2 points, 3–5 points and 6–9 points respectively. Any conflict was resolved by discussion or with input from the senior author (S-LC).

Statistical analysis

All outcomes (i.e. CD, visual impairment, auditory impairment and CP) were analysed as categorical data. Analyses were done for each NDI domain separately. To ensure comparability across scales, results from different studies were only pooled if the same measurement tools were used to assess the outcomes and hence sub-group analyses were based on different scales and/or different definitions of neurocognitive outcomes used by authors. Both unadjusted and adjusted odds ratios (aOR) and/or relative risk (RR) for each NDI domain were recorded. Where source data were present, we calculated the unadjusted OR if the authors did not report one, together with the 95% confidence interval (CI). For adjusted odds ratio, these were extracted from individual studies and variables used for adjustment were determined at the individual study level.

Meta-analysis was conducted for all outcomes that were reported by at least 2 independent studies or cohorts. Studies were included in the meta-analysis only if they reported outcomes for individual NDI domains within 30 months from sepsis occurrence. For each domain, all selected studies were pooled using DerSimonian-Laird random effects model due to expected heterogeneity. Studies were pooled based on adjusted and unadjusted analyses. Case–control and cohort studies were pooled separately. The pooled results were expressed as unadjusted odds ratio (OR) or adjusted odds ratio (aOR) with corresponding 95% confidence interval (95% CI). If there was more than 1 study that utilized the same population, we only analysed data from the most recent publication or from the larger sample size, to avoid double counting. Standard error (SE) from studies with multiple arms with same control group were adjusted using SE = √(K/2), where K refers to number of treatment arms including control [ 26 ]. Heterogeneity across studies was evaluated using the I^2 statistic, for which ≥ 50% is indicative of significant heterogeneity. With regards to publication bias, this was performed using Egger’s test and funnel plots only if the number of studies pooled were 10 or more for each outcome.

For neurocognitive related outcomes, subgroup analyses were performed based on the severity of the NDI domain outcomes and distinct, non-overlapping populations of septic infants (such as late onset vs early onset sepsis, culture positive sepsis vs clinically diagnosed sepsis, term and post term patients).

All analyses were done using ‘meta’ library from R software (version 4.2.2) [ 27 ]. The statistical significance threshold was a two tailed P- value < 0.05.

Certainty of evidence

The certainty of evidence for outcomes in this review was performed during the GRADE criteria [ 28 ] which is centred on the study design, risk of bias, inconsistency, indirectness, imprecision, and other considerations.

Study selection

From 7,909 studies identified, a total of 24 articles were included (Fig. 1 ) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. A total of 101,657 and 19,988 preterm and term infants were included in this review.

PRISMA flowchart of the study selection process for search

Study characteristics

There were 2 case–control studies and 22 cohort studies, with a total of 121,645 infants (Table 1 ). Studies were conducted in 16 different countries (Fig. 2 ), with the most studies conducted in the United States of America (USA) (7 studies, n = 92,358 patients) [ 30 , 33 , 37 , 41 , 42 , 47 , 52 ]. There were no studies that were conducted solely on term infants. 5 studies reported data specifically on ELBW infants (27,078 infants) and 6 studies on VLBW infants (3,322 infants). All studies were performed among neonates.

World map depicting distribution of studies that evaluate neurocognitive outcomes in infantile and neonatal sepsis

Risk of bias

Overall, all 24 studies were classified as low risk (Supplementary Table 2). 5 papers scored high risk for outcome bias for having greater than 10% of initial population being lost to follow-up [ 29 , 32 , 40 , 41 , 42 ].

Outcome measures reported by domain

As the number of studies pooled for each outcome was less than 10, publication bias was not analysed in the meta-analyses.

Cognitive delay (CD)

Among 24 studies that assessed for CD, 16 studies reported either the incidence of CD among young infants with sepsis compared to those without, and/or the odds ratio (adjusted and/or unadjusted) comparing the two populations [ 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 45 , 46 , 48 , 49 ]. The scales used, authors’ definition of CD, incidence of CD among those with sepsis and those without are described in Table 2 . The most common tools used for assessment of CD were the Bayley Scales of Infant Development (BSID) ( n = 13) and Denver Development Screening Test II ( n = 2).

