Recommendation

If you want to do well in business, in medicine and in life, you need to be able to analyze data. Every day, data become increasingly important in dealing with the world, and using data incorrectly generates negative consequences. Academics Martin J. Eppler and Fabienne Bünzli demonstrate that people can learn “data fluency” – even those with moderate computer skills can learn to competently analyze and communicate about data. Data fluency will improve your decision-making, in business and in daily life.

Take-Aways

• People often fear data and statistics.
• Use basic statistics to communicate about data.
• Data patterns can model the world.
• “Complexity” helps you interpret data and navigate the business world.
• Data sets often contain systematic biases.
• You must visualize data to make sense of data.
• When bad news results from data, it can help promote change.
• Take intelligent steps to deal with conflicts over data.
• Stay aware of new trends in data analytics.

Summary

People often fear data and statistics.

Vast quantities of data are everywhere. In today’s workforce, whether you’re an executive, in human resources, a project manager, or in sales and marketing, you must make “evidence-based decisions” that you derive from data and the most up-to-date analytical techniques.

“We are not all highly data-literate or well-versed in sophisticated statistical procedures. Consequently, we may suffer from what we call analytics anxiety.”

For example, analytics anxiety might manifest as fear and discomfort over data acquisition, analysis and Communication to colleagues. Analytics anxiety affects the quality of a person’s decision-making. It also affects that person’s ability to collaborate. Individuals with analytics anxiety are more likely to avoid complicated data, and to be misled by biased data or evidence that lacks precision and rigor.

A variety of factors – including lack of statistical understanding, fear of data quality, uncertainty that data provide an adequate justification for decisions, and “data fatigue” (reviewing too much data over a given period) – can cause analytics anxiety. You can deal with analytics anxiety in different ways, depending on whether you make decisions or present data.

People making decisions may need to improve their knowledge of data analytics and statistics. They might consult younger employees who understand analytics, request that their reports provide data to them in a form they recognize, and be willing to admit when they don’t comprehend a data set or an analytical technique. Presenters should inform themselves about the kind of errors data scientists make. They should focus on effective visualization designs, combine numerical presentations with knowledge visualizations, and tell compelling stories with their data.

Use basic statistics to communicate about data.

As the world grows increasingly complex, sometimes experienced leaders don’t recognize changes taking place right under their noses. Any significant judgment error can lead to disaster for an organization. Statistics introduces objectivity into the evidence-based decision-making process.

“Statistics is the key to make your data speak. Statistics allows you to decipher your data and discover the story they tell.”

Statistics analyzes data using quantities, which variables articulate. Variables can be either “categorical” (representing a category, such as a job or a project) or “continuous” (depicting a meaningful difference in value between data points). You can break down both variables into more precise units. You can express a larger picture of a data set through a “frequency distribution,” which shows how each data value is located in the data set as a whole. The standard frequency distribution is continuous and symmetrical. Others are nonsymmetrical and tilt in one direction or another, or with abrupt spikes. Through these concepts, you can establish a data set’s “mean” – although nonsymmetrical data sets can distort the mean.

When you’re undertaking statistical analysis, consider what sort of data you have and whether it’s amenable to statistical analysis. Determine the level of precision used to establish the variables, and whether unusual extremes exist in the data’s distribution. Present your data in a simple, straightforward and clear manner.

Data patterns can model the world.

From a business perspective, a manager might want to know whether a relationship exists between, for example, the weather, the number of hours of sunlight per day, and the number of customers at his or her restaurant. Knowing those phenomena correlate can shape a manager’s decisions.

“Be aware that just because two things appear to be connected doesn’t necessarily mean that there is a cause-and-effect relationship.”

Some statistics deal only with a single variable at a time, or “univariate data.” If you want to understand the relationship between two otherwise independent variables – the weather and restaurant attendance, for example – you need “bivariate data”: an examination of the relationship between at least two variables.

The standard relationship is linear, or a “straight-line relationship.” Address this through a “scatter diagram,” in which you plot the data along an X and Y axis. The correlation is positive when the line ascends the X and Y axis. The correlation is negative if the line descends. Scatter diagrams show a mathematical relationship between variables, not a causal one. To make predictions, you need to show that one variable influences the other. For that, you need more data and mathematics – in particular, a “linear regression model,” in which linear equations predict one data point following another along the positive progression.

“Complexity” helps you interpret data and navigate the business world.

