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PMP exam guide

9 Data Gathering Techniques You Should Know for PMP Exam

November 1, 2018 By Manickavel Arumugam 14 Comments

PMBOK ® Guide has grouped most of the tools and techniques in a logical way, by their purpose. The following are the various groups used in PMBOK Guide , Sixth Edition:

• Data gathering techniques
• Data analysis techniques
• Data representation techniques
• Decision-making techniques
• Communication skills
• Interpersonal and team skills

In addition, they also have several tools and techniques which does not fit into any of the groups mentioned above.

In this article, we are going to focus on the various data gathering techniques used in PMBOK Guide.

Table of Contents

Data Gathering Techniques

Data gathering techniques are used to collect data and information from a variety of sources. We have 9 data gathering techniques used in PMBOK Guide, Sixth Edition. They are:

Benchmarking

Brainstorming.

• Check sheets

Focus groups

Market research, questionnaires and surveys, statistical sampling, matrix of process and data gathering techniques.

The above 9 data gathering techniques find applications in 13 project management processes. The following matrix helps you to picture the processes and the relevant data gathering technique used in those processes.

Let us try to understand each of the data gathering techniques in detail.

Benchmarking is a technique by which an organization compares its actual or planned practices, to those of comparable organizations.

Benchmarking can be used to identify best practices, generate ideas for improvement and provide a basis for measuring performance.

The projects used for benchmarking can be within the organization, external to the organization, can be that of a competitor, can be within the same industry or from other industries.

For example, if India wants to venture into high-speed rail project, they can benchmark their project against the high-speed rail projects in Germany, France, Japan or China. This will help them to easily collect requirements and plan quality. They can set their expectations against an existing, successful system.

Benchmarking finds use in the following processes:

• Collect requirements
• Plan quality management
• Plan stakeholder engagement

Brainstorming is a technique used to identify a list of ideas by holding a group discussion, led by a facilitator.  The group can include team members and subject matter experts. It is a quick way to generate a large quantity of ideas.

It involves two stages:

• Idea generation, and

During the idea generation phase, the focus is on quantity rather than quality. Every idea is recorded by the facilitator; no idea is stupid or useless.

Data representation techniques like affinity diagrams or mind mapping may be used to further understand the ideas generated, which could lead to new ideas.

Brainstorming can be enhanced through Nominal group technique with a voting process.

Brainstorming is used in in the following processes:

• Develop project charter
• Develop project management plan
• Identify risks
• Identify stakeholders

Check sheets (also known as Tally sheets)

Check sheets provide a structured way to collect data about a potential quality problem.

Check sheets may be used to quantify defects by type or by location or by cause.

Check sheets are considered to be one among the 7 basic tools of quality. Though PMBOK Guide uses this tool only in quality management knowledge area, this is a generic tool that can be adapted for a wide variety of purposes.

Used in: Control quality process

Checklists are often used as reminders. It can be a list of items, actions, to-dos or points to be remembered.

It helps to ensure consistency and completeness in carrying out a process.

Checklists are quick and simple to use.

All of us would have used checklists at some point of time in our life: may be for shopping, may be for packing items for a holiday trip, may be for studying important topics in a subject, etc.

Checklists are frequently used in quality control inspections to remind the inspectors of the various requirements that has to be checked. Auditors use checklists to ensure they audit all the relevant clauses in a Quality Management System.

Checklists are employed in the following processes:

• Manage quality
• Control quality

Focus groups bring together pre-qualified stakeholders and subject matter experts to get an idea of how the market will respond to certain features of the product.

In a brainstorming session, you are looking at generating ideas; while in focus groups, you are looking at gathering the expectations and attitudes about a proposed idea, product, service or result.

A skilled moderator guides the session to be more conversational.

Focus groups are used in the following processes:

Interviews are used to elicit information from stakeholders by talking directly to them. Interviewing experienced project participants or subject matter experts can help in identifying requirements, risks, project constraints, acceptance criteria, quality needs and expectations, etc.

Interviews are very useful when you want to collect confidential information. The interviewer should facilitate an interview setting that encourages honest and unbiased feedback.

An interview can either be

• Structured,
• Semi-structured or
• Unstructured.

Structured interviews include a list of predetermined questions, with little or no variation during the interview.

Unstructured interviews, on the other hand, does not follow a set of predetermined questions. Rather the interview starts with an open question, and further questions are posed based on the response to the earlier questions.

Semi-structured interviews contain some key questions while allowing the interviewer to seek more details, if needed.

Interviews are generally conducted one-on-one, but it may also involve multiple interviewers and/ or multiple interviewees.

Interviews can be used as a technique in many processes including:

• Perform qualitative risk analysis
• Perform quantitative risk analysis
• Plan risk responses

Market research is a technique that is employed in Plan procurement management process.

Market research can be used to help determine the market conditions. It helps to identify the products, services and results available in the market place. It includes examination of industry and specific seller capabilities.

Information can be gathered through conferences, online reviews, etc.

Market research can help you identify emerging technologies.

It can help improve the information in the procurement management plan, procurement strategy, procurement statement of work, and the source selection criteria.

Questionnaires and surveys are a cost-effective way of obtaining data from stakeholders, regarding their needs and expectations.

These are designed to quickly collect information from a large set of respondents.

Surveys are useful tools to gather information about stakeholder satisfaction.

Questionnaires and surveys are used in the following project management processes:

Statistical sampling is used when it is impractical or too expensive to inspect each item during quality control.

Sampling can reduce the cost of quality control activities.

It is defined as selecting part of a population for inspection. The samples are chosen and tested/ inspected according to the quality management plan.

Sampling methods include

• Attribute sampling (discrete): the result either conforms or does not conform. For example, a pass/ fail report of a student. You can only have either “Pass” or “Fail” as the result.
• Variable sampling (continuous): the result is rated on a continuous scale that measures the degree of conformity. For example, the marks of a student. One student can have 70 marks, while another one can have 85 marks.

Statistical sampling is a tool/ technique used in Control quality process.

I hope this article helps you in understanding the various data gathering techniques used in PMBOK Guide, Sixth Edition.

Do you find this article useful? Please share your thoughts.

What kind of techniques do you use in your projects for collecting data? Can you share some examples from your projects on the application of these data gathering techniques?

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Vikash says

October 13, 2021 at 2:24 pm

This post is so helpful. Any plans to do the same for other tools on Data analysis/Data representation.

April 3, 2021 at 6:54 am

What about prompt lists?

Manickavel Arumugam says

April 3, 2021 at 7:14 am

Prompt list is not grouped under Data Gathering Techniques by PMBOK Guide, Sixth Edition.

November 8, 2020 at 1:34 am

We articulated and explained.

Namratha says

July 27, 2020 at 8:46 am

Very well explained. Thank you

July 27, 2020 at 11:16 am

Thanks Namratha.

olufemi says

May 2, 2020 at 4:02 am

Well understood, what a simple explanation.

May 2, 2020 at 6:23 pm

Bob Adedayo says

January 11, 2020 at 8:21 pm

Hello Manickavel, do you have a similar blog post for data representation and data analysis?

January 15, 2020 at 1:35 pm

Unfortunately, I don’t have a post covering the other tools & techniques. Will add to my to do list.

Marco Mongalo says

October 8, 2019 at 5:23 pm

Great and simple explanation. Thanks

October 9, 2019 at 5:17 am

Thanks Marco for your feedback..

November 6, 2018 at 5:33 am

Thank you Mr.Manickavel

for the description of the tools it was really helpful and I always read your lessons it quite good and he;p if you can explain also the mathematics equations and questions as a test questions that important

thank you again

November 6, 2018 at 5:47 pm

Thank you Diaa. I am glad that you find the articles useful.

Regarding the numerical questions, you may find this article useful: https://www.pmdrill.com/pmp-formulae/

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Project Management Knowledge

Simply explained by a PMI-certified Project Manager

Data Gathering and Representation Techniques

Data is necessary for project management. It serves as the backbone for all types of decisions to be made by the project manager . It is, therefore, important to collect, organize and present data clearly so that all stakeholders will understand the status of the project. This is the reason why it is so important for good project managers to not only know and understand but also utilize data gathering and representation techniques. These techniques are used to collect, organize and present data and other information involved in the project life cycle .

There are two types of data gathering and representation techniques used in project management and these include (1) interviewing and (2) probability distribution. Interviewing is a technique that draws the historical data to quantify the impact of risks on the objectives of the project. The information that needs to be collected and organized depends on the type of probability distributions used.  It is also important to create the necessary documents such as the risk ranges to provide important insight on the credibility of the analysis of the data.

The second method uses extensive simulation and modeling. It represents uncertain values like duration of scheduled activities as well as the cost of the different components of the project. The probability distribution may include discrete and uniform  depending on the data that is available. Distribution methods are used to depict that shapes compatible with the data developed during a quantitative risk analysis. Aside from the two methods, simulation is also another method used to estimate the risk and probability distribution.

In project management, the data gathering and representation techniques are very important in performing quantitative risk analysis and management plans. It is, therefore, crucial for the project manager to use these techniques to shed light on what the collected data is all about.

This term is defined in the 5th edition of the PMBOK.

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Simplifying Project Management

Data Representation Techniques

Data Representation is one of the tools and techniques of the Plan Resource Management process. A legit question here would be, what is Data Representation of any use as part of this process where we are planning resources?

Charts, be it hierarchical, matrix or text based are used to document and communicate the roles and responsibilities of the team members. Now regardless of the method used, the objective here is to ensure that every work package has a clear owner and everyone in the project team understand their roles and responsibilities.

Here are the most common chart types,

1. Hierarchical Charts

These are your classic chart structures that is used to show relationships in top-down format. Examples include,

Work Breakdown Structure (WBS)

WBS shows how project deliverables are broken down into work packages. Check out more on WBS here

Organization Breakdown Structure (OBS)

OBS lists the project work packages or activities according to the organization’s existing departments or teams. For example, the purchasing department can look into its portion to understand the expected work packages or activities from them as part of the project

Resource Breakdown Structure (RBS)

RBS represents team and physical resources related by category as well as the resource type. Every descending level represents an increasingly detailed description of the resource until the information is good enough to be used in conjunction with WBS. Check out more on RBS here

2. Responsibility Assignment Matrix

A Responsibility Assignment Matrix or RAM shows the connection between work packages or activities and the project team members. One of the biggest advantages of this is the fact that this ensures only one person is accountable for a task avoiding confusion. One example of RAM is a RACI Matrix or Responsible, Accountable, Consult and Inform matrix. This simple chart lists all the activities in the first left column and resources in the first row followed by RACI entries in the matrix

3. Text Oriented Formats

Sometimes detailed descriptions are required and a simple RACI chart won’t do. In that case documents are created clearly outlining descriptive information like responsibilities, competencies, qualifications, position descriptions etc. These documents can be easily reused as templates for future projects as well

Check more articles on  Resource Management

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3 Comments on “Data Representation Techniques”

Every videos I have watched , you always have someother video in reference to. Why don’t u line up ur videos in orderly manner according to the PMP processes. For ex. Quality management knowledge area has 3 processes. Plan QM, Manage Q, and Control Q) so when we play the first video plan QM, you say we already discussed this process in the previous video. Now what process comes before PlanQM. ?? Like wise all other videos u always say u discussed in previous videos, which is very confusing. Pls take ur videos in orderly manner. Thanks.