Infantile sepsis was associated with increased risk of overall CD delays [aOR 1.14 (95%CI: 1.01, 1.28)], overall PDI delay (aOR 1.73 (95%CI: 1.16, 2.58)) and moderate PDI delay [aOR 1.85 (95%CI: 1.01, 3.36)]. Conversely, infantile sepsis was not associated with increased risk for severe PDI delay nor overall MDI delay [aOR 1.30 (95%CI: 0.99, 1.71)] or its subgroups. There were no significant differences in outcomes between different subgroups of infections as well as culture-proven or clinically defined sepsis for either MDI or PDI (Table 8 , Fig. 3 A and B).

A Forest plot on adjusted odds ratios for neurocognitive outcomes related to MDI, PDI, visual impairment, hearing impairment and cerebral palsy. B Forest plot on unadjusted odds ratios for neurocognitive outcomes related to MDI, PDI, visual impairment, hearing impairment and cerebral palsy. Legend: MDI: Mental Developmental Index; PDI: Psychomotor Developmental Index. Foot note: Mild MDI or PDI: < 85 or < 80; Moderate MDI or PDI < 70; Severe MDI or PDI < 55

Visual impairment

Seven studies reported data on visual impairment (Table 3 ) [ 31 , 33 , 41 , 42 , 47 , 49 ]. The most common definition of visual impairment utilized was “visual acuity of < 20/200” ( n = 4, 66.7%).

In the meta-analysis, infantile sepsis was associated with significantly increased risk of visual impairment [aOR 2.57 (95%CI: 1.14, 5.82)] but there were no statistically significant differences in visual impairment between subgroups of early or late onset sepsis, and blood culture negative conditions as compared to the non-septic population (Table 8 , Fig. 3 A and B).

Hearing impairment

Seven studies reported data on hearing impairment (Table 4 ) [ 31 , 33 , 41 , 42 , 47 , 49 ]. Two studies defined hearing impairment as permanent hearing loss affecting communication with or without amplification [ 42 , 47 ]. Other definitions included “sensorineural hearing loss requiring amplification” ( n = 1), “bilateral hearing impairment with no functional hearing (with or without amplification)” ( n = 1), “clinical hearing loss” ( n = 1).

In the meta-analysis, sepsis was associated with increased risk of hearing impairment [aOR 1.70 (95% CI: 1.02–2.81)]. However, in the subgroup analyses, there were no differences in risk of hearing impairment between patients with late onset sepsis as compared to the non-septic population (Table 8 , Fig. 3 A and B).

Cerebral palsy

Nine studies [ 29 , 32 , 33 , 41 , 42 , 47 , 48 , 49 , 50 ] reported data on CP (Table 5 ), of which 5 studies [ 41 , 42 , 45 , 49 , 50 ] used the GMFCS scale. In the meta-analysis, infantile sepsis was associated with significantly increased risk of CP [aOR 2.48 (95%CI: 1.03; 5.99)]. There was no difference in rates of CP among patients with proven or suspected sepsis, as compared with infants with no sepsis (Table 8 , Fig. 3 A and B).

Differences in neurocognitive outcomes between neonates with culture-proven or clinically diagnosed sepsis as well as early or late onset sepsis

Tables 6 and 7 showed data related to differences in neurocognitive outcomes between neonates with culture-proven or clinically diagnosed sepsis as well as early or late onset sepsis. Meta-analyses were not be performed due to significant heterogeneity in definitions of sepsis, time of assessment of outcomes.

Differences in neurocognitive outcomes between term and post-term neonates

There were no studies which evaluated neurocognitive outcomes between term and post-term neonates and infants.

We found that the certainty of evidence to be very low to low for the four main neurocognitive outcomes selected. (Supplementary File 3).

