Data Analytics Guide

Data Analytics

Introduction

This is designed to help HR professionals of all levels advance their knowledge in Data Analytics. Leading HR groups such as i4cp, Gartner and HR Certification Institute (HRCI) all agree that a critical

competency for HR professionals is Data Analytics.

It is designed to pair HRCI’s framework of business knowledge with reflection and BorgWarner content that allow you to develop your BorgWarner HR Data Analytics skills!

Remember, when you see this symbol….

Example link

…there are linked resources!

Table of Contents

✓ Introduction • Get Started • FAQ’s • Module 1: Understanding Analytics Types • Module 2: Using Analytics in HR Functions • Module 3: HR Organizational Performance • Module 4: Avoiding Analytics Pitfalls • Module 5: Sourcing the Right Data • Module 6: Visualizing Data for Impact: Data Storytelling • Closing

Frequently Asked

What is Data Analytics?

Data analytics is the practice of extracting meaningful insights from data to better understand current challenges, guide decision-making, and solve problems.

For HR professionals, that means data-driven thinking, and technical skills to collect, clean, analyze, and interpret data. Someone with this ability can identify patterns, uncover trends, and make data-driven recommendations.

What does this mean for HR?

• Knows how to identify patterns and trends in workforce data; this includes metrics like headcount, turnover rate, absenteeism and hire data. Ask the right questions: What is the data telling us? What conclusions can we form from this?

• Learns and understands how to use HR analytics tools, such as how to manipulate and extract data.

• Communicates findings to stakeholders clearly through visuals and storytelling of the data. Makes data-driven recommendations.

How do I measure my proficiency level in Data Analytics? Questions

You can refer to the Data Analytics Insights Competency Resource Guide for the assessment scale.

i4cp Lists Data Analytics as a critical skill for HR development. Click here for their development recommendations.

How is this different from Visier?

Data Analytics (HR Competency)

Visier (Visualization Tool)

A skillset and mindset for interpreting HR data to uncover insights, solve problems, and guide decisions. Goes beyond reporting — builds narratives using trends, patterns, and context. Can be applied using multiple tools (Excel, Visier, OneStream etc.), depending on the data source. Enables HR to become strategic partners by asking “why” and “how” behind the numbers.

A technology platform that visualizes HR data from systems like Workday to support analytics. Offers pre-built dashboards and graphical trends to make data easier to explore. Supports the analytics process but is not comprehensive — some BorgWarner data may not be in Visier. Helps HR save time , reduce manual work, and focus on storytelling and decision making .

Get Started!

There are 6 videos to watch for a total 45 minutes of formal learning content. Each video has a Companion Learning Guide to challenge you to go deeper into the concepts and to connect you to additional learning materials. Learning Process

Navigate to Module 1 in this guide.

Click the video link to start the video.

Consider how to enrich your experience by taking time to complete the connection challenges, practice your learning or exploring the questions with a colleague.

As the video plays, complete the reflection activities in this guide. You do not need to answer all the questions on each page!

Repeat for Modules 2, 3, 4, 5, and 6

Have fun and keep learning!

Behaviors Matter

Showing up with the right behaviors before starting this journey will help you learn more deeply. Here are some tips from our BorgWarner Belief Behaviors to help you show up ready to Explore, Learn and Excel in HR!

1. Be Curious! You may already know some of these concepts, but there are always more questions to ask! Stay curious and ask yourself, what else can I learn here? 2. Be Accountable : Put in the work and the effort! You will only grow when you push yourself out of your comfort zone. 3. Be Connected : Many of the reflection questions cannot be answered just by watching the videos. You are encouraged to reach out to others and get their insights and perspectives. Connect to new people through this process!

Be BorgWarner

Learning is great,

Before you start your learning journey, plan for development first.

Learning focuses on acquiring new knowledge. Development focuses on using that knowledge to do something different.

What are the steps to Development?

• Assess your current state using the Data Analytics self assessment in the Data Analytics Competency Resource. It is even better if you get feedback from your manager or peers on what they see in your actions too.

• Identify what gaps you want to close.

• Make use of the 70/20/10 philosophy to structure your development plan. 10% of your actions should focus on formal learning. 20% should be focused on continued feedback from peers, managers, mentors, direct reports, etc. 70% should be focused on doing something differently.

but development is better!!

Is there any thing to help me?