Have you checked this link yet? – https://www.pmclounge.com/pmp-training/

super clear and easy to understand. Thank you so much, it have me a lot

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Data Representation Techniques to Improve Project Efficiency

Table of contents.

• Gantt Charts 
• Flowcharts 
• Network Diagrams 
• Tables and Spreadsheets 

Data representation is an essential aspect of project management . It involves using various tools and techniques to visualize and organize project data. These tools include Gantt charts, flowcharts, network diagrams, tables, and spreadsheets. Using these tools, project managers can track progress, identify risks, and make informed decisions to ensure project success. Data representation is critical in project management by providing valuable insights and facilitating effective communication among project stakeholders.

Data Representation

The data representation plays a crucial role in project management, as it helps project managers to make informed decisions based on accurate and relevant information. In this article, we will explore the different types of data representation commonly used in project management and how you can utilize them to achieve project success.

Gantt Charts 

Gantt charts are a popular tool in project management to visualize project schedules. They visually represent project tasks, their dependencies, and the timeline for completing each task. Gantt charts are used to identify critical path tasks, which must be completed on time to prevent delays in the project schedule. Using Gantt charts, project managers can easily track progress and adjust the schedule as necessary.

Here’s a detailed step-by-step guide on how to make a Gantt chart:

Step 1: Define Your Project Tasks and Milestones

Before creating a Gantt chart, you need to identify all the tasks required to complete your project and any significant milestones. Tasks should be specific, measurable, achievable, relevant, and time-bound (SMART). Milestones are key events or achievements that mark significant project progress.

Step 2: Determine Task Dependencies

Identify dependencies between tasks, such as sequential or concurrent relationships. Dependencies indicate the order in which tasks must be completed and help determine the critical path—the longest sequence of dependent tasks that determines the project’s minimum duration.

Step 3: Estimate Task Durations

Estimate the duration required to complete each task. Break down larger tasks into smaller sub-tasks if necessary to provide more accurate estimates. Consider factors such as resource availability, complexity, and potential risks when estimating task durations.

Step 4: Choose a Gantt Chart Tool

Select a software tool or platform to create your Gantt chart. There are numerous options available, ranging from spreadsheet programs like Microsoft Excel and Google Sheets to specialized project management software such as Microsoft Project, Trello, Asana, or TeamGantt.

Step 5: Set Up Your Gantt Chart

Open your chosen Gantt chart tool and set up the chart layout. Typically, Gantt charts consist of a horizontal timeline representing project duration and vertical bars representing individual tasks. Customize the chart settings as needed, such as adjusting the time scale and task formatting.

Step 6: Enter Task Details

Enter the project tasks, milestones, and their respective start and end dates into the Gantt chart tool. Arrange the tasks in chronological order along the timeline. Indicate milestones with distinct markers or symbols to highlight their significance.

Step 7: Define Task Dependencies

Establish task dependencies by linking related tasks on the Gantt chart. Use arrows or connectors to indicate the sequence of tasks and their dependencies. Ensure that task dependencies accurately reflect the project’s workflow and constraints.

Step 8: Adjust Task Durations and Resource Allocation

Fine-tune task durations and resource allocation as needed based on project requirements and constraints. Consider reallocating resources, adjusting task dependencies, or modifying schedules to optimize project efficiency and minimize delays.

Step 9: Review and Finalize the Gantt Chart

Review the completed Gantt chart to ensure accuracy, clarity, and consistency. Verify that all tasks, milestones, dependencies, and resource allocations are correctly represented. Make any necessary adjustments or revisions before finalizing the chart.

Step 10: Share and Communicate the Gantt Chart

Share the finalized Gantt chart with project stakeholders, team members, and other relevant parties. Use the Gantt chart as a visual communication tool to provide project updates, track progress, and coordinate tasks effectively. Regularly update and revise the Gantt chart as the project progresses.

Flowcharts 

Flowcharts are another useful data representation tool in project management. They can depict the flow of tasks or processes within a project. Flowcharts can help project managers to identify bottlenecks in the project process and make improvements to increase efficiency. They can also identify areas where tasks require automation or outsourcing.

Here’s a detailed step-by-step guide on how to make a flowchart:

Step 1: Define the Process or Workflow

Before creating the flowchart, clearly define the process or workflow you want to represent. Identify the sequence of steps, decision points, and interactions involved in the project process. Break down the process into manageable steps to ensure clarity and accuracy.

Step 2: Determine Flowchart Symbols

Choose the appropriate flowchart symbols to represent different elements of the process. Common flowchart symbols include:

Start/End: Represented by a rounded rectangle or oval shape to indicate the beginning or end of the process. Process: Represented by a rectangle to indicate a specific action or task. Decision: Represented by a diamond shape to indicate a decision point with multiple possible outcomes. Connector: Represented by a circle or oval shape to connect different parts of the flowchart. Arrow: Used to show the direction of flow between steps or decision points. Step 3: Select a Flowchart Tool

Select a software tool or platform to create your flowchart. You can choose from various options, including Microsoft Visio, Lucidchart, draw.io, or even Microsoft PowerPoint or Google Slides for simpler flowcharts. Choose a tool that offers the features and flexibility you need to create and edit your flowchart effectively.

Step 4: Set Up Your Flowchart

Open your chosen flowchart tool and set up the chart layout. Create a new document or template for your flowchart. Customize the canvas size, orientation, and grid settings as needed to accommodate the flowchart’s complexity and size.

Step 5: Add Flowchart Symbols

Begin adding flowchart symbols to represent the different elements of the process. Start with the start/end symbol to indicate the beginning or end of the process. Then, add process symbols to represent each task or action in the workflow. Use decision symbols to represent decision points and connectors to link the symbols together.

Step 6: Define Flowchart Logic

Define the logic and sequence of steps in the flowchart. Use arrows to indicate the direction of flow between symbols. Ensure that the flowchart accurately reflects the process flow and decision-making logic involved in the project process.

Step 7: Test and Validate the Flowchart

Review the completed flowchart to ensure accuracy, clarity, and logical flow. Test the flowchart by following the sequence of steps to verify that it accurately represents the project process. Validate the flowchart with stakeholders or subject matter experts to identify any discrepancies or areas for improvement.

Step 8: Revise and Finalize the Flowchart

Make any necessary revisions or adjustments to the flowchart based on feedback and testing results. Ensure that the flowchart is clear, concise, and easy to understand. Finalize the flowchart once all revisions have been made and stakeholders are satisfied with the representation of the project process.

Step 9: Share and Communicate the Flowchart

Share the finalized flowchart with project stakeholders, team members, and other relevant parties. Use the flowchart as a visual communication tool to illustrate the project process, identify bottlenecks, and make improvements to increase efficiency. Regularly update and revise the flowchart as needed to reflect changes in the project process.

Network Diagrams 

Network diagrams are used to depict the relationships between project tasks. They can help project managers to identify critical paths and dependencies between tasks. Network diagrams can also help to identify potential risks in the project schedule and develop contingency plans to mitigate these risks.

Here’s a detailed step-by-step guide on how to make a network diagram:

Step 1: Define Your Project Activities

Before creating the network diagram, identify all the activities required to complete your project. Activities should be specific, measurable, and time-bound, and they should represent the individual tasks or steps needed to achieve project objectives.

Step 2: Determine Activity Dependencies

Identify dependencies between project activities, such as finish-to-start, start-to-start, finish-to-finish, or start-to-finish relationships. Dependencies indicate the order in which activities must be completed and help determine the critical path—the longest sequence of dependent activities that determines the project’s minimum duration.

Step 3: Choose a Network Diagram Format

Select a format for your network diagram, such as a Precedence Diagramming Method (PDM) or an Activity-On-Node (AON) diagram. In a PDM diagram, activities are represented as boxes, and arrows indicate dependencies between activities. In an AON diagram, activities are represented as nodes, and arrows indicate the sequence of activities.

Step 4: Select a Network Diagram Tool

Choose a software tool or platform to create your network diagram. Common options include Microsoft Visio, Lucidchart, draw.io, or specialized project management software such as Microsoft Project or Primavera P6. Select a tool that offers the features and flexibility you need to create and edit your network diagram effectively.

Step 5: Set Up Your Network Diagram

Open your chosen network diagram tool and set up the diagram layout. Create a new document or template for your network diagram. Customize the canvas size, orientation, and grid settings as needed to accommodate the diagram’s complexity and size.

Step 6: Add Project Activities

Begin adding project activities to the network diagram. Represent each activity as a box or node on the diagram. Label each activity with a unique identifier and a brief description of the task or step involved. Arrange the activities in chronological order based on their start and end dates.

Step 7: Define Activity Dependencies

Establish activity dependencies by connecting related activities with arrows or lines on the diagram. Use different arrow types to represent different types of dependencies, such as finish-to-start, start-to-start, or finish-to-finish relationships. Ensure that activity dependencies accurately reflect the project’s workflow and constraints.

Step 8: Analyze the Network Diagram

Review the completed network diagram to identify critical paths, bottlenecks, and potential risks in the project schedule. The critical path represents the longest sequence of dependent activities and determines the project’s minimum duration. Use the network diagram to develop contingency plans and mitigate risks to the project schedule.

Step 9: Revise and Finalize the Network Diagram

Make any necessary revisions or adjustments to the network diagram based on feedback and analysis results. Ensure that the diagram is clear, concise, and accurately represents the project’s activities and dependencies. Finalize the network diagram once all revisions have been made and stakeholders are satisfied with the representation of the project schedule.

Step 10: Share and Communicate the Network Diagram

Share the finalized network diagram with project stakeholders, team members, and other relevant parties. Use the network diagram as a visual communication tool to illustrate the project schedule, critical paths, and dependencies between activities. Regularly update and revise the network diagram as needed to reflect changes in the project schedule.

Tables and Spreadsheets 

Project management often uses tables and spreadsheets to organize and analyze data. They can track project budgets, resource allocation, and progress. Tables and spreadsheets can also be used to create reports for stakeholders, which provide an overview of the project’s progress and performance.

Here’s a detailed step-by-step guide on how to use tables and spreadsheets effectively in project management:

Step 1: Identify Data Requirements

Before creating tables and spreadsheets, identify the specific data requirements for your project. Determine what information needs to be tracked, such as project tasks, timelines, budgets, resource allocations, and progress updates. This will help you design tables and spreadsheets that effectively capture and organize relevant project data.

Step 2: Choose the Right Tool

Select a software tool or platform to create your tables and spreadsheets. Common options include Microsoft Excel, Google Sheets, and specialized project management software like Microsoft Project or Smartsheet. Choose a tool that offers the features and functionality you need to organize, analyze, and visualize your project data effectively.

Step 3: Design the Table or Spreadsheet Layout

Plan the layout of your table or spreadsheet based on the identified data requirements. Organize columns and rows to represent different project elements, such as tasks, timelines, budgets, resources, and statuses. Use clear headers and labels to ensure readability and ease of navigation.

Step 4: Enter Project Data

Enter project data into the table or spreadsheet according to the defined layout. Input task names, start and end dates, duration estimates, resource assignments, budget allocations, and other relevant information. Ensure data accuracy and consistency throughout the table or spreadsheet.

Step 5: Format and Customize

Format the table or spreadsheet to enhance visual clarity and organization. Apply formatting options such as bold text, borders, shading, and color-coding to distinguish different types of data and highlight important information. Customize the layout and design to meet your specific project needs and preferences.