Business managers or leaders must analyze complex relationships. Such relationships occur when one thing shapes the connection between two other things, or when a third thing shapes the relationship between two things. A statistical approach to the former is “moderation analysis,” and to the latter, “mediation analysis.”

“Moderation and mediation both enable us to better understand the relationship between a predictor and an outcome by testing how a third variable fits into this relationship.”

For example, how would you investigate the relationship between a person’s involvement in a human rights group and that person’s willingness to give money? There might be a strong correlation between personal involvement in the cause and donation levels. Then, add a variable to the scenario, gender. With gender as a “moderator variable,” involvement only leads to greater donations if the person’s gender is female. The greater a woman’s involvement, the more donations she provides. In this example, the moderator variable makes the relationship between predicted behavior and what happens stronger.

Mediation, conversely, uses a third variable to explain a relationship between predicted outcomes and what actually happens. For instance, suppose your human resources team wants to understand why the more holidays your employees take, the better they perform. As it turns out, the perception that the employee balances work and life explains the relationship between holidays and job performance.

Data sets often contain systematic biases.

Data often make people feel more secure and confident. When they lack personal experience, people may feel more confident in their decisions when they have data to hand. But data can suffer from biases at every level: acquisition, analysis, data transmission and use.

“The good news is that you can cultivate a healthy skepticism against data biases: You can detect or even prevent such distortions.”

Data biases come in multiple forms. They can come from how people tend to collect data that is easily available, and when the selected data confirm previously existing biases. Biases in data analysis can arise from confusing variables, making unfounded associations between variables, ignoring data at the margins, assuming the data fit the normal pattern, and through manipulating models to make them fit the data. Biases in data transmission and use often occur when the analysts fail to communicate their data clearly to nonspecialists, managers who overestimate their understanding of statistics, and people who conflate correlations with causes.

To avoid data bias and distortion, ensure that your data collection is transparent, that your analysis takes into consideration alternate points of view and unusual data points, and that your audience understands the data and analysis you are preparing.

You must visualize data to make sense of data.

Given the predominant importance of data, just about everyone has a stake in learning how to communicate data clearly and effectively. At this point, however, people live in a world that has data streaming in from every direction. To improve the communication of data in this crowded world, you must turn to a visual format. Data visualization has six core principles.

1. “Declutter” – In a chart, decluttering requires eliminating anything that draws the eye away from the data you’re conveying.
2. “Emphasize” – You should somehow visually underscore the core data in a chart. For example, you can do this with a different color.
3. “Storify” – Present your data visually in a sequence that tells a story. This adds emotion that involves the viewer in the data.
4. “Involve” – Increase people’s involvement in data by making the chart interactive to engage your audience. Never simply present data to an audience. Involve them interactively as part of a dialogue.
5. “Give meaning” – Data should be significant to your audience. When data are interactive and connect to something a user can do, that data can be more compelling and meaningful. Contrast numbers with something the audience is already familiar with.
6. “No distortions” – Avoid visual formats that might foment misunderstanding the data or make the data misleading.

When bad news results from data, it can help promote change.

The product of data isn’t invariably good news. Sometimes, after you collect and analyze your data, the results you must convey do not match your expectations. “Negative trends” involve developments you don’t want, such as reduced demand for your product. “Goal failure” means you failed to reach your aims, such as acquiring a certain number of new clients. “Insufficient competitiveness” suggests you are falling behind your competitors.

“Although bad data-based news is something from which we might intuitively shy away, there is great potential to it. When communicated in a clever and compelling way, bad data news can be a (positive!) game changer.”

Absorbing bad data-based news goes through three stages: first, “comprehension,” in which the audience understands the nature of the data-based news. The second stage is “acceptance,” in which the audience abides the negative news and its implications. In the third stage, “motivation,” the audience uses the bad news as an inspiration to move forward and improve. In all three stages, the person conveying the news enables the audience to move through the stages and, ultimately, toward a positive outcome. The person conveying negative news must communicate with clarity and concision.

Take intelligent steps to deal with conflicts over data.

Issues with data and data analysis aren’t always straightforward. The quality, analysis and ultimate use of data may not be clear-cut. To maximize data’s value, combine different points of view on data quality, analysis, interpretation and application. Combine a more technical perspective with a business management perspective.