OF COURSE! • Individual Development Plan template – this can help you structure a robust 70/20/10 development plan • The 70/20/10 Guide can help you brainstorm activities in the 70, 20 and 10 categories. • Your BU talent team – Talent Director and Learning Specialist are great resources! • Drive Your Development: Development Plans class

Anything else I should consider?

Remember to add your development plan into Workday as a professional development goal! Even if you use the template above, it is best practice to reference the overall aim of the goal as a Workday Professional Development Goal.

Program Note

Each module includes a short 3 – 5-minute video and guide including self-reflection questions to support your learning.

Use this learning module as an opportunity to explore AI as a learning and development tool! Get creative on the prompts you use to ask questions about concepts you may not yet understand. Spark Learning has an embedded AI Assistant you can use too!

Course Navigation – Remember, you can change the speed of the video using the gear button. You can also add subtitles to the video in different languages. Click the button, then choose your Caption Language

Using this Guide – Best Practices

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Module 1 Understanding Analytics Types 1 2 4 3 Start the video for this module.

Flip the pages of this guide to follow along with the video.

Give yourself time to reflect on the questions asked and explore the additional resources in the guide.

Make sure to stop the video at the end, or it will auto play into a new topic.

Analytics Types: Descriptive

Can you identify three HR metrics or KPIs that you have tracked recently? What trends or patterns stand out to you?

Ask yourself, what happened, when, where, and involving whom?

Was there anything in the data that surprised you? Why?

Analytics Types: Diagnostic

What internal or external factors might be influencing the trends you're seeing in your data? What is one way you could clearly communicate the link between the trends and the underlying causes you are seeing to stakeholders? What are some ways you can validate your assumptions about the "why" when using diagnostic analytics?

Analytics Types: Predictive

Do you currently use predictive analytics at your location for any HR metric? How might predictive analytics help us prepare for future workforce trends, such as growth in business, retirements, or resignations? How can we ensure that our data is complete and accurate to support utilizing a predictive model for business decisions?

Analytics Types: Prescriptive

Prescriptive analytics can be used to solve many different HR issues. How do we choose which HR problems to prioritize? How do we ensure our HR teams have the right skills to understand and make decisions using prescriptive analytics? How can we ensure the recommendations from prescriptive analytics are aligned with BorgWarner's goals and KPIs?

Connection Challenge : Connect with an HR colleague outside of your location or region and learn how they might be using one of these analytics types in their roles. See which HR trends they are tracking and using for predictive analytics that support BorgWarner goals.

Summary

• What is one new thing you learned?

• What is one new thing you are interested in learning more about?

• Is there an area in your HR work where applying one of the analytics types could reveal new meaningful insights?

• How does this align with the development plan you set?

Percipio: Dig Deeper into a Few Analytics Types!

Percipio Video Descriptive Data Analytics Percipio Video What Is Predictive Analytics? Percipio Video Prescriptive Data Analytics

Module 1

Summary

Building "analytics literacy" within an enterprise is crucial for converting data into actionable insights. This literacy empowers businesses to improve processes and foster innovation. A fundamental aspect of this literacy is understanding the "four main categories of analytics: descriptive, diagnostic, predictive, and prescriptive." These categories form the "basic vocabulary of business analytics" and represent a "broad continuum" of increasing complexity, sophistication, insight, and potential business value. While it is "useful to be able to distinguish these four categories," it's crucial to "bear in mind that the boundaries between them aren't fixed." A single business application, such as a sales forecast, may "incorporate elements of several categories." Understanding this "continuum of analytics" not only allows for an appreciation of current analytical uses but also helps in identifying "new opportunities to use them."

Key Terms

• Analytics Literacy: The ability of an enterprise or individual business professional to understand and effectively use different types of analytics to convert data into insights that drive decision-making and actions. • Continuum of Analytics: Refers to the progression from descriptive to diagnostic, predictive, and prescriptive analytics, along which the complexity, sophistication of methods, degree of insight, and potential business value generally increase. • Key Performance Indicator (KPI): A measurable value that demonstrates how effectively a company is achieving key business objectives. Changes in KPIs can trigger diagnostic analysis.