Step 6: Use Formulas and Functions

Utilize formulas and functions available in your chosen spreadsheet software to perform calculations and automate data analysis. Calculate project metrics such as total budget, actual costs, resource utilization, task durations, and completion percentages. Incorporate formulas to track progress, identify variances, and generate reports dynamically.

Step 7: Create Charts and Graphs

Visualize project data using charts and graphs to provide stakeholders with meaningful insights. Generate bar charts, line graphs, pie charts, and other visual representations of key project metrics and performance indicators. Embed charts and graphs directly into the table or spreadsheet for easy reference and analysis.

Step 8: Regularly Update and Maintain

Regularly update the table or spreadsheet with the latest project data to ensure accuracy and relevance. Maintain consistency in data entry and formatting to facilitate comparison and analysis over time. Review and revise the table or spreadsheet as needed to reflect changes in project scope, requirements, or priorities.

Step 9: Share and Collaborate

Share the table or spreadsheet with project stakeholders, team members, and other relevant parties to facilitate collaboration and communication. Use cloud-based platforms or shared drives to enable real-time access and updates. Collaborate on data entry, analysis, and reporting to ensure alignment and transparency across the project team.

Step 10: Review and Analyze

Regularly review and analyze the data in the table or spreadsheet to monitor project progress, identify trends, and make informed decisions. Conduct periodic reviews with stakeholders to discuss insights, address issues, and adjust project plans as needed. Use the table or spreadsheet as a central hub for project data management and analysis.

Data representation techniques in project management refer to the methods used to visually depict project-related information, such as schedules, tasks, budgets, and progress. These techniques include charts, graphs, diagrams, tables, and spreadsheets, which help project managers and stakeholders understand, analyze, and communicate complex data effectively.

Data representation techniques are important in project management because they facilitate clear communication, analysis, and decision-making. By visually representing project data, stakeholders can gain insights, identify trends, track progress, and mitigate risks more effectively. Data representation techniques help ensure that project information is accessible, understandable, and actionable for all parties involved.

Common data representation techniques used in project management include Gantt charts, network diagrams, flowcharts, histograms, pie charts, bar graphs, tables, and spreadsheets. Each technique serves a specific purpose, such as illustrating project schedules, dependencies, resource allocations, budget distributions, or performance metrics.

Gantt charts are bar charts that visually represent project schedules, tasks, milestones, and dependencies over time. They provide a clear overview of project timelines, critical paths, and resource allocation, helping stakeholders understand project progress and identify potential delays. Gantt charts also facilitate project planning, scheduling, and coordination by visualizing task sequences and dependencies.

Network diagrams depict the relationships between project tasks and activities, helping project managers identify critical paths, dependencies, and potential risks in the project schedule. Network diagrams enable stakeholders to visualize task sequences, optimize resource allocation, and develop contingency plans to mitigate risks and ensure project success.

Flowcharts illustrate the flow of tasks or processes within a project, helping project managers identify bottlenecks, streamline workflows, and improve efficiency. Flowcharts visualize decision points, alternative paths, and interactions between tasks, enabling stakeholders to understand complex processes and make informed decisions to optimize project execution.

Tables and spreadsheets are valuable tools in project management for organizing, analyzing, and presenting project-related data. They can track project budgets, resource allocations, progress updates, and other key metrics, providing stakeholders with detailed insights into project performance. Tables and spreadsheets also facilitate report generation, data visualization, and collaboration among project team members.

In nutshell, data representation is a critical aspect of project management. Gantt charts, flowcharts, network diagrams, tables, and spreadsheets are all effective tools for organizing and analyzing project data. Project managers can make informed decisions and take proactive steps to ensure project success by utilizing these tools.

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How Data Visualization Tools Can Improve Your Project Management

People process information differently, and there are many types of intelligence.   According to developmental psychologist Howard Gardner, there are nine types of intelligence , which explain how individuals learn.

One type of intelligence that Gardner coined is spatial, which means picture smart. While most people have a melting pot of different types to help them learn, the use of visuals tends to benefit most people. It gives them a concrete way to understand and organize what otherwise might be too abstract.

Data visualization is a great way to help people literally get the big picture. It shows processes and projects in a visual format that pulls people out of the weeds and adds context to their tasks, whether that be with a dashboard , graph or slide presentation.

What Is Data Visualization?

Data visualization is a way to display data in a picture or graphic. It helps people process visually in order to grasp difficult concepts and identify patterns not yet discovered.

This is not a new idea. Think of maps that help people navigate the world. By the 1800s, the pie chart was invented. However, with the invention of the computer and its ability to work with large amounts of data, the use of data visualization can make complex information easier to comprehend.

As a means of communication, data visualization uses statistical graphics, information graphics and other tools for clear and efficient communications.

Qualities of Great Data Visualization

The thing about data visualization is that it’s a blend of art and science. According to Edward Tufte, who wrote the book on data visualization called The Visual Display of Quantitative Information , “Excellence in statistical graphics consists of complex ideas communicated with clarity, precision and efficiency.”

Tufte listed what he believes all graphic displays should do:

• Show the data
• Make the graphic elements, methodology, etc., background, so the substance is what the viewer is thinking about
• Never distort the data
• Present many numbers in a small space
• Make large data coherent
• Think of the viewer and what they’re looking at, help guide their eye to what’s important
• Go from the big picture to more detailed
• Have a clear purpose
• Integrate statistical and verbal descriptions of data
• Have the graphic reveal the data

Within project management, data visualization can help in several ways.

• Informational Graphics: This is where visual graphs and charts relay complex data quickly. They help people see patterns in the data, such as trends that might not be as obvious without visuals.
• Visual Literacy: Pictures can be read. Data is communicated and understood by a viewer’s ability to interpret meaning from the image. Literacy is not solely regulated by words.
• Exploratory Data Analysis: By modeling data sets in a visual context, the main characteristics can be highlighted and summarized.

Data visualization has many tools that can be incorporated for communication, such as charts, dashboards, diagrams, drawings, graphs, ideograms, pictograms, data plots, schematics, tables, technical drawings and maps.

Data Visualization and Project Management

Projects, even small ones, can often be complex. There are many variables to control, teams to lead and budget and time constraints to manage. Data visualization can be used as a tool to understand conceptual and idea-development processes. It fosters communication with the project team in a visual language that all can understand.

Some visual tools that can be used in project management include mind mapping, process mapping, storyboarding, root cause analysis, charting, diagramming, graphing, drawing, sketching, wireframing and use cases.

Enhance Communication

Communication is an overriding concern in every aspect of project management, and data visualization is a great way to communicate clearly and effectively to both teams and stakeholders. While it was normal in the past to use written reports or verbal updates, data visualization has offered a better way to communicate complex information.

For example, there’s a project dashboard that can be set up to track the metrics you want to measure and turn those into simple, visually appealing graphs and charts. But the dashboard is only one method, there are also visual ways to report on earned value analysis, make road maps, use Kanban boards in lead methodology or Scrum boards for Agile.

Boost Collaboration

Data visualization also supports a collaborative environment. To get teams to collaborate and better communicate, project managers have used such visual tools and techniques as a project display wall, project collaboration wall, project social media, 3-D project environments, project gamification, etc.

There are many points during a project in which data visualization can help streamline the process. Projects are data-rich environments, and data visualization can help you with the status of a project. You can use it to help disseminate data about project planning, execution, monitoring and even control activities.

Improve Clarity

Data visualization also helps with improving the clarity of the project scope and all operational planning. Resource allocation can also be boiled down to essentials that help decision-making.

And when there is a change to the scope, plan or priority of a project, data visualization helps relay that information in a way that everyone can understand. Plus, these visual materials can be delivered and consumed at times that are convenient for the target audience.

Data management offers at-a-glance views of project status and, if you’re working with an online project management software , real-time project status reporting, issue management and resolution status. With all this data delivered simply, decision-making is also improved.

Old Versus New

The difference between data visualization and more traditional project management information communications is that in the past it was the project manager who pushed communications and determined what was being delivered. The recipient has little say in the matter.

Now, with electronic communications prevalent, the recipient has more control over when they get the information. Rather than a meeting, conference call or email, the data visualization is shared in a common location, which fosters communications between the project manager and the team or stakeholders. The project manager can take the data visualization and target it to the needs of the audience.

There’s a data visualization revolution , according to Scientific American, and its use is no longer located solely in the graphic design department. Project managers have access to huge amounts of data, and data visualization lets them design and share it in an easily digestible visual aid. Are you using this powerful tool?

ProjectManager makes data visualization easy. Our cloud-based software gets real-time data and feeds it instantly to a real-time dashboard. Then those metrics are translated into easy-to-read graphs and charts that can be formatted to show only the data you want for the audience you’re delivering it to. Sharing is also simple, with just a keystroke. See how ProjectManager can help with your data visualization by taking this free 30-day trial.

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Data is necessary for project management. It serves as the backbone for all types of decisions to be made by the project manager. It is, therefore, important to collect, organize and present data clearly so that all stakeholders will understand the status of the project. This is the reason why it is so important for good project managers to not only know and understand but also utilize data gathering and representation techniques. These techniques are used to collect, organize and present data and other information involved in the project life cycle.

There are two types of data gathering and representation techniques used in project management and these include (1) interviewing and (2) probability distribution. Interviewing is a technique that draws the historical data to quantify the impact of risks on the objectives of the project. The information that needs to be collected and organized depends on the type of probability distributions used. It is also important to create the necessary documents such as the risk ranges to provide important insight on the credibility of the analysis of the data.

The second method uses extensive simulation and modeling. It represents uncertain values like duration of scheduled activities as well as the cost of the different components of the project. The probability distribution may include discrete and uniform depending on the data that is available. Distribution methods are used to depict that shapes compatible with the data developed during a quantitative risk analysis. Aside from the two methods, simulation is also another method used to estimate the risk and probability distribution.

In project management, the data gathering and representation techniques are very important in performing quantitative risk analysis and management plans. It is, therefore, crucial for the project manager to use these techniques to shed light on what the collected data is all about.

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• Data Interpretation 101: How Project Managers Can Use Data To Make Better Decisions

Data is essential. It helps people make better decisions. It helps companies avoid terrible decisions. It gives you the insight you need to steer your team in the right direction. But how do you turn a pile of numbers — or a bunch of user interviews — into something you can actually use? You use a process called data interpretation.

Here’s a guide to data interpretation for project managers, marketers, developers, and anyone else who depends on data.

What is data?

Data, at its core, is information. It comes in all shapes, across all kinds of sources, but it’s always information . A statement can be data, if used as evidence to make a point. Numbers are most often what you’ll think of when you hear the term “data.” Anything from percentages to measurements and counts can be data. As a project manager, examples of data points you encounter include revenue, time spent on a task, overdue tasks, and user-based metrics like signups and churn.

Quantitative vs. Qualitative data

All data fall into one of these two categories. Quantitative data is any data that can be represented by a number. Revenue, customer churn, and the number of signups are all examples of quantitative data.

In contrast, qualitative data is usually represented with words. Statements from user interviews and feedback from employees are both examples of qualitative data.

As a project manager, your data interpretation skills will have to cover both types of data.

The 7-step data interpretation process

Now that you know what data is, let’s dive into how you can use it in your project management work. This process assumes you’re responsible for both getting the data together and interpreting it. If you already have your data in hand, you can start with step 4.