In this review involving more than 121,000 infants, we provide an update to the literature regarding young infant sepsis and neurocognitive impairment. Current collective evidence demonstrate that young infant sepsis was associated with increased risk of developing neurocognitive impairment in all domains of CD, visual impairment, auditory impairment and cerebral palsy.

Cognitive delay

In this review, higher rates of cognitive delay were noted among infants with sepsis [ 29 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 45 , 46 , 48 , 49 , 52 ]. We found that infants with sepsis reported lower PDI scores (Table 8 ), which measures mainly neuromotor development. On the other hand, young infant sepsis was not associated with lower MDI scores (Table 8 ), which assesses cognitive and language development. The pathophysiological mechanism of young infant sepsis and its preferential impact on PDI remains unclear. Postulated mechanisms include development of white matter lesions which may arise from the susceptibility of oligodendrocyte precursors to inflammatory processes such as hypoxia and ischemia [ 53 ]. Future studies should look into evaluating the causes of the above findings. A majority of included studies focused on early CD outcomes while no studies evaluated long-term outcomes into adulthood. CD is known to involve complex genetic and experiential interactions [ 54 ] and may evolve overtime with brain maturation. Delays in speech and language, intellectual delay and borderline intellectual functioning are shown to be associated with poorer academic or employment outcomes in adulthood [ 55 , 56 ], and early assessment of CD may not fully reveal the extent of delays. The only study with follow-up to the adolescent phase showed a progressive increase in NDI rate as the participants aged, which provides evidence of incremental long-term negative outcomes associated with infantile sepsis [ 44 ]. Moving forward, studies with longer follow-up may allow for further examination of the long-term effects of neonatal sepsis on CD.

There were different versions of the BSID instrument (BSID-II and BSID-III) [ 19 , 57 , 58 ]. BSID-II lacked subscales in PDI and MDI scores, leading to the development of BSID-III with the segregation of PDI into fine and gross motor scales and MDI into cognitive, receptive language, and expressive language scales [ 59 ]. Although we pooled results of both BSID-II and BSID-III in our study, we recognize that comparisons between BSID-II and BSID-III are technically challenging due to differences in standardised scores [ 59 , 60 ]. In addition, the BSID-IV was created in 2019 which has fewer items, However, none of our studies utilized this instrument. Future studies should consider this instrument, as well as standardising the timepoints for assessment of CD.

Young infant sepsis was associated with increased risk of developing visual impairment. This was similar to results noted by a previous systematic review published in 2014 [ 61 ] and 2019 [ 62 ] which showed that neonatal sepsis was associated with twofold risk of developing retinopathy of prematurity in preterm infants. Specifically, meningitis was associated with a greater risk of visual impairment compared to just sepsis alone [ 47 ]. The mechanism of visual impairment has not been fully described although various theories have been suggested, including sepsis mediated vascular endothelial damage, increased body oxidative stress response as well as involvement of inflammatory cytokines and mediators [ 63 , 64 ].

Our meta-analysis showed an increased risk of hearing impairment for young infants with young infants with sepsis. This is consistent with a previous report that found an association between neonatal meningitis and sensorineural hearing loss [ 65 ]. One potential confounder which we were unable to account for may have been the use of ototoxic antimicrobial agents such as aminoglycosides. Additional confounders include very low birth weight, patient’s clinical states (e.g. hyperbilirubinemia requiring exchange transfusion) and use of mechanical ventilation or extracorporeal membrane support. To allow for meaningful comparisons of results across different study populations, it is imperative that a standardised definition of hearing impairment post neonatal sepsis be established for future studies.

Our meta-analysis found an association between neonatal sepsis and an increased risk of developing CP. This is also consistent with previous systematic reviews which had found a significant association of sepsis and CP in VLBW and early preterm infants [ 11 ]. One study found that infants born at full term and who experienced neonatal infections were at a higher risk of developing a spastic triplegia or quadriplegia phenotype of CP [ 66 ]. The pathophysiology and mechanism of injury to white matter resulting in increased motor dysfunction remains unclear and more research is required in this area.