• Descriptive Analytics: The most basic type of analytics that answers the question "what happened?" It involves counting things, performing elementary statistical analyses (averages, distributions, trends), and presenting results in standard business reports. Produces "hindsight." • Diagnostic Analytics: The type of analytics that answers the question "why" something happened. It involves drilling down into data, identifying patterns or anomalies, and using statistical methods (e.g., regression models) to understand causal relationships between variables. Produces "insight." • Predictive Analytics: The type of analytics that answers questions like "which works better?" or "what will happen next?" It involves methods like A/B testing for comparative analysis and business forecasting using complex models of multiple variables. Produces "foresight." • Prescriptive Analytics: The most sophisticated type of analytics that answers the question "what should we do?" It involves complex simulations, optimization models, and recommendation engines, often incorporating AI, to advise on immediate or long-term decisions. Produces "smartsite." • A/B Testing: A method used in predictive analytics that involves comparing two versions of something (A and B) by trying each approach on a significant sample to determine which performs better in terms of driving desired outcomes. • Regression Models: Statistical methods used in diagnostic analytics to quantify the influence of one variable on another, helping to understand causal relationships and patterns in data. • Forecasting: A key application of predictive analytics that involves estimating future outcomes (e.g., sales, profit) based on current and historical data and trends. • Simulations: Computational models used in prescriptive analytics to imitate the operations of a real-world process or system to test different "what if" scenarios and predict outcomes. • Optimization Models: Mathematical techniques used in prescriptive analytics to find the best possible solution or decision from a set of alternatives, often by maximizing or minimizing certain objectives. • Hindsight: The understanding of an event or situation only after it has happened, typically produced by descriptive analytics. • Insight: A deeper understanding of a situation, especially why something happened, typically produced by diagnostic analytics. • Foresight: The ability to predict what will happen or what will be needed in the future, typically produced by predictive analytics. • Smartsite: A term used to describe the type of highly actionable insight produced by prescriptive analytics, enabling optimal decision-making.

Module 2 Using Analytics in HR Functions 1 2 4 3 Start the video for this module. Flip the pages of this guide to follow along with the video. Give yourself time to reflect on the questions asked and explore the additional resources in the guide. Make sure to stop the video at the end, or it will auto play into a new topic.

Data Analytics on Hiring

What is important to measure and analyze about recruiting and hiring in your site or BU?

What measures or insights would you like your hiring analytics to provide that they currently do not?

Is there a hiring-related metric you are not tracking that you believe would be beneficial?

Data Analytics on Performance

What type of data is in Workday for Performance?

What other data could be combined with performance data to generate insights?

Data Analytics on Retention

What data could be useful to analyze and identify the employees at risk of leaving? How can we use retention analytics to understand why people are leaving? How do we act on those insights?

Connection Challenge : Dig deep into your retention data. Then, find another HR site and ask them to review their analysis. Share insights generated from the analysis and the overall process so you learn and teach each other.

Data Analytics on Workforce Planning

How could you use analytics to determine future workforce needs, such as retirements or turnover? How do you ensure that workforce planning is aligned with future business goals and needs? Think of an example where workforce analytics might help you prepare for future business needs.

Summary

o What is one new thing you learned?

o What is one new thing you are interested in learning more about?

o Considering our current KPIs at BorgWarner, which HR area do you believe would yield the most valuable insights? Hiring, Performance, Retention, or Workforce Planning?

Visier University : Boost your People Analytics Skills with these courses!

Introduction to People Analytics Headcount & Movement Retention

Note: Access to Visier is required to register for these courses.

Module 2

Summary

This video explores the evolving role of HR analytics in driving business performance, shifting HR from traditional silos to strategic leadership. It highlights how HR measures, metrics, and analytics provide data-driven insights for effective workforce management and decision-making. The video details the reasons to implement HR analytics, such as measuring and managing, improving ROI, and enhancing performance, along with the five-step lifecycle of HR analytics, from question identification to conclusion and improvement. Additionally, the video outlines the five phases of HR analytics maturity, emphasizing the progression from basic data justification to creating significant organizational impact through evidence based decisions. Ultimately, the emphasis is on transforming HR into a data-centric function that directly contributes to business objectives and competitive advantage.

Key Terms

• Advanced Analytics: Sophisticated quantitative and qualitative methods used to extract insights, predict future trends, and recommend actions from large datasets, moving beyond basic reporting. • Attrition: The gradual reduction of a workforce due to employees leaving the organization through resignation, retirement, or other reasons. Often expensive for companies.