Data interpretation step 1: Pick the right data

Before you start getting data together , you need to know what you’re looking for. If you’re dealing with quantitative data, you’ll want to make sure you have the right metrics. Even a project dedicated to something very specific — like reducing churn — could have its success measured with all sorts of metrics.

For qualitative data interpretation, you’ll need to make sure you’re getting the right information. That means making your questions easy to understand, asking the right questions, and asking the right amount of questions, too.

Data interpretation step 2: Data collection

Once you know what kind of data you need to collect, it’s time to go out and actually get it. Depending on what you’re gathering, that might be as simple as asking a data specialist for access to their tools or as complex as planning a series of user interviews.

No matter how your data collection happens, standardize everything . With numbers, make sure they’re all referring to the same thing, in the same way, even if they’re scattered across many tools. With words, make sure you’re asking the same questions every time . Otherwise, you’ll be creating problems for yourself further down the road.

Data interpretation step 3: Process data

Now that you have a pile of data, it’s time to make sense of it. For quantitative data, it can mean putting it in a table or building an interface with a tool like Airtable to better represent what you’re working with.

With qualitative data, you’ll need to do something called coding . No, don’t worry, you don’t have to be a programmer to do this. All it means is identifying themes and relationships across bits of data and applying them to what you’ve collected. That way, you go from a bunch of random statements to trends and categories.

Data interpretation step 4: Clean up your data

All your data is processed and coded, so it’s time to dive in, right?

No matter how careful you are when collecting and processing data, you’re guaranteed to run into some weirdness. With quantitative data, you might get corrupted entries when you’re exporting from multiple tools, for example.

With qualitative data, your transcription tool of choice might have confused synergy with clergy , or you might have to edit out some…interesting answers.

So it’s important that you take the time to clean up your data.

Data interpretation step 5: Explore your results

Alright, now that everything’s cleaned up, it’s time to actually dive in and see what you’re working with. This is when you start looking for patterns, anomalies, outliers, and otherwise comparing your results against your initial hypothesis.

There are a ton of methods you can use to explore data, from counting unique values to checking the frequency of specific results and clustering similar results.

Whatever you choose, this is when you go through and start making sense of your data. This is what most people think of when they hear the term “data interpretation.”

Data interpretation step 6: Turn your data into a chart or model

Now that you’ve done most of the hard work that comes with data interpretation, it’s time to make your data palatable for everyone else. That might mean loading it into a tool everyone uses — like Google Sheets or Airtable — or even boiling it down to a simple write-up.

The method you pick has to be easy to consume for your target audience. If you’re sharing data with the data team , you can go all out. If you’re dealing with stakeholders from different teams, you’re going to have to simplify things.

Data interpretation step 7: Get insights

What’s the point of collecting all this data if you don’t do anything with it? A big part of data interpretation is turning a spreadsheet or a bunch of interviews into action items that’ll help propel your project forward.

You can do this alone or with your team. Try running a brainstorming session where you present your data and ask for everyone’s help coming up with new initiatives.

No matter how you do it, it’s important to actually use the data you’ve spent all this time working on.

Data interpretation mistakes to watch out for

Now that you know how data interpretation works, it’s time to cover some of the pitfalls that pop up throughout this process. With these in mind, you can avoid common mistakes project managers run into when working with data.

Ignoring bias

A bias is anything that pushes your interpretation of data away from rationality towards pre-conceived notions or problematic patterns. There are a ton of cognitive biases , but here are a few you’re likely to run into:

• Confirmation bias: The tendency to go out looking for data that confirms something you already believe rather than interpreting data from a more neutral position.
• False consensus: Assuming that people agree with you more than they actually do.
• Base rate fallacy: Ignoring more general information in favor of specific information you think is more relevant to whatever you’re exploring.
• Contrast effect: A change in your perception of a specific piece of data when contrasted against other types of data.

When interpreting data, you can look up common examples of bias to try and keep a more objective view of what you’re working with.

Mistaking correlation for causation

This is a classic mistake. While looking through your data, you notice that when Variable A goes up, Variable B goes up as well. “Ah-hah!” you think to yourself. “Variable A’s increase causes an increase in Variable B!”

Congratulations, you just confused correlation with causation. Don’t worry, it happens to everyone.

Correlation describes a relationship between two variables that both move (in one way or another), whereas causation implies that one variable directly causes the other.

For example, say you’re working on reducing customer churn, and you’re analyzing the behavior of churned customers. You might notice that churned customers are responding less to messages from your automated support bot, and that they’re interacting with your product less before they churn. This is an example of correlation. Not responding to a chatbot doesn’t cause your customers to interact with your product less, but you can establish a correlative relationship between the two variables.

Using a small sample size

When determining the data you’re going to collect, you need to make sure the sample you’re selecting is large enough to give you actionable insights. If your sample size is too small, the takeaways from your data might seem solid, but they’re not actually representative of broader trends.

So how do you make sure you’ve picked a sample that’s the right size?

While data specialists use complex processes to calculate this, project managers can use these basic rules from tools4dev.org :

• Go no lower than 100. If the population you’re trying to get data for is smaller than that, you’ll need to include them all in your data.
• Shoot for a maximum of 10% of your overall population, as long as that 10% isn’t over 1,000.
• Pick a number between the minimum and maximum, based on your resources.

Dashboards: the layman’s key for data interpretation

At this point, you might have realized that data interpretation is really complicated. If you’re a project manager who needs to work with data regularly, you might be getting to the point of giving up.

After all, the data you’re working with is often scattered across multiple tools, and you don’t have time to constantly hop back and forth between them to collect it. Especially when you have to report on progress multiple times a month.

That’s why Unito created a template you can use to build automated dashboards that collect data from your project management tools and lay it out for you in a way that’s easy to interpret.

This template — available both for Google Sheets and Microsoft Excel — automatically pulls data from your project management tools to give you actionable insights in minutes .

You’ll know each team member’s workload, get a burndown chart that’ll help keep you on track, and more.

How does it work? With a little help from Unito.

Unito is a no-code 2-way integration platform with some of the deepest options for customization for the most popular tools on the market, including ServiceNow, Azure DevOps , Asana, Excel, Google Sheets, Jira, GitHub, and more. With a Unito flow, you can pull tasks from your project management tool into a spreadsheet, where they’ll stay updated in real time. No more copying and pasting, no more cleaning up piles of data.

The best part? You can try it for 14 days for free, no credit card required.

The numbers don’t lie (except when they do)

Data is essential, and with how widespread SaaS tools have become, it’s not just data scientists that need to know how to use it. But with the right process — and the right tools — data interpretation doesn’t have to feel like you’re climbing a mountain, in sub-zero temperatures, in sandals.

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A guide to data analytics in project management

April 19, 2024 - 10 min read

You’ve got a project at work. It could be anything — launching a new website, setting up a company event, or rolling out a new software. You start by collecting data. This could be the hours your team logs in, cash flow, progress reports, you name it. 

Now, data alone can be overwhelming; it’s just a bunch of numbers. However, analytics turns those numbers into insights. For example, analytics could flag that one phase of the project is sucking up too much time and money. With that insight, you can figure out what’s up and adjust your plan to get things back on track. Data analytics predicts how your project will unfold before it happens. This kind of foresight lets you prepare and adapt.

In this article, you will learn how to use data analytics to forecast your project’s progress. We will also introduce you to the most powerful project management tool, Wrike, which Gartner recognized as a leader in collaborative work management. We’ll show you how big names like Walmart Canada and Sony Picture Television use Wrike’s powerful analytics tools to visualize and manage their projects.

Analyze your project in Wrike — start your free trial now .

What is data analytics in project management?

Data analytics refers to the use of statistical and quantitative methods to analyze datasets and extract insights that can be applied to project management processes . It involves collecting, organizing, and analyzing these datasets to uncover patterns and trends that can inform business decisions. This may include data mining and machine learning, among others.

For an IT team, this might involve tracking the time it takes to resolve support tickets or figuring out which system updates lead to fewer customer complaints. You’re always collecting this data but with data analytics, you can actually use it to improve your work. You can also look forward and predict what could happen. For example, say your analytics show you that server outages spike every time you get a surge in web traffic. Now, you can plan for that and get ahead of the game. And the best part is that you don’t need to be a data scientist to get it. 

There are tools out there that can help any project manager become a data analytics pro. These tools turn that sea of numbers into easy-to-read charts and reports, meaning you can spend less time digging through data and more time managing your project.

Benefits of implementing data analytics in project management

Are you leading a small team or managing large-scale projects? Let’s see how data analytics can significantly enhance your operations. 

Data analytics can help you:

Improve project efficiency and resource allocation

Companies often identify areas where they can optimize their project processes to reduce costs and improve efficiency. They may flag certain tasks that can be automated or outsourced to reduce labor costs, or they may use predictive analytics to forecast resource needs and allocate resources more effectively.

Enhance risk management and mitigation

Businesses can address potential risks and mitigate them before they become major problems. Predictive analytics algorithms can forecast potential risks and enable project managers to take preventive action before they occur.

Optimize project scheduling and time management

Organizations can optimize project scheduling and time management by identifying bottlenecks or areas for more time or resources. Project managers can adjust their schedules or reallocate resources to ensure projects are completed on time and within budget.

Boost stakeholder engagement and communication

Companies can improve stakeholder engagement and communication by providing real-time data on project performance and other key metrics. This can increase transparency and accountability and help to build trust between project managers and stakeholders.

Essential data analytics tools for project managers 

How do you choose from the mountain of data analytics tools at your fingertips? We’ve broken it down into four categories to help you out.

Project management software with analytics features

One essential tool that every project manager should consider is project management software with robust analytics features. But what does that mean? Put simply, it’s a platform that keeps your tasks in order and provides deep insights into every aspect of your project. That’s where Wrike can be a game changer in how you manage your project. 

We have a range of features designed to enhance project tracking and data analysis, making it an ideal choice for project managers. Wrike’s advanced analytics can show you at a glance how resources are being allocated, where bottlenecks might be forming, and how closely project timelines are being adhered to. 

Let’s say you’re rolling out a new software update. You can track each stage with an analytics board, monitor team performance, and predict potential delays based on historical data. You can also generate custom reports to gain insights into various aspects, such as task completion rates, team performance, and resource allocation .  

Wrike also has a variety of other project management features.

• Use a Gantt chart to see the entire scope of a project at a glance, including overlapping activities and dependencies between tasks.  
• Visualize work stages, move tasks between columns as they progress, and monitor workflow bottlenecks with a Kanban board .
• Get a clear view of who is working on what and whether any team member is overloaded, allowing for timely adjustments, with a workload chart .

By integrating Wrike into your project management strategy, you get a data-driven approach that allows you to anticipate challenges and adapt strategies quickly.

Data visualization tools

You know how hard it can be to decipher a dense spreadsheet; it’s time-consuming and, frankly, a bit of a headache. Now, picture the same data displayed as a colorful or interactive graph. Suddenly, everything clicks into place — trends, outliers, and patterns become instantly apparent. 

Data visualization tools allow you to create charts, graphs, and other visualizations that help you quickly understand data and identify patterns. This can be especially useful in identifying trends over time or comparing datasets from multiple sources.

Whether you’re presenting to stakeholders or updating your team, visuals help you tell a clearer story. Instead of overwhelming your audience with numbers and tables, you show them a graph that gets straight to the point. 