Limitations and recommendations for future research

The main limitation of this review lies in the heterogeneity in the definitions of sepsis, exposures and assessment of outcomes across studies. This is likely attributed to the varying definition of sepsis used in different countries as well as lack of gold standard definitions or instruments for assessment of each component of NDI. A recent review of RCTs [ 67 ] also reported similar limitations where 128 different varying definitions of neonatal sepsis were used in literature. Notably, there is a critical need for developing international standardized guidelines for defining neonatal sepsis as well as assessment of NDI such as hearing and visual impairment. Another important limitation relates to the inability to assess quality of neonatal care delivered as well as temporal changes in medical practices which could have affected neurocognitive outcomes for neonates with sepsis. Improving quality of neonatal care has been shown to significantly reduce mortality risk among neonates with sepsis, especially in resource-poor countries [ 68 ]. We performed a comprehensive search strategy (PubMed, Embase, Web of Science and CENTRAL) coupled with hand searching of references within included systematic reviews, but did not evaluate grey literature. Future studies should include additional literature databases and grey literature. Another area of research gap lies in the paucity of data related to differences in neurocognitive outcomes between term and post-term neonates with sepsis and future research is required to bridge this area of research gap. Likewise, there are few studies which evaluated differences in neurocognitive outcomes between early or late onset sepsis and outcomes assessed were significantly heterogenous which limits meaningful meta-analyses. Similarly, there was significant heterogeneity in study outcomes, causative organisms and severity of disease.

We found a lack of long-term outcomes and recommend that future prospective cohorts include a longer follow-up duration as part of the study design. This is important given the implication of NDI on development into adulthood. Most data were reported for preterm infants with low birth weight, and there was a paucity of data for term infants in our literature review. Since prematurity itself is a significant cause of NDI [ 69 ], future studies should consider how gestational age and/or birth weight can be adequately adjusted for in the analysis.

Apart from the domains of NDI we chose to focus on in this review, there are other cognitive domains classified by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) [ 70 ] and/or recommended by the Common Data Elements (CDE) workgroup [ 71 ]. Future studies may wish to look into the implications of sepsis on other neuro-cognitive domains related to executive function, complex attention and societal cognition which are studied for other types of acquired brain injury [ 71 , 72 ].

Our systematic review and meta-analysis found that neonates surviving sepsis are at a higher risk of poorer neurodevelopment. However, the evidence is limited by significant heterogeneity and selection bias due to differing definitions used for NDI and for sepsis. There is also a lack of long-term follow-up data, as well as data specific for term and post-term infants. Future prospective studies should be conducted with long-term follow-up to assess the impact of neurodevelopmental impairment among all populations of neonates with sepsis.

Availability of data and materials

All data generated or analyzed in the study are found in the tables and supplementary materials.

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Acknowledgements

We would like to thank Ms. Wong Suei Nee, senior librarian from the National University of Singapore for helping us with the search strategy. We will also like to thank Dr Ming Ying Gan, Dr Shu Ting Tammie Seethor, Dr Jen Heng Pek, Dr Rachel Greenberg, Dr Christoph Hornik and Dr Bobby Tan, for their inputs in the initial design of this study.

Conflict of interest

No financial or non-financial benefits have been received or will be received from any party related directly or indirectly to the subject of this article.

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Wei Jie Ong and Jun Jie Benjamin Seng are co-first authors.