• Candidate Pool: The total group of individuals who have applied for open positions within an organization. • Composite Profiles: Detailed, data-driven descriptions of employees or jobs, built by combining various attributes such as skills, attitudes, performance indicators, and experience. • Cultural Fit: The likelihood that a candidate's beliefs, values, and behaviors align with those of the organization, contributing positively to the workplace environment. • Employment Life Cycle: The entire progression of an individual's journey within an organization, from hiring and onboarding through development, performance management, engagement, and eventual departure. • Performance Profiles: Data-driven descriptions of the characteristics, skills, and experiences that define top performers in specific job roles or within the organization. • Predictive Models: Statistical or machine learning algorithms that use historical data to identify patterns and forecast future outcomes, such as employee attrition or potential high performers. • Retention: The ability of an organization to keep its employees. High retention rates indicate success in retaining valuable talent. • Talent Supply: The availability of skilled and qualified individuals within an organization or in the external labor market to meet current and future staffing needs. • Workforce Planning: The strategic process of analyzing an organization's current workforce and forecasting future labor needs to ensure the right number of people with the right skills are in the right place at the right time.

Module 3 HR Organizational Performance 1 2 4 3 Start the video for this module. Flip the pages of this guide to follow along with the video. Give yourself time to reflect on the questions asked and explore the additional resources in the guide. Make sure to stop the video at the end, or it will auto play into a new topic.

HR Metrics

What insights you get from these metrics? How do you use these to help with business performance? Which of these would you like to investigate more? How do your business metrics work with your HR metrics?

Remember that AI can be a valuable tool for exploring these concepts further!

HR Analysis

How can HR analytics help break down silos and foster more integrated decision-making across departments? How might you use sales (revenue) per employee or similar metrics like employee cost as a percent of sales to benchmark and improve team performance? What additional data or tools would help you make a stronger business case for HR programs?

HR Analytics Process Life Cycle

What challenges do you face in shifting HR from a traditional support role to a strategic business partner especially as we work to move towards deeper analytics capability? How ready is your organization to adopt a data-driven approach to talent and workforce strategy?

Journey to HR Analytics

How confident are you in the quality and consistency of your HR data? What steps do you take to validate it? Can you share an example where data analysis led to a successful intervention or improvement in your organization? Which phase of the HR analytics maturity model best describes your organization today — Justification, Measurement, Effectiveness, Value Creation, or Impact? What are the biggest barriers preventing your team from advancing to the next phase?

BorgWarner Bonus! : Learn more from i4cp!

3 4

Unlocking the Power of People Analytics with Buddy Benge of Edward Jones

Ready to try your skills? Choose any (or all!) of the 3 scenarios to practice.

• Each scenario includes: • An overview of the scenario • A business problem statement • A small data set (embedded Excel file) • Recommended analysis • Your tasks: • Read the scenario • Analyze the data using some of the factors you have learned in these courses (Excel) • Create recommendations for next steps for this sample organisation – consider using the principles from Data Storytelling (Module 6) to document your recommendations • Go deeper: • Find a colleague in your site, region, or BU and see how they approached the problem • Compare and contrast approaches and recommendations • Draw conclusions on how to apply these learnings to your work

Scenario: Training Impact on Productivity • Your manufacturing site recently implemented a new training program aimed at improving employee productivity. You want to assess whether the training has had a measurable impact.

Scenario: Staffing

Scenario: Workforce Age and Retention • Your HR team is investigating how workforce age affects turnover and retention. This dataset includes employee age, tenure, and reasons for termination. Analyze trends across age groups

Efficiency at a Manufacturing Site • Your manufacturing

site operates across three departments — Assembly, Packaging, and Welding — with two shifts per day: Morning and Evening. Recently, you have noticed fluctuations in staffing levels that may be affecting production efficiency and employee morale. leadership wants to understand how closely actual

to identify retention challenges and recommend strategies.

• The site

staffing aligns with planned staffing and

whether certain departments or shifts consistently experience

overstaffing or understaffing.

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Data Analytics Learning Guide

Module 3

Summary

This video highlights the critical role of HR analytics in transforming HR from a traditional, siloed function into a strategic business partner, capable of driving organizational performance through data-driven decisions. By moving beyond traditional HR functions and embracing data driven insights, organizations can unlock the full potential of their workforce, make smarter decisions, and directly contribute to business performance and strategic objectives. The shift from relying on "experiences and instincts" to "evidence-based decision making" is critical for HR to become a true strategic partner.