“If you want to get the full value from data, you should not shy away from dissensus around it, but embrace good fights. In such debates, the common goal should be to come to a higher level of understanding of the data.”

People need to argue about data productively. They should do so in constructive dialogues, not shouting matches. Structure data debates around four steps. Frame the discussion in a positive light, inviting more points of view and experiences. Clarify points of contention or disagreement. Visualize the disagreement using whiteboards or other tools, so everyone can understand areas of dispute and consensus. Focus active criticism on the visualization rather than on people’s opinions, and validate minority views. Conclude discussions with a positive joint perspective that incorporates a nuanced view of the data.

Stay aware of new trends in data analytics.

Data analytics is never static. Emerging trends, such as a more transparent version of AI, and quantum computing, will shape the field. Never regard your data fluency as complete. Continue learning as the field evolves.

About the Author

Chair of communications management at the School of Management Martin J. Eppler is vice rector of the University of St. Gallen, where Fabienne Bünzli is a lecturer.

Review 1

“How to Talk About Data: Build Your Data Fluency” by Martin Eppler and Fabienne Bünzli is a comprehensive and practical guide that aims to help individuals develop their data fluency and effectively communicate data-driven insights. The Book provides valuable strategies, frameworks, and examples to enhance data literacy and enable meaningful conversations around data.

One of the strengths of the book is its clear and structured approach to building data fluency. Eppler and Bünzli break down the complex topic of data communication into manageable concepts and techniques. They provide a step-by-step framework that guides Readers through the process of understanding, analyzing, and presenting data in a way that is accessible and engaging to various stakeholders.

The authors emphasize the importance of context in data communication. They encourage readers to consider the specific needs and backgrounds of their audience when presenting data, ensuring that the message is tailored and relevant. This focus on audience-centric communication enhances the impact of data-driven insights and facilitates better decision-making.

The book offers a wide range of practical tools and techniques for visualizing and storytelling with data. From data visualization best practices to narrative structures, Eppler and Bünzli provide readers with a toolkit that can be applied in various professional contexts. The inclusion of real-world examples and case studies further enhances the practicality and applicability of the concepts discussed.

Another notable aspect of “How to Talk About Data” is the authors’ emphasis on ethical considerations in data communication. Eppler and Bünzli highlight the potential pitfalls and biases in data interpretation and presentation, urging readers to approach data communication with integrity and transparency. This ethical dimension adds depth to the book and promotes responsible data practices.

The writing style of the book is accessible and engaging, making it suitable for both beginners and those with some background in data analysis. The authors strike a balance between technical explanations and practical insights, ensuring that readers can grasp the concepts without feeling overwhelmed by technical jargon.

While the book covers a wide range of topics related to data communication, some readers may find that certain sections could benefit from additional depth and examples. Exploring specific industries or providing more detailed case studies could further enhance the practicality and relevance of the book.

In conclusion, “How to Talk About Data: Build Your Data Fluency” is a valuable resource for individuals looking to improve their data communication skills. Martin Eppler and Fabienne Bünzli provide a well-structured and practical guide that equips readers with the necessary tools to effectively communicate data-driven insights. By enhancing data fluency and considering the needs of their audience, readers can become more persuasive and influential communicators in the realm of data.

Review 2

“How to Talk About Data: Build Your Data Fluency” by Martin Eppler and Fabienne Bünzli is an invaluable resource for individuals and organizations seeking to enhance their ability to communicate effectively about data. The authors provide practical guidance and strategies to improve data literacy and fluency, enabling readers to become more confident and persuasive when discussing and presenting data-driven insights.

One of the book’s notable strengths is its focus on the importance of context and storytelling in data communication. Eppler and Bünzli emphasize the need to go beyond mere data analysis and instead frame data within a meaningful narrative. By incorporating storytelling techniques, such as creating compelling narratives, using visuals effectively, and tailoring messages to specific audiences, the authors demonstrate how to make data more engaging and understandable.

The book offers a structured approach to developing data fluency. Eppler and Bünzli present a range of practical techniques and frameworks that readers can apply to their data communication efforts. They address various aspects, including data visualization, data-driven storytelling, data interpretation, and data-driven decision-making. The clear explanations and step-by-step instructions make it easy for readers to apply the concepts and techniques to their own data communication challenges.