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MOH Holdings, Singapore, 1 Maritime Square, Singapore, 099253, Singapore

Wei Jie Ong, Jun Jie Benjamin Seng, Beijun Yap & Chen Lin Ng

SingHealth Regional Health System PULSES Centre, Singapore Health Services, Outram Rd, Singapore, 169608, Singapore

Jun Jie Benjamin Seng

SingHealth Duke-NUS Family Medicine Academic Clinical Programme, Singapore, Singapore

Yong Loo Lin School of Medicine, 10 Medical Dr, Yong Loo Lin School of Medicine, Singapore, Singapore

George He & Nooriyah Aliasgar Moochhala

Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore

Rehena Ganguly

Children’s Intensive Care Unit, KK Women’s and Children’s Hospital, SingHealth Paediatrics Academic Clinical Programme, 100 Bukit Timah Rd, Singapore, 229899, Singapore

Jan Hau Lee

Department of Emergency Medicine, KK Women’s and Children’s Hospital, SingHealth Paediatrics Academic Clinical Programme, SingHealth Emergency Medicine Academic Clinical Programme, 100 Bukit Timah Rd, Singapore, 229899, Singapore

Shu-Ling Chong

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Contributions

SLC and JHL were the study’s principal investigators and were responsible for the conception and design of the study. WJO, JJBS, BY, GE, NAM and CLN were the co-investigators. WJO, JJBS, BY, GE, NAM and CLN were responsible for the screening and inclusion of articles and data extraction. All authors contributed to the data analyses and interpretation of data. WJO, JJBS, BY, GE, NAM and CLN prepared the initial draft of the manuscript. All authors revised the draft critically for important intellectual content and agreed to the final submission. All authors had access to all study data, revised the draft critically for important intellectual content and agreed to the final submission.

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Correspondence to Jun Jie Benjamin Seng .

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Ong, W.J., Seng, J.J.B., Yap, B. et al. Impact of neonatal sepsis on neurocognitive outcomes: a systematic review and meta-analysis. BMC Pediatr 24 , 505 (2024). https://doi.org/10.1186/s12887-024-04977-8

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Traditional villages are important carriers of cultural heritage, and the quantitative study of their spatial characteristics is an important approach to their preservation. However, the rapid extraction, statistics, and estimation of the rich spatial characteristic indicators in these villages have become bottlenecks in traditional village research. This paper employs UAV (unmanned aerial ...
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Sepsis is associated with neurocognitive impairment among preterm neonates but less is known about term neonates with sepsis. This systematic review and meta-analysis aims to provide an update of neurocognitive outcomes including cognitive delay, visual impairment, auditory impairment, and cerebral palsy, among neonates with sepsis. We performed a systematic review of PubMed, Embase, CENTRAL ...

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Qualitative case study data analysis: an example from practice

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

The 5 steps to doing qualitative data analysis

What is Qualitative Data Analysis?

How is qualitative data analysis different from quantitative data analysis?

Qualitative Data Analysis methods

Content Analysis

Narrative Analysis

Discourse Analysis

Thematic Analysis

Grounded Theory

Challenges of Qualitative Data Analysis

Benefits of qualitative data analysis

How to do Qualitative Data Analysis: 5 steps

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

Classic methods of gathering qualitative data

Using your existing qualitative feedback

Utilize untapped qualitative data channels

Step 2: Connect & organize all your qualitative data

The manual approach to organizing your data

Computer-assisted qualitative data analysis software (CAQDAS)

Organizing your qualitative data in a feedback repository

Organizing your qualitative data in a feedback analytics platform

Step 3: Coding your qualitative data

How to manually code your qualitative data

Using software to speed up manual coding of qualitative data

Automating the qualitative coding process using thematic analysis software

The key benefits of using an automated coding solution

Step 4: Analyze your data: Find meaningful insights

Manually create sub-codes to improve the quality of insights

Correlate the frequency of codes to customer segments

Manually visualizing coded qualitative data

Trends over time

Using a text analytics solution to automate analysis

Step 5: Report on your data: Tell the story

Creating graphs and reporting in Powerpoint

Using visualization software for reporting

Visualizing your insights inside a feedback analytics platform

Conclusion - Manual or Automated?

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Qualitative Case Study Data Analysis: An Example from Practice.

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Case Study – Methods, Examples and Guide

Types of Case Study

Single-Case Study

Multiple-Case Study

Descriptive Case Study

Instrumental Case Study

Case Study Data Collection Methods

Observations

How to conduct Case Study Research

Examples of Case Study

Application of Case Study

Business and Management

Social Sciences