Key Terms

• Analytics: The process of converting metrics into decision-supporting insights to understand and manage the impact of people on business performance. • Evidence-Based Decision Making: Relying on data and facts rather than experience or instinct to make informed choices. • Predictive Models: Analytical tools used in HR analytics to forecast future trends, behaviors, and outcomes based on historical data. • Strategic Analytics: Advanced analytical techniques used to support long-term strategic planning and decision-making within HR.

• HR Analytics Lifecycle: A five-step process encompassing question identification, data gathering, data cleansing, data analysis, and conclusion/improvement. • Question Identification: The first step in the HR analytics lifecycle, involving asking the right questions to understand issues impacting business strategy. • Data Gathering: The second step in the HR analytics lifecycle, involving collecting and selecting data from various sources (HR, business, external) to support research design. • Data Cleansing: The third step in the HR analytics lifecycle, focused on validating data to improve quality and optimize usability by making it integratable and interrogable. • Data Analysis: The fourth step in the HR analytics lifecycle, involving understanding and interpreting data through suitable statistical solutions and business acumen. • Conclusion: The final step in the HR analytics lifecycle, involving translating insights into tangible actions and improvements. • Justification (Phase): The basic, foundational, and reactive first maturity phase of HR analytics, with distributed data collection and rudimentary reporting. • Measurement (Phase): The proactive second maturity phase of HR analytics, utilizing advanced reporting capabilities such as metrics, scorecards, and dashboards. • Effectiveness (Phase): The third maturity phase of HR analytics, characterized by the use of sophisticated tools like strategic analytics and KPIs, with cohesive efforts for process improvements. • Value Creation (Phase): The fourth maturity phase of HR analytics, focusing on predictive models for genuine insights that produce cultural shifts and data-driven decisions. • Impact (Phase): The fifth and highest maturity phase of HR analytics, where HR provides insights that significantly impact business operations and achieve the true purpose of analytics.

Module 4 Avoiding Analytics Pitfalls 1 2 4 3 Start the video for this module. Flip the pages of this guide to follow along with the video. Give yourself time to reflect on the questions asked and explore the additional resources in the guide. Make sure to stop the video at the end, or it will auto play into a new topic.

Common Mistakes in Data Analytics

Think about the metrics you are tracking now. What is your objective in tracking these metrics? How are they aligned within the broader organization? Think Workday Data Quality. How confident are you in the integrity of your employee data?

Ii4CP: Learn how to gather the right data!

4 Steps to Partner with People Analytics - i4cp

Common Mistakes in Data Analytics

When you are analyzing your data now, how do you test your assumptions? Is that enough? What could you do differently? Think about the insights you are focusing on, how will it lead to a meaningful impact and align with our business goal? How can you distinguish whether an outlier is a one-time anomaly or a sign of a deeper, more significant issue?

Common Mistakes in Data Analytics

Under the situation of a close deadline, how can you ensure you aren't drawing conclusions prematurely?

BorgWarner Bonus! : Learn more from i4cp!

4 1

Creepy Analytics: Avoid Crossing the Line and Establish Ethical HR Analytics for Smarter Workforce Decisions

Broader Limits of Analytics

How do Business Insights and Data Analytics support each other?

Are you aware of the data privacy laws that apply to your work, including those from other countries and regions?

What are some ways you can ensure the data is being used ethically?

Connection Challenge : Connect with a member of the BorgWarner HRIS or Legal Team to ask questions about protecting HR data. Gain insights into how GDPR affects our organization and discover ways to ensure our daily practices remain ethical and compliant with data protection laws.

Summary

• What is one new thing you learned?

• What is one new thing you are interested in learning more about?

• Is there one area of the common mistakes of data analytics that you'd like to be more informed on?

• How does this align with the development plan you set?

Want More? : Click for videos that go deeper into Pitfalls

Why Workday? The Importance of Data Quality

Percipio Video Avoiding Analytics Pitfalls0

Module 4

Summary

The video eight common mistakes that individuals and organizations make when engaging with data analytics: Lacking an Objective, Insufficient Data, Mistaking Correlation for Causation, Mistaking Statistical Significance for Business Significance, Ignoring Outliers, Improper Tool and Methods Selection, and Drawing Conclusions Prematurely. Avoiding these errors is crucial for achieving accurate and impactful results. Beyond specific mistakes, analytics operates within three types of broader limitations: practical, legal, and ethical. Recognizing these is fundamental to being "fully conversant in analytics." The field of analytics ethics is "new and evolving," and organizations should "play it safe" by validating approaches to ensure they are not only legal and ethical but also "that customers will find them acceptable."