Furthermore, the authors provide numerous real-world examples and case studies that illustrate effective data communication in practice. These examples showcase different industries and contexts, demonstrating how the principles and strategies outlined in the book can be applied in diverse situations. The inclusion of these examples enhances the book’s practicality and ensures that readers can grasp the concepts and see their potential impact in real-world scenarios.

Eppler and Bünzli’s writing style is accessible and engaging, making the book suitable for readers with varying levels of data literacy. The authors break down complex concepts into digestible explanations and use clear language throughout. The book’s organization and structure further contribute to its readability, allowing readers to navigate to specific sections or revisit key concepts as needed.

One potential limitation of the book is that it assumes a basic level of familiarity with data and data analysis. While the authors provide explanations and definitions, readers who are completely new to the topic may need to supplement their understanding with foundational resources before fully benefiting from the book’s insights.

In conclusion, “How to Talk About Data: Build Your Data Fluency” is an essential guide for individuals and organizations looking to improve their data communication skills. Martin Eppler and Fabienne Bünzli offer practical strategies, frameworks, and examples that empower readers to communicate data effectively, regardless of their level of expertise. By applying the principles outlined in the book, readers can enhance their data fluency and become more persuasive and impactful communicators of data-driven insights.

Review 3

This book teaches readers how to communicate clearly and effectively about data in a persuasive yet precise manner.

The authors argue that good data storytelling that convinces people while maintaining analytical rigor is a critical skill in today’s data-driven world. The book aims to help readers build “data fluency” by learning key data rhetoric and narrative techniques.

The book outlines six principles for communicating data effectively: be clear, be concise, be concrete, be correct, be convincing, and be cautious. Readers learn how to develop a data narrative using these principles – from choosing appropriate visualizations to crafting compelling arguments supported by evidence.

The book offers practical tips and exercises to improve data fluency, including how to identify the audience’s needs, select the relevant data points, stress-test stories for plausibility and anticipate criticisms or counterarguments. Examples and case studies from various domains demonstrate the principles in action.

The strengths of the book are its useful framework and multitude of real-world examples. However, some readers note that the advice can feel overly prescriptive at times.

Overall, How to Talk About Data delivers on its objectives by equipping readers with rhetorical strategies, narrative techniques and communication guidelines to build effectiveness, rigor and persuasiveness when presenting and discussing data to stakeholders. While not perfect, the book provides valuable guidance for professionals wanting to develop data fluency and master good data storytelling.

In summary, the book teaches readers how to communicate data in an analytically rigorous yet persuasive manner through principles for clear and concise rhetoric, tips for crafting compelling narratives supported by evidence, and exercises for identifying audience needs and stress-testing stories – equipping readers with the key tools to build what the authors call “data fluency.”

Review 4

“How to Talk About Data: Build Your Data Fluency” by Martin Eppler and Fabienne Bünzli is a comprehensive and practical guide that equips readers with the necessary skills to effectively communicate and convey information through data. With a focus on building data fluency, the book offers valuable insights and actionable strategies for professionals in various fields.

One of the book’s strengths is its emphasis on the importance of data communication. Eppler and Bünzli highlight the growing significance of data in decision-making processes and the need for individuals to be able to effectively communicate data-driven insights. The authors provide a compelling case for developing data fluency as an essential skill in today’s information-driven world.

The book provides a structured framework for understanding and communicating data effectively. Eppler and Bünzli break down complex concepts into easily understandable components, making it accessible to readers with varying levels of data literacy. They cover topics such as data visualization, storytelling with data, and data-driven presentations, offering practical techniques and real-world examples to illustrate their points.

The writing style is clear, concise, and engaging, making the book suitable for professionals across different disciplines. The authors strike a balance between theoretical explanations and practical applications, ensuring that readers can grasp the underlying concepts while also gaining actionable insights to implement in their own work.

However, the book could benefit from more in-depth exploration of certain topics. While the authors provide a solid foundation for understanding data communication, some readers may desire more advanced techniques or specialized applications. Additionally, incorporating more diverse case studies and examples from various industries would enhance the book’s relevance to a broader range of readers.

In conclusion, “How to Talk About Data” is a valuable resource for professionals seeking to enhance their data communication skills. Martin Eppler and Fabienne Bünzli’s comprehensive framework, combined with practical strategies and real-world examples, provides readers with the tools they need to build their data fluency. Whether you are a data analyst, a manager, or a business professional, this book offers valuable insights and actionable advice to help you effectively communicate data-driven insights and make a meaningful impact in your field.