Choosing the right tool and the right type of visualization for your data is important. You need to match your needs to the capabilities of the tool, whether that’s generating real-time, interactive dashboards or static reports for monthly meetings. 

You can use Wrike’s:

• Chart view to create and customize charts quickly, turning project data into easy-to-understand visuals
• Dashboards to track milestones, assess team workload, or keep an eye on budget use
• Real-time reports to keep stakeholders updated and make informed decisions quickly as they provide the latest data at any given moment

Predictive analytics tools

Predictive analytics tools offer more advanced data analytics that can help project managers create forecasts based on historical data via machine learning algorithms. They can identify patterns and trends in data that may take time to become apparent to human analysts.

Tools like IBM Watson Analytics or Microsoft Azure Machine Learning can help you identify potential risks or opportunities that may otherwise go unnoticed. By analyzing historical data on project performance, these tools can predict future outcomes with a high degree of accuracy. This can help you make informed decisions and proactively mitigate risks or capitalize on opportunities.

Collaboration and communication tools

Think about the last time you tried to coordinate a team where everyone used different platforms for discussions, task tracking, and file sharing. It’s pretty chaotic. Collaboration and communication tools help you achieve better project outcomes by improving stakeholder engagement and keeping all team members on the same page.

With Wrike’s collaborative features, your team can:

• Link tasks, projects, and even entire folders with specific labels, thanks to cross-tagging
• Add comments directly to images, PDFs, and videos with proofing
• Speed up chains of approval and also send secure links to external partners for sign-off
• Collaborate in real time with colleagues using the live editor
• Use the Wrike Document Editor plugin to edit text documents and spreadsheets without downloading them
• Collect all the necessary project details through external request forms so they automatically funnel into your workflow
• Integrate with cloud storage platforms like Google Drive, Dropbox, and Box, enabling you to attach files to tasks and projects directly from these apps

How to implement data analytics in your project management process

Project management is a complex process that involves many moving parts. From managing resources and timelines to ensuring customer satisfaction, project managers have a lot on their plate. 

Now, how do you factor data analytics into all of that? We’ve got you covered.

1. Identify key performance indicators (KPIs)

Imagine you’re managing a software development project. One of your main goals is to release the product on time. So, what’s your KPI there? Deadlines met, right? You want to track how often your team hits or misses these dates. Miss too many, and you know there’s something wonky going on that needs your attention.

But it goes beyond hitting milestones alone. Say you want to ensure the code is quality — no one likes buggy software. You might look at the number of defects reported after a release. If that number starts climbing, it’s a red flag that something’s not quite right in the development process.

Tracking project completion rates can help you identify areas where your team may need to catch up on or improve deadlines. On the other hand, tracking resource usage can highlight areas where you may be overusing (or indeed, underusing) resources.

2. Collect and organize data

Next, collect and organize the data you need to analyze. This may involve pulling data from multiple sources, such as project management software, CRM systems, or other data sources.

For instance, if you manage a chain of cafés, the data you get from peak sales hours, popular menu items, and customer feedback is gold. You gather this data through your POS system, customer surveys, and social media chatter. Once you’ve got this information, you organize it in a way that’s easy to understand.

Collecting and organizing data can be a time-consuming process, but it’s critical to ensure your analysis is accurate and meaningful. This will give you a complete picture of your project’s performance and allow you to make informed decisions based on that data.

3. Analyze data for insights

Now it’s time to analyze your data for insights through predictive analytics algorithms, data visualization tools, or other analytical methods to uncover patterns and trends that inform your decision-making process.

Using predictive analytics algorithms helps you forecast project outcomes based on historical data, allowing you to adjust your project plan before issues arise. Meanwhile, data visualization tools let you identify trends and patterns in your data that may be difficult to spot otherwise, such as spikes in resource usage or dips in customer satisfaction.

4. Make data-driven decisions

Finally, the insights generated through data analytics should be used to inform your decision-making process. This may call for making changes to your project processes, reallocating resources, or taking other steps to optimize project performance based on the insights gleaned from your analysis.

Let’s say you run an online boutique store. You’ve got sales data, customer feedback, ad click-through rates, and even the time spent on different webpages ready to go. By analyzing this data, you could determine which products are flying off the shelves and which ads are bringing in the most eyeballs. This helps you decide where to invest your marketing dollars and which products to feature more prominently.

Speaking of fashion, clothing rental service Gwynnie Bee overcame significant challenges, such as scaling up operations while maintaining visibility over project timelines, using Wrike. Previously, this was not easily tracked, and risks were difficult to predict. As a direct result of implementing Wrike, Gwynnie Bee achieved a remarkable 60% reduction in order processing time, and shipping quality and speed also saw dramatic improvements.

Phillip Hoffman, Senior Program Manager, says:

“Wrike allows us to plan a project with enough detail that we really reduce the risk, or likelihood of the delay, of the project not going correctly.”

Boost your data analytics skills with Wrike

Wrike provides a sharp lens through which project managers can view their projects’ present state and predict the future. With Wrike, you have a dashboard that displays what each team member is working on and predicts when they might hit a roadblock. And that’s not all. 

If you’re in the middle of a phase in your project, unforeseen delays or oversights won’t catch you off-guard because our AI project risk prediction helps you stay on track. You’ll be notified of any potential overruns so you can adjust schedules or resources accordingly to hit deadlines.

You can also build a repository of data over time, which is like gold for any project manager. This historical data can help you see which processes or project types have been most successful, guiding you to replicate those strategies in the future.  

Want to increase the use of data analytics in your project management processes? Start your two-week trial of Wrike today.

Note: This article was created with the assistance of an AI engine. It has been reviewed and revised by our team of experts to ensure accuracy and quality.

Occasionally we write blog posts where multiple people contribute. Since our idea of having a gladiator arena where contributors would fight to the death to win total authorship wasn’t approved by HR, this was the compromise.

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Data Analysis Made Easy: Unleashing the Power of Pivot Sheets

Data analysis is a crucial skill in today's data-driven world. By carefully examining and interpreting data, businesses and individuals can gain valuable insights that drive informed decision-making. However, the process of data analysis can often be overwhelming and time-consuming, especially when dealing with large datasets. But fear not, there is a solution that can make your data analysis journey much smoother - pivot sheets. Understanding the Basics of Data Analysis Data analysis involves collecting, cleaning, transforming, and modeling data to uncover patterns, trends, and correlations. It allows you to make sense of complex information and extract meaningful insights. Moreover, it is crucial for making informed decisions. because it can identify areas of improvement, uncover market trends, and predict customer behavior. Data analysis also allows individuals to gain valuable insights into their personal lives, such as managing finances or tracking health and fitness. Key Concepts  There are several key concepts in data analysis that are essential to grasp: Descriptive analysis: This involves summarizing and describing the main characteristics of a dataset. Usually, a summary of the data (measures of central tendency via mean, median, and mode) and measures of dispersion (range and standard deviation) are included. Inferential analysis: Using statistical techniques allows you to draw conclusions and make predictions based on a sample of data. This is particularly useful when it is not feasible or practical to collect data from an entire population. Hypothesis testing: This calls for testing assumptions and determining if there is enough evidence to support or reject a hypothesis. This is done by comparing sample data to a null hypothesis, which represents the absence of an effect or relationship. If the evidence is strong enough, you can reject the null hypothesis and conclude that there is a significant effect or relationship in the population. Causal analysis: This explores cause-and-effect relationships between variables in a dataset. It aims to determine if one variable directly influences another variable. This is often done through experimental studies, where one variable is manipulated while others are controlled. [caption id="attachment_490561" align="alignnone" width="1024"] Photo by Luke Chesser on Unsplash[/caption] Introducing Pivot Sheets Now that we have a solid foundation in data analysis, let's discuss the powerful tool known as pivot sheets. Also known as pivot tables, pivot sheets are a feature found in spreadsheet software (e.g., Microsoft Excel or Google Sheets) that allow you to analyze and summarize data quickly and efficiently. Benefits  There are several benefits to using pivot sheets in data analysis: Efficiency: Pivot sheets allow you to perform complex data analysis tasks with just a few clicks, saving you valuable time and effort. They automate the process of summarizing and aggregating data, allowing you to quickly gain insights and make informed decisions. Flexibility: Pivot sheets provide a flexible framework where you can easily change the way data is organized and analyzed, allowing for iterative exploration and deep insights. You can rearrange columns and rows, apply filters, and create calculated fields to customize your analysis.  Visualizations: Pivot sheets offer visually appealing and interactive visualizations that help you understand and communicate the insights derived from your data. With just a few clicks, you can create charts, graphs, and pivot charts that effectively communicate the insights derived from your analysis. Error detection: Pivot sheets can assist in spotting data errors or inconsistencies, ensuring the accuracy and reliability of your analysis. Getting Started with Pivot Sheets Now that we understand the benefits of using pivot sheets, let's dive into how you can get started with this powerful tool. To set up your first pivot sheet, follow these steps: Import or enter your data into a spreadsheet software. Select the data range you want to analyze. Click on the "Pivot Table" or "Pivot Sheet" option in the toolbar. Choose the fields you want to include in your pivot table, such as the columns and rows. Apply any necessary calculations or aggregations to your data. Explore and interact with your pivot table to gain insights. Essential Pivot Sheet Functions When working with pivot sheets, there are several essential functions that you should be familiar with: Pivot: Rearrange and reorganize the data in your pivot table, changing the orientation of your analysis. If you have a pivot table that shows sales data by product category and region, you can use the pivot function to switch the rows and columns, allowing you to analyze the data by region and product category instead. Grouping: Combine data into categories, making it easier to analyze and summarize. This can be particularly useful when you have a large dataset with many individual data points. If you have a pivot table with sales data for each day of the year, you can group the data by month to get a higher-level view of the sales performance over time. Filtering: Narrow down your data set based on specific criteria, allowing for more targeted analysis. If you have a pivot table showing sales data for multiple regions, you can apply a filter to only display the data for a specific region, allowing you to analyze the sales performance in that particular area. Calculations: Pivot sheets offer various calculation options, such as sum, average, or count, to apply to your data. These calculations can be applied to individual data points, groups, or the entire dataset, depending on your analysis needs. You can use the sum calculation to determine the total sales revenue for a specific product category in your pivot table. [caption id="attachment_490567" align="alignnone" width="1024"] Photo by Firmbee.com on Unsplash[/caption] Advanced Techniques in Pivot Sheets While pivot sheets provide a straightforward way to summarize data, they also allow you to manipulate your data to gain deeper insights. Some advanced techniques include: Data formatting: Customize the appearance of your pivot table by changing fonts, colors, and styles. Data slicing: Analyze specific segments of your data by using slicers, which act as interactive filters. Calculated fields: Create new fields based on existing fields in your data, allowing for more complex analysis. Troubleshooting Common Issues Even with its power, pivot sheets can sometimes present challenges. Here are some common issues you may encounter and how to troubleshoot them: Missing data: Double-check that you have included all the necessary data and fields in your pivot table. Inconsistent data formatting: Verify that your data is consistent and properly formatted to avoid calculation errors. Refresh errors: If your pivot table does not update automatically when you change your data, manually refresh it. Large datasets: Pivot sheets may slow down when handling large datasets, so consider optimizing your data before analysis. Optimizing Your Data Analysis with Pivot Sheets Now that you're well-versed in pivot sheets, let's explore some best practices to optimize your data analysis using this powerful tool. Keep your data clean: Ensure that your data is accurate, consistent, and free from errors before using it in your pivot sheets. Regularly update your pivot sheets: Keep your pivot sheets up to date by refreshing the data source periodically. Experiment and iterate: Pivot sheets provide a flexible environment, so don't be afraid to try different configurations and analyze your data from various angles. Document your analysis: Record your steps, insights, and assumptions to create a clear and replicable analysis process. Avoiding Common Pitfalls  While pivot sheets are powerful, it's important to be aware of common pitfalls that can hinder your analysis: Overcomplicating your pivot tables: Keep your pivot tables simple and focused to avoid overwhelming yourself and your audience. Ignoring data integrity: Ensure the integrity of your data by regularly validating, cleaning, and verifying its accuracy. Relying solely on pivot sheets: Pivot sheets are a valuable tool, but they should be complemented with other data analysis techniques to gain a comprehensive understanding of your data. Make Data Analysis Easy with Wrike Unleashing the power of pivot sheets for easy data analysis is like using a powerful magnifying glass. It helps you examine data from different angles and uncover hidden insights. However, managing these pivot sheets across multiple data sets can be complex. This is where Wrike steps in. Within Wrike, you can easily create folders for each data set or pivot sheet. These folders can serve as a place where you can store data details, pivot configurations, and even your data analysis reports. This structured approach brings ease and power to your data analysis, much like a powerful magnifying glass. And when it comes to the other documents and workflows your business needs — whether it's data management or report generation — Wrike has you covered with robust project management features and ready-to-use templates. Ready to make data analysis easy? Start your free trial of Wrike today. Note: This article was created with the assistance of an AI engine. It has been reviewed and revised by our team of experts to ensure accuracy and quality.