Key Terms

• Algorithms: A set of rules or instructions that a computer follows to solve a problem or perform a task. In analytics, algorithms are used to process data, identify patterns, and build predictive models. • Bias (in analytics): Systematic errors or prejudices in the data or algorithms that lead to unfair or inaccurate outcomes, often disadvantaging specific groups. • Causation: A relationship where one event or variable directly causes another event or variable to occur.

• Correlation: A statistical measure that describes the extent to which two variables tend to change together. It indicates a relationship but not necessarily a cause-and-effect link. • Control Group: In formal experiments, a group that does not receive the treatment or intervention being studied, used as a baseline for comparison. • Ethical Limitations: Constraints on analytics related to moral principles and standards, ensuring that data use and algorithms do not violate societal values or lead to unfair outcomes. • Feedback Loop: A system where outputs are returned as inputs to influence future actions, allowing for continuous improvement of models or processes based on results. • Legal Limitations: Constraints on analytics imposed by laws and regulations, particularly concerning data privacy, collection, usage, and individuals' rights over their data. • Outliers: Data points that significantly differ from other observations, potentially indicating errors or unusual events. • Practical Limitations: Constraints on analytics arising from real-world factors such as data quality, the complexity of situations, and the skill of the analysts. • Statistical Significance: A determination by a statistical test that an observed effect is unlikely to have occurred by chance, suggesting a real relationship or difference. • Transparency (in AI): The ability to understand how an AI system works, how it makes decisions, and the data it uses, especially important for accountability and bias detection.

Module 5 Sourcing The Right Data 1 2 4 3 Start the video for this module. Flip the pages of this guide to follow along with the video. Give yourself time to reflect on the questions asked and explore the additional resources in the guide. Make sure to stop the video at the end, or it will auto play into a new topic.

Internal Data

How do you assess the completeness and quality of the internal data you rely on? How do you ensure that the data you use is relevant to the specific HR or business question you're trying to answer? How do you combine different types of internal data (e.g., HR and financial) to tell a more complete story?

External Data

What external data sources have you used in your HR or workforce planning efforts (e.g., labor market trends, benchmarking reports, social media insights)?

Combined Sources

How do you combine external data with your internal HR data to create a more complete picture?

How do you ensure that the external data you use is timely and relevant to your organization’s goals?

Data Sourcing

Think of a business problem you are trying to solve. Start analyzing:

What data is needed?

•

Why is it needed?

•

What is the estimated cost?

•

What is the effort cost?

•

• How much time is required to source the data? • Where are my current data holes?

BorgWarner Bonus! : Learn more from i4cp!

Data Collection Guide

Summary

• What is one new thing you learned?

• What is one new thing you are interested in learning more about?

• Considering some of your projects there any "data holes,“? How can you address them?

• How does this align with the development plan you set?

Module 5

Summary

This video will cover the next steps after defining a problem and identifying the necessary data, focusing on how to source that data while considering time and cost. It explains that data can come from internal sources — such as financial records and employee data — which are typically free and easily accessible. For broader insights like market trends or competitor analysis, external sources such as social media, government databases, or commercial providers may be needed. These external sources vary in cost and reliability but often offer pre-cleaned data that saves time. The video emphasizes the importance of ensuring all data aligns with the business problem and identifying any gaps, or 'data holes,' that still need to be filled.

Key Terms

• Problem Statement: A clear, concise description of the issue or challenge that data analysis aims to address. • Data Wish List: A detailed enumeration of the specific types of data required to solve a defined business problem.

• Internal Data: Information, statistics, or facts that are generated, collected, and maintained within an organization itself. • Operational Data: Data related to the day-to-day functioning and transactions of an organization (e.g., sales transactions, production records). • Employee Data: Information pertaining to an organization's workforce, including payroll, demographics, and performance records. • External Data: Information, statistics, or facts that are obtained from sources outside of an organization. • Data Holes: Specific pieces of data that are identified as necessary for a project but cannot be immediately sourced or are currently unavailable.