How To Make Data Speak With Data Visualization Tools

Data visualization is the process of converting complex data sets into visual representations that are easy to understand and analyze. By using the right data visualization tools, businesses can gain valuable insights from their data and make informed decisions efficiently. In this article, we will explore the importance of data visualization, discuss key features, review some of the best data visualization tools available in the market, and take a look at key trends. Understanding the Importance of Data Visualization Data visualization plays a crucial role in today's business landscape. In an era where data is abundant and decision-making is driven by insights, the ability to interpret and understand data is paramount. Raw data, in its unprocessed form, can be overwhelming and challenging to comprehend. However, data visualization offers a solution to this problem by presenting complex data in a visually appealing and digestible format. Imagine being presented with a spreadsheet filled with seemingly endless rows and columns of numbers. It would take considerable effort and time to make sense of the data and extract any meaningful insights. But with data visualization, this process becomes much simpler. By transforming raw data into interactive and visually stimulating charts, graphs, and diagrams, data visualization allows stakeholders to grasp key insights quickly and effortlessly.  For businesses, data visualization is a game-changer. It enables organizations to analyze vast amounts of data, such as sales figures, customer demographics, and market trends, with ease. By visualizing data, businesses can identify patterns, detect anomalies, and uncover hidden opportunities that may have otherwise gone unnoticed. This newfound ability to extract actionable insights from data empowers businesses to make informed, data-driven decisions and stay ahead of the competition. How Data Visualization Enhances Decision-Making and Communication Effective decision-making requires a thorough understanding of the underlying data. Traditionally, data analysis methods such as spreadsheets and charts have been used to analyze and present data. However, these methods can be time-consuming and cumbersome, especially when dealing with large datasets. Data visualization tools offer a more efficient and user-friendly alternative, that decision-makers can use to easily spot trends, outliers, and patterns that may have a significant impact on their business. For example, a bar chart can show which products are the top sellers, helping companies identify their best-performing products and allocate resources accordingly. Similarly, a scatter plot can reveal correlations between different variables, enabling organizations to make strategic decisions based on these relationships. Furthermore, data visualization enhances communication because visual representations of data are often easier to understand and share compared to raw data or lengthy reports. By using visualizations in presentations and reports, decision-makers can effectively communicate complex information to stakeholders and facilitate meaningful discussions. Key Features to Look for in Data Visualization Tools With the increasing complexity and volume of data, it's essential to choose the right tool with certain features that will cater to your specific needs. Interactivity and Ease of Use The ability to interact with visualizations can provide a more immersive and engaging experience. Look for tools that allow users to zoom in and out, filter data based on specific criteria, and sort data points. You want to be able to explore data from different angles and gain deeper insights. Furthermore, the ease of use of a data visualization tool is crucial, especially for non-technical users. Choose tools that have intuitive interfaces and require minimal training to get started. Empower users across different departments to create and share visualizations, and foster a data-driven culture within your organization. Customization Options Search for tools that offer a wide range of customization options, such as color schemes, chart types, and labeling. You want to have the option of tailoring visualizations to your specific needs and preferences. For example, use the company's brand colors in your visualizations to maintain consistency with your overall branding. Highlight specific data points or emphasize certain trends to make your visualizations more impactful and informative. Integration Capabilities Data is often scattered across various systems and platforms, including databases, spreadsheets, and APIs. Therefore, it's crucial to choose a data visualization tool that seamlessly integrates with your existing data sources and tools. Look for tools that support a wide range of data connections and have robust integration capabilities, for a comprehensive view of your business' performance. You wan to have access to all real-time data and keep your visualizations up to date. For instance, if you have a live dashboard that displays key performance indicators, you want the data to be automatically updated as new information becomes available.  Review of Top Data Visualization Tools In this review, we will explore the three top data visualization tools. Tableau: A Comprehensive Tool for Data Analysis Widely recognized as one of the leading data visualization tools in the market, Tableau offers a user-friendly interface, an extensive set of features, and powerful analytics capabilities. With Tableau's drag-and-drop functionality and intuitive design, users can create interactive dashboards, reports, and charts with ease. This is perfect for both beginners and advanced users looking to handle their large data sets.  Tableau offers a vast library of pre-built visualizations, allowing users to quickly create compelling charts and graphs. It also provides advanced analytics capabilities, such as forecasting, clustering, and trend analysis, empowering users to uncover hidden patterns and trends in their data. Microsoft Power BI: Powerful Business Intelligence A robust data visualization and business intelligence tool that seamlessly integrates with other Microsoft products, Power BI boasts a wide range of visualization options, including charts, maps, and graphs. It also provides advanced analytics capabilities, enabling users to perform complex calculations, create interactive dashboards, and conduct data exploration and analyses. Power BI's strength lies in its flexibility, via the ability to connect to various data sources, both on-premises and in the cloud. It supports direct connections to popular databases, cloud services, and even streaming data. Additionally, its cloud-based nature allows for easy collaboration and data sharing. Users can share reports and dashboards with colleagues, clients, or stakeholders, enabling seamless collaboration and fostering data-driven decision-making across teams. QlikView: A User-friendly Visualization Tool Known for its user-friendly interface and powerful visualization capabilities, QlikView allows users to create highly interactive and dynamic dashboards and reports. QlikView's unique associative data model lets users explore data intuitively by dynamically linking data points. Furthermore, the wide range of visualization options, via bar charts, line charts, and scatter plots, can suit the individual's specific needs by, presenting data in the most impactful way possible. QlikView even boasts robust data security features that keep sensitive information protected. It offers granular access controls, encryption, and data governance capabilities, making it suitable for organizations that prioritize data privacy and compliance. Future Trends in Data Visualization Here are some exciting future trends in data visualization: The Rise of Augmented Reality in Data Visualization Augmented reality (AR) is set to revolutionize the way data is visualized and consumed. AR technologies enable users to overlay virtual visualizations onto the real world, creating a more immersive and interactive experience. Soon, users may be able to walk through virtual data representations, manipulate visualizations with gestures, and gain a deeper understanding of complex data sets. Augmented reality holds immense potential in bridging the gap between the digital and physical worlds, making data visualization more engaging and meaningful. The Impact of AI on Data Visualization Tools Artificial intelligence (AI) is transforming the field of data visualization by automating complex tasks and enhancing the user experience. AI-powered data visualization tools can analyze data, identify patterns, and suggest the most effective visualizations for a given dataset. These tools can also help users interpret their data by generating contextually relevant insights and recommendations. As AI continues to evolve, data visualization tools will become smarter, enabling users to derive insights from data more efficiently and effectively. Overall, data visualization is an essential tool for businesses seeking to derive meaningful insights from their data. By choosing the right data visualization tool, businesses can unlock the power of their data and make informed decisions. Also, understanding the importance of data visualization and evaluating key features in data visualization tools can help businesses stay ahead in the data-driven world. As technology continues to advance, future trends such as augmented reality and AI will further enhance the way we visualize and interpret data. So embrace the power of data visualization and let your data speak for itself. Allow your data to unveil its story with Wrike's best data visualization tools. Start enjoying a free trial today and gain impressive, actionable insights. Note: This article was created with the assistance of an AI engine. It has been reviewed and revised by our team of experts to ensure accuracy and quality.

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PMP PMBOK Common Tools and Techniques  

  October 31, 2021

By   Dave Litten

PMP Common Tools and Techniques

The Project Management Institute’s PMBOK methodology is built around a set of five process groups and 10 knowledge areas. Within each process group, there are many processes, each with its own set of inputs, outputs, and tools.

These processes have inputs, tools, and techniques, that are combined to execute a specific activity on a project and create a specific output. Every process has inputs, which are needed to start the process.

Tools and techniques are things that help you to execute a process. Output is what you get out of the process.

For your PMP exam, these are often referred to as ITTO, which is an acronym standing for input, tools and techniques, and output.

There are however a set of common tools that are used many times throughout those process groups. In this article, I will be explaining to you the set of eight common tools that you will need to know in your PMP exam.

First, here is a graphic showing how the five process groups and their 49 processes plus 10 knowledge areas interact with the PMBOK knowledge areas:

PMP Expert judgement

Expert judgement is one of the most common tools in the planning process. Expert judgement includes hiring an expert or subject matter expert (SME), to help you plan a process or conduct a process.

Experts can be people with specialised knowledge or training in a particular process, industry, or technology. For example, if you must develop a project charter, but you’re not sure how to do it, then hire an expert who can help you with the creation of the project charter.

PMP Data gathering

Data gathering is a tool that is used to do exactly what the name says, gather data about a particular process that you’re working on. On certain processes, you will need to gather additional data before coming up with an output for that process.

For example, when developing a project charter, you might sit down with stakeholders and brainstorm what should and should not be included in the project. Brainstorming is part of data gathering. It is just the technique that is used to gather information in a particular process.

Here are some of the techniques that you might be utilizing when using this tool. These techniques will be used on most of the processes that involve this tool, but there are other techniques that can be used.

• Brainstorming. Brainstorming is when you bring together a group of stakeholders to get ideas and analyse them. Brainstorming sessions are generally facilitated by the project manager
• Interviews. Anytime you want to gather data from a particular group of stakeholders, one of the best methods is to just interview them. Ask them a series of questions and talk with them about their thoughts and views
• Focus groups. A focus group is when you bring together subject matter experts to understand their perspectives and how they would go about solving problems
• Checklist. A checklist is generally created by the organisation and then given to potential stakeholders on a project for them to identify items they may or may not want on a project, and any success criteria they may have for the project
• Questionnaires and surveys. Questionnaires and surveys can be given to stakeholders to better understand what they may be looking for on a project and to better understand their needs

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Data analysis is used to analyze the data that has been gathered. During a process, a project manager and team collect different types of data and will then need to analyze that data in order to make decisions on the project.

For example, in the process of “control schedule”, you will gather data about the schedule, and then you will need to analyse the data to determine if the project is ahead or behind schedule.

Here are some of the techniques that you might be doing when using this tool. These techniques will be used on most of the processes that involve this tool, but there are other techniques that can be used.

• Alternative analysis. Alternative analysis involves looking at different options or ways to accomplish something. For example, by looking at a change request and then determine a few different ways we can implement the change. An alternative analysis is the most popular option in the PMBOK Guide when it comes to doing data analysis
• Root cause analysis (RCA) . A root cause analysis is used to identify the main underlying reason for a particular event. For example, if there were defects in a deliverable, a project manager would use this technique to identify the main cause
• Variance analysis. Variance analysis is used quite often to find the exact differences between different things. For example, a project manager will use variance analysis to identify if a project is on budget by looking at the variance between the planned budget and the actual costs
• Trend analysis. Trend analysis involves looking at data over a period to see if a particular trend is forming. For example, throughout the execution of the project, a project manager will be looking to see if the project his consistently on budget or over budget period

PMP Data representation

Data representation is used throughout the PMBOK Guide to illustrate different ways that data could be shown to stakeholders. Methods generally include the use of charts, matrices, and different types of diagrams. Certain processes will have unique methods to represent their data.

PMP Decision making

In many processes, you will gather a lot of data and then need to make a decision on what to do with that data.

Decision-making is a tool that is used to come to a decision that can best serve the project.

Here are some of the techniques that you might be executing when using this tool. These techniques will be used for most of the processes that have this tool, but there are other techniques that can be used:

• Voting. Voting is used by a group to determine whether to proceed, change, or reject something. Voting can be, majority wins, unanimity, where everyone agrees, or plurality, where a majority is not obtained, but the most popular decision is chosen
• Multi criteria decision analysis . This is when you make a table (matrix) that lists different types of criteria and then evaluate an idea based on those criteria. For example, a project manager would use different criteria when selecting a team member, such as their availability, experience, education, and costs – here, you can make a table listing these criteria and potential team members
• Autocratic decision-making. This is when one person decides for the entire team.
• Interpersonal and team skills. All project managers need to have good interpersonal and team skills in order to manage the different stakeholders that will be on the project. This is the most important tool in real life project management, and any project manager who does not have these skills is sure to have many problems on a project

Here are some of the techniques that you might be executing when using this tool. These techniques will be used on most of the processes that have this tool, but there are other techniques that can be used:

• Active listening. Active listening is understanding, acknowledging, and clarifying what others are saying to you
• Conflict management. Anytime you bring a team together, you are bound to have conflicts on that team. A project manager or need to resolve these conflicts in order to move forward
• Facilitation . Facilitation is the art of managing a group. This can include bringing the group together, generating ideas, solving problems, and dissipating the team. This is generally the job of the project manager when it comes to the facilitation of a project team. This will be a key skill in real life project management, as you will have to facilitate groups of different stakeholders
• Meeting management. Most projects will have many meetings involving different types of stakeholders. A project manager needs to be able to manage these meetings to ensure they are productive and meaningful to the project. Meeting management generally includes having an agenda, inviting the right stakeholders, setting a time limit, and following up with meeting minutes and action items.

PMP Project Management Information System (PMIS)

The PMIS is an automated system that is used to help the project manager optimise the schedule or keep track of all the documents and the deliverables. It is usually a computer system that an organisation uses to manage its projects. It should include all the software and hardware tools that we need to manage the project from start to finish.

The PMIS includes the work authorization system and the configuration management system. The work authorization system is used to ensure work gets done in the right order and at the right time.

The purpose of the configuration management system is to ensure the product gets the right settings and configuration. The configuration management system includes the change management system which is used to ensure that changes to a project are documented, tracked, and authorised or denied.

PMP Meetings

Meetings are used often in the 49 processes, and can be done face to face or virtually. Meetings frequently include all different types of stakeholders throughout the project, and here are some points to make meetings effective:

• have an agenda and distribute it to all attendees before the meeting
• meetings must be timed, including having set start and finish times for topics and the entire meeting
• make sure that the meeting always stays on topic and does not go off topic
• ensure that all attendees have input on the topics distribute detailed meeting minutes once the meeting is complete

If you want to pass your PMP Exam at, first try, then get your hands on Projex Academy’s PMP Masterclass teaching you the PMP PMBOK Exam Syllabus and exam containing 50% of agile questions:

Dave Litten

Dave spent 25+ years as a senior project manager for UK and USA multinationals and has deep experience in project management. He now develops a wide range of Project Management Masterclasses, under the Projex Academy brand name. In addition, David runs project management training seminars across the world, and is a prolific writer on the many topics of project management.

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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

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What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

About the Author

• Data Gathering and Representation Techniques

• Interviewing. Interviewing techniques are used to quantify the probability and impact of risks on project objectives. The information needed depends upon the type of probability distributions that will be used. For instance, information would be gathered on the optimistic (low), pessimistic (high), and most likely scenarios for some commonly used distributions, and the mean and standard deviation for others. Examples of three-point estimates for cost arc shown in Figure 11-8. Documenting the rationale of the risk ranges and the assumptions behind them arc important components of the risk interview, bccause they can provide insight on the reliability and credibility of the analysis.

Interviewing relevant stakeholders helps determine the three-point estimates for each WBS element for triangular, beta or other distributions. In this example, the likelihood of completing the project at or below the most likely estimate of $41 million is relatively small as shown in the simulation results in Figure appearing in 11.4,2.2 (Modeling and simulation

Figure 11-8. Range of Project Cost Estimates Collected During the Risk Interview

• Probability distributions. Continuous probability distributions represent the uncertainty in values, such as durations of schedule activities and costs of projcct components. Discrete distributions can be used to represent uncertain events, such as the outcome of a test or a possible scenario in a decision tree. Two examples of widely used continuous distributions arc shown in Figure 11-9. These asymmetrical distributions depict shapes that are compatible with the data typically developed during the quantitative risk analysis. Uniform distributions can be used if there is no obvious value that is more likely than any other between specified high and low bounds, such as in the early concept stage of design.

Beta Distribution Triangular Distribution

Seta and triangular distributions are frequently used in quantitative risk analysis. The data shown here is one example of a family of such distributions determined by two "shape parameters". Other commonly used distributions include the uniform, normal and lognormal. In these charts the horizontal (X) axes represent possible values of time or cost and the vertical (Y) axes represent relative likelihood,

Figure 11-9. Examples of Commonly Used Probability Distributions

• Expert judgment. Subject matter experts internal or external to the organization, such as engineering or statistical experts, validate data and techniques.

Continue reading here: Quantitative Risk Analysis and Modeling Techniques

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Delivering Project Management Excellence

Tools and Techniques Group PMBOK

Tools and Techniques in PMBOK 6th edition has seven groups of 132 different project management tools and techniques. These tools are grouped as per their purpose. The group names describe the intent of what needs to be done. The project management techniques in the group represent different methods to accomplish the intent.

Table of Contents

Project Management Tools and Techniques Groups

PMBOK 6th edition has seven groups of 132 different project management tools. The following list enumerates the break-up of these tools based on their groups.

• 09 – Data Gathering Tools
• 27 – Data Analysis Techniques
• 15 – Data Representation Tools and Techniques
• 02 – Decision Making Techniques
• 02 – Communication Skills
• 17 – Interpersonal and Team Skills
• 60 – Ungrouped Tools and Techniques

Data Gathering Tools

Data-gathering tools and techniques are used to collect data and information from a variety of sources.  The following list enumerates the nine data-gathering tools.

• Benchmarking
• Brainstorming
• Check sheets
• Focus groups
• Market research
• Questions and Surveys
• Statistical sampling

Data Analysis Techniques

Data analysis tools and techniques are used to organize, assess and evaluate data and information. There are 27 data analysis tools. The following paragraph enumerates important data analysis techniques.

• Cost of Quality
• Cost-Benefit Analysis
• Decision Tree Analysis
• Earned Value Analysis
• Make-or-Buy Analysis
• Process Analysis
• Regression Analysis
• Risk Probability And Impact Assessment
• Root Cause Analysis
• SWOT Analysis
• Trend Analysis
• Variance Analysis
• What-if Scenario Analysis

Also read: Seven Basic Quality Tools PMP Exam Guide

Data Representation Tools and Techniques

Data representation techniques represent the data and information in a visual format. There are 15 data representation tools. The following list enumerates some of the important data representation tools.

• Affinity Diagrams
• Cause-and-Effect Diagrams
• Control charts
• Mind Mapping
• Probability and Impact Matrix
• Scatter Diagrams
• Stakeholder Engagement Assessment Matrix
• Stakeholder mapping/representation.

Also read:  Quality Control Data Representation Tools

Decision-Making Techniques

Decision-making techniques are used to select a course of action from different alternatives. The following are the two decision-making techniques.

• Multi-criteria decision analysis

Communication Skills

Communication skills are the tools used to transfer information among different project stakeholders. The following are the two communication skills.

• Presentations

Interpersonal and Team Skills

Interpersonal and team skills consist of 17 different techniques used to lead project teams. The following list presents some important interpersonal skills.

• Active Listening
• Conflict Management
• Cultural awareness
• Decision Making
• Emotional Intelligence
• Facilitation
• Influencing
• Negotiation
• Nominal Group Technique
• Political awareness

Ungrouped Tools and Techniques

This group consists of an exhaustive list of 60 different tools. The following list describes some of the most important project management techniques in this category.

• Critical Path Method
• Decomposition
• Knowledge Management
• Leads and Lags
• Organizational Theory
• Precedence Diagramming Method
• Resource Optimization
• Risk Categorization
• Rolling Wave Planning
• Schedule Compression
• Schedule Network Analysis
• Three Point Estimating
• To-Complete Performance Index

To summarize, the 132 project management tools described in PMBOK 6th edition are good practices that can be used for successful project delivery. Appendix X6 in PMBOK maps all 132 tools with their processes and corresponding knowledge areas. For a complete list of these techniques refer to The Project Management Body of Knowledge, 6th Edition®.

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Data Representation – PMP Tools

Data Representation  Tools

Description, affinity diagrams, cause-and-effect diagrams, control charts, hierarchical charts, logical data model, matrix diagrams, mind mapping, probability and impact matrix, responsibility assignment matrix, scatter diagrams, stakeholder engagement assessment matrix, stakeholder mapping, text-oriented formats, related posts:.

• Decision Making – PMP Tools
• Data Gathering – PMP Tools
• Data Analysis – PMP Tools
• Communication Skills – PMP Tools
• Interpersonal & Team Skills – PMP Tools
• General Project Management PMP Tools
• PMP Knowledge Areas
• COMPLETE LIST OF PMP TOOLS AND TECHNIQUES
• TEXT-ORIENTED FORMATS – PMP Tools and Techniques
• STAKEHOLDER MAPPING – PMP Tools and Techniques
• STAKEHOLDER ENGAGEMENT ASSESSMENT MATRIX – PMP Tools and Techniques
• LOGICAL DATA MODEL – PMP Tools and Techniques

Kailash B is a project management expert. He is passionate about helping people to excel in project and product management, become PMP certified, and progress in their management careers.

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• Data Science

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What is Data Visualization, and What is its Role in Data Science?