Module 6 What is Data Storytelling 1 2 4 3 Start the video for this module. Flip the pages of this guide to follow along with the video. Give yourself time to reflect on the questions asked and explore the additional resources in the guide. Make sure to stop the video at the end, or it will auto play into a new topic.

What is Data Storytelling?

Look at each bold word in the definition above. Think about a

presentation you have heard or given recently. Go back and see if all these elements were in the presentation. Evaluate how the presentation and the audience could have been different if you had considered a structure that focused on all these pieces.

Data Story Elements

How do you ensure your data stories have a clear beginning, middle, and end? What challenges do you face in maintaining this structure? Have you ever had to present an insight that challenged the status quo (e.g., recommending a major policy change)? How did your audience react? What support or tools do you need to improve your ability to tell compelling data stories in your HR role?

BorgWarner Bonus! : Stay in Brand!

BorgWarner Brand Information

If your insight is _____, a story can ____.”

Connection Challenge : Connect with a colleague outside of HR or an HR colleague outside of your location and ask them to brainstorm with you on which of these insight types is matched to your analysis. Work together to make sure the story fits the insight type.

Data Storytelling Process

How do you make sure you clearly define the key stakeholders and what they need to know about the data? How do you ensure your story is interpreted the way you intend it to be? You likely approach the process from start to finish. How often do you use the back arrows in the process to adjust?

Data Storytelling is focusing on an insight and persuading an audience that the outcome of your analysis demands a course of action through narrative and visual communication.

BorgWarner Bonus! : Learn more from i4cp!

T-Mobile's Framework for Data Storytelling

Summary

o What is one new thing you learned?

o What is one new thing you will put into practice?

o What is one new thing you are interested in learning more about?

o How does this align with the development plan you set?

Want More? : Click for classes that go deeper into Storytelling

Spark Learning - Data Visualization and Storytelling Bootcamp Spark Learning – Visualizing Data for Impact: Data Storytelling Visier University – Telling Stories with Data

Module 6

Summary

This video will cover the concept of data storytelling and how it differs from data visualization. It explains that data storytelling combines insights with narrative and visual elements to persuade audiences to take action. Unlike simple visualizations, data stories use a structured format — beginning, middle, and end — incorporating text, images, annotations, and graphics to make complex insights understandable and memorable. The video also explores how to tailor stories based on the nature of the insight, whether it's disruptive, costly, unpleasant, or surprising, and offers strategies for guiding audiences through these challenges. Finally, it emphasizes refining the story for the intended audience, sketching visualizations, and practicing delivery to ensure clarity and impact.

Key Terms

• Annotations: Explanatory notes added to a visualization or text to provide additional context or highlight specific points. • Callouts: Visual elements used to draw attention to particular parts of a visualization or text, often with accompanying descriptive text. • Data Storytelling: The process of creating a cohesive narrative around data insights, using visuals, text, and narrative structure to persuade an audience to take a specific course of action. It focuses on setting up and revealing key findings quickly and memorably.

• Data Visualization: The graphical representation of information and data, using visual elements like charts, graphs, and maps to help audiences understand complex data. • Disruptive Insight: A data finding that challenges existing traditions, practices, or beliefs within an organization, requiring the audience to break from established norms. • Insight: A key finding or understanding derived from data analysis that offers new knowledge or reveals a hidden pattern, often forming the core message of a data story. • Linear Sequence: A progression of information presented in a step-by step or chronological order, typically with a clear beginning, middle, and end. • Narrative: The coherent, structured sequence of events or information presented in a story, designed to guide the audience through an understanding of the data. • Return on Investment (ROI): A performance measure used to evaluate the efficiency or profitability of an investment or to compare the efficiency of several different investments. In data storytelling, it demonstrates the value of a high-cost solution. • Stakeholders: Individuals, groups, or organizations who have an interest or concern in a particular project or decision, and who are the target audience for a data story.

Closing

This guide and these reflection questions are just the beginning! Keep learning, keep growing and keep developing your skills.

Additional Resources

Data Analytics Competency Resource Guide

Data Analytics Competency Resource Guide

Other Resources

Additional Resources

Individual Development Plan Template

Individual Development Plan Template

i4cp's Entire People Analytics Knowledge Center

i4cp's Entire People Analytics Knowledge Center

Feedback? Typos? Broken Links? Suggestions? Praise? Let us know!