• Written by Karin Kelley
• Updated on July 29, 2024

How do you transform endless rows of data into a story that can drive decisions and inspire action? Data visualization is the answer. In our data-driven world, converting complex data sets into intuitive, visual formats is essential for uncovering insights and making informed decisions.

This blog answers the high-level question: “What is data visualization?” and discusses its importance, various categories, techniques, and practical applications. We’ll explore how visualizing data can turn raw numbers into powerful narratives that inform, engage, and persuade. Those looking to master this skill and advance their data science career should consider enrolling in a comprehensive data science bootcamp .

So, What is Data Visualization?

Data visualization is the graphical representation of information and data. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. This visual context helps users understand the data’s insights and make data-driven decisions more effectively. It’s a powerful tool for exploratory data analysis and conveying findings to others.

Data visualization involves transforming raw data into visual formats that reveal patterns, trends, and correlations. This transformation process can range from simple static charts to highly interactive and complex visualizations. The goal is to make the data more understandable, insightful, and actionable. Effective data visualization leverages visual perception principles to present data in a way that is both aesthetically pleasing and informative.

It is not just about creating pretty pictures but about creating meaningful representations that can tell a story or answer a question. This process involves understanding the audience, selecting the appropriate visualization techniques, and presenting the data in a way that aligns with the intended message. For instance, a bar chart might be used to compare sales figures across different regions, while a line chart could illustrate trends in stock prices over time.

Also Read: Technology at Work: Data Science in Finance

Why is Data Visualization Important?

Data visualization is essential because it transforms complex data into clear and actionable insights. Converting raw numbers into visual formats allows for quick comprehension and informed decision-making.

Here are some key reasons why data visualization is so crucial.

• Unlocking the narrative: Data visualization helps uncover the story hidden within data, allowing decision-makers to grasp complex concepts and identify new patterns.
• Effective communication: It plays a vital role in presenting data findings clearly and concisely, ensuring that the message is easily understood.
• Handling big data : Visualization tools enable data scientists and analysts to interpret large data sets quickly and efficiently, driving better business strategies and operations.
• Immediate understanding: Humans are inherently visual creatures, and our brains process visual information more effectively than text or numbers alone. Visual representations allow us to quickly identify trends, spot outliers, and understand relationships within the data.
• Essential for quick decision-making: This immediate comprehension is particularly valuable in fast-paced environments where quick decision-making is crucial.
• Broadening audience reach : Data visualization makes complex data accessible and understandable to a broader audience, including business executives, policymakers, and laypersons.
• Democratizing data: By making data more accessible, visualization empowers more people to engage with and make informed decisions based on data.

What is Data Visualization? Big Data Visualization Categories

Big data visualization involves handling vast and complex data sets, often requiring advanced tools and techniques. It can be categorized into three main types:

Interactive Visualization

This allows users to engage with the data dynamically. Interactive dashboards and reports enable users to manipulate data and uncover insights through various filters and controls. Tools like Tableau and Power BI are commonly used to create interactive visualizations that allow users to drill down into data, explore different perspectives, and gain deeper insights.

Real-time Visualization

This involves visualizing data as it is collected. It’s particularly useful for monitoring live data streams, such as social media feeds, sensor data, or financial market movements. Real-time visualization tools can provide up-to-the-minute insights, enabling quick responses to changing conditions. For example, financial traders use real-time visualization to monitor market movements and make timely investment decisions.

3D Visualization

3D visualization can provide additional depth and clarity for particularly complex data sets. It’s often used in medical imaging, geospatial analysis, and engineering. 3D visualizations can help to understand complex structures and relationships that might be difficult to interpret in two dimensions. For example, 3D visualizations of MRI or CT scans in medical imaging can help doctors diagnose and plan treatments more effectively.

Also Read: The Top Data Science Interview Questions for 2024

Top Data Visualization Techniques

Numerous techniques are available for data visualization, each serving different purposes and data types. Here are some of the top ones.

Bar Charts and Column Charts

These compare different categories or track changes over time. They are simple yet effective for presenting categorical data. Bar charts can be used to compare sales figures across various regions, while column charts can show the change in sales over different quarters.

Line Charts

Line charts connect individual data points to show continuous data, which is ideal for showing trends over time. They are often used to illustrate trends in stock prices, website traffic, or temperature changes over time. Line charts can help to identify patterns and predict future trends.

Pie Charts and Donut Charts

These charts show the proportions of a whole. While popular, they should be used carefully as they can sometimes be misleading. Pie charts are best used for showing simple proportions, such as the market share of different companies. Donut charts are similar but have a central hole, making them visually distinct.

These visualize data through color variations. They effectively show data density and variations across different categories or geographical areas. Heat maps are often used in fields like marketing to show customer activity across various regions or in scientific research to show gene expression levels.

Scatter Plots

These display values for typically two variables for a data set, showing how much one variable is affected by another. Scatter plots help identify correlations and relationships between variables. For example, a scatter plot might show the relationship between advertising spend and sales revenue.

Bubble Charts

Similar to scatter plots, but with an added data dimension represented by the bubble size. Bubble charts can show the relationship between three variables, with the size of the bubble representing the third variable. They are often used in business to show the performance of different products or regions.

Geospatial Maps

Geospatial maps visualize data related to geographical locations and are useful in fields like meteorology, urban planning, and logistics. They can show the distribution of phenomena across different regions, such as the spread of diseases, population density, or delivery routes.

Also Read: Big Data and Analytics: Unlocking the Future

What is Data Visualization? Use Cases and Applications

Data visualization is used across various industries and applications, enhancing the ability to understand and utilize data effectively:

Business Intelligence

Companies use data visualization for performance tracking, market analysis, and strategic planning. Tableau and Power BI are popular for creating interactive dashboards and reports. These tools enable businesses to monitor key performance indicators (KPIs), track sales performance, and analyze real-time market trends. For example, a retail company might use a dashboard to monitor daily sales figures, inventory levels, and customer feedback.

Visualization helps understand patient data, track disease outbreaks, and optimize healthcare operations. For example, heat maps can show the spread of diseases geographically, allowing public health officials to allocate resources effectively. In hospitals, data visualization can be used to monitor patient vitals, track the progress of treatments, and identify potential complications early.

In finance, visualization aids in tracking stock market trends, risk management, and portfolio analysis. Real-time visualization tools are crucial for making timely investment decisions. For example, traders use real-time charts to monitor stock prices, identify trends, and execute trades. Risk managers use visualizations to assess portfolios’ risk exposure and develop mitigation strategies.

Marketers use visualization to analyze consumer behavior, campaign performance, and market trends. Interactive dashboards can show the impact of marketing efforts in real time. For example, a marketing team might use a dashboard to track the performance of different campaigns, analyze customer engagement, and optimize marketing strategies based on data insights.

Data visualization improves teaching methods, tracks student performance, and research trends. Educational institutions use visual tools to analyze and present data on student outcomes. For example, schools use dashboards to monitor student attendance, track academic performance, and identify students who need additional support. Researchers use data visualization to analyze education trends and develop evidence-based policies.

Scientific Research

Scientists use data visualization to interpret complex data sets from experiments and simulations. 3D visualizations can provide in-depth insights into scientific phenomena. For example, climate scientists use visualizations to analyze data from climate models, track changes in temperature and precipitation patterns, and predict future climate scenarios. Biologists use visualizations to analyze gene expression data and understand diseases’ underlying mechanisms.

Government and Public Policy

Governments use data visualization to analyze population data, economic indicators, and public health information. This aids in policy-making and public communication. For example, governments use visualizations to track the spread of COVID-19, monitor economic performance, and allocate resources. Policymakers use data visualization to analyze different policies’ impact and communicate findings to the public.

Also Read: Five Outstanding Data Visualization Examples for Marketing

Building Data Science and Data Visualization Skills

Data visualization is more than just a way to present data; it’s a crucial tool for making sense of complex information and driving informed decision-making across various sectors. Aspiring data scientists and analysts should prioritize gaining data visualization expertise to unlock their data’s full potential. Enrolling in a comprehensive data science program can provide the necessary skills and knowledge to excel in this field.

As data continues to grow in volume and complexity, the importance of data visualization will only increase, making it a vital skill for the future. By harnessing the power of data visualization, individuals and organizations can turn data into actionable insights, drive better decisions, and achieve their goals. Whether you’re working in business, healthcare, finance, education, scientific research, or government, data visualization can help you understand and leverage the power of data.

You might also like to read:

Data Science Bootcamps vs. Traditional Degrees: Which Learning Path to Choose?

Data Scientist vs. Machine Learning Engineer

What is A/B Testing in Data Science?

What is Natural Language Generation in Data Science, and Why Does It Matter?

What is Exploratory Data Analysis? Types, Tools, Importance, etc.

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Extending the Project Management Body of Knowledge (PMBOK) for Data Visualization in Software Project Management

• Original Research
• Published: 11 May 2022
• Volume 3 , article number  283 , ( 2022 )

Cite this article

• Julia Colleoni Couto   ORCID: orcid.org/0000-0002-4022-0142 1 ,
• Josiane Kroll 2 ,
• Duncan Dubugras Ruiz 1 &
• Rafael Prikladnicki 1  

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The human brain responds better to visual information when compared to plain text. In other words, our brain consumes visual information more easily than text-only content, which helps improve communication, sharing and retaining information, reducing misinterpretation, and clarifying complex information. However, most of the tools adopted for software project management are based on textual reports. The number of software projects that fail is huge, and the stakeholders’ lack of understanding of the project is among the reasons for project failure. The implementation of data visualization using techniques and tools for project management can help identify and prevent project issues such as unexpected budget increases, unrealistic deadlines, lack of clear goals, and success criteria. In this paper, we extend our previous work by evaluating the proposed data visualization extension for the Guide to the Project Management Body of Knowledge (PMBOK \(\text{\textregistered}\) guide) in terms of its applicability in software project management and alignment within the PMBOK guide. The results from the evaluation show that our proposal adds support to visual project management and helps to identify the status and progress of the project quickly and prevent future issues related to communication. Our proposal was also found to be helpful for less experienced software project managers.

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Acknowledgements

We thank the study participants and acknowledge that this research was partially sponsored by Dell Brazil using incentives of the Brazilian Informatics Law (Law no 8.2.48, year 1991).

This research was partially sponsored by Dell Brazil using incentives of the Brazilian Informatics Law (Law no 8.2.48, year 1991).

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School of Technology, PUCRS University, Porto Alegre, RS, Brazil

Julia Colleoni Couto, Duncan Dubugras Ruiz & Rafael Prikladnicki

University of Manitoba, Winnipeg, MB, Canada

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JC developed the extension, conceived and designed the mapping study, the focus group, and the two surveys, analyzed the data, wrote the paper, prepared figures and tables, reviewed drafts of the paper. JK helped design the evaluation survey, analyzed the data, wrote the paper, prepared figures and tables, reviewed drafts of the paper. DR and RP reviewed drafts of the paper.

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Appendix A Context examples for PMBOK KAs.

Table  6 presents DV techniques and tools and context examples for each PMBOK KA.

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Couto, J.C., Kroll, J., Ruiz, D.D. et al. Extending the Project Management Body of Knowledge (PMBOK) for Data Visualization in Software Project Management. SN COMPUT. SCI. 3 , 283 (2022). https://doi.org/10.1007/s42979-022-01168-z

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Received : 13 September 2021

Accepted : 19 April 2022

Published : 11 May 2022

DOI : https://doi.org/10.1007/s42979-022-01168-z

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