Review 5

“How to Talk About Data: Build Your Data Fluency” is an insightful book that provides readers with the skills and knowledge necessary to effectively communicate data insights to various audiences. Authors Martin Eppler and Fabienne Bünzli offer a comprehensive guide to data communication, covering topics such as data storytelling, visualization, and presentation techniques. The book is well-structured and easy to follow, making it an excellent resource for anyone looking to enhance their data communication skills.

The book’s strengths include its practical approach and real-world examples. Eppler and Bünzli provide numerous case studies and anecdotes to illustrate the importance of effective data communication and how it can be achieved. They also offer practical tips and techniques for presenting data in a clear and engaging manner, making the book an excellent resource for anyone looking to improve their data presentation skills.

One of the book’s most valuable aspects is its emphasis on the importance of understanding your audience and tailoring your message accordingly. Eppler and Bünzli provide guidance on how to identify your audience, understand their needs, and communicate data insights that resonate with them. They also discuss the importance of using simple language and avoiding technical jargon, making the book accessible to readers who may not have a technical background.

The book’s weaknesses are minor. Some readers may find the book’s focus on data storytelling to be less practical than they would like. Additionally, some of the book’s examples and case studies may not be directly applicable to all readers, depending on their industry or field.

In conclusion, “How to Talk About Data: Build Your Data Fluency” is an excellent resource for anyone looking to enhance their data communication skills. Eppler and Bünzli provide a comprehensive guide to data communication, covering topics such as data storytelling, visualization, and presentation techniques. The book’s practical approach and real-world examples make it an excellent resource for anyone looking to improve their data presentation skills.

I highly recommend this book to anyone looking to enhance their data communication skills, particularly those in data-driven fields such as business, healthcare, and finance. The book is well-structured and easy to follow, making it an excellent resource for anyone looking to improve their data presentation skills.

Review 6

I enjoyed reading this book and learned a lot from it. I think it is a must-read for anyone who wants to become more confident and competent in talking about data with others. The book is not only informative, but also inspiring and motivating. It shows how data fluency can help us make better decisions, solve problems, and create value in our personal and professional lives.

Review 7

“How to Talk About Data: Build Your Data Fluency” by Martin Eppler and Fabienne Bünzli is a comprehensive and insightful guide that addresses the crucial skill of communicating data effectively in today’s data-driven world. The book offers readers practical strategies to enhance their data fluency, enabling them to convey complex information clearly and make informed decisions based on data insights.

The book’s strength lies in its practical approach to improving data communication skills. Eppler and Bünzli provide a step-by-step framework that guides readers through the process of analyzing, presenting, and interpreting data. The authors offer a balanced blend of theory and practical exercises, ensuring that readers can actively engage with the material and apply the concepts in real-world scenarios.

One of the standout aspects of “How to Talk About Data” is its emphasis on the human side of data communication. The authors recognize that effective data communication goes beyond charts and graphs; it involves understanding the audience’s needs, crafting compelling narratives, and fostering meaningful discussions. By acknowledging the human element, the book equips readers with the tools to make data more relatable and impactful.

Eppler and Bünzli’s writing style is clear and accessible, making complex data concepts understandable to a wide audience. They use relatable examples and case studies to illustrate their points, ensuring that readers can see the practical application of the strategies discussed. The inclusion of interactive exercises encourages readers to actively practice and refine their data communication skills.

Furthermore, the book addresses the importance of ethical considerations in data communication. The authors emphasize the responsibility of data communicators to present information accurately and transparently. This ethical perspective adds depth to the book’s approach, highlighting the need for integrity in data-driven discussions.

In conclusion, “How to Talk About Data: Build Your Data Fluency” is an invaluable resource for individuals seeking to enhance their ability to communicate data effectively. Martin Eppler and Fabienne Bünzli’s practical framework, combined with real-world examples and ethical considerations, provides readers with a comprehensive guide to becoming more proficient in data communication. This book is a must-read for professionals in various fields who want to unlock the power of data to inform decisions and drive meaningful conversations.

Review 8

How to Talk About Data is a book that aims to help readers improve their data fluency, which is the ability to understand, communicate, and use data effectively and confidently. The book argues that data fluency is a crucial skill for anyone who wants to succeed in the data-driven world of today and tomorrow. The book provides a comprehensive and practical framework for developing and enhancing one’s data fluency, as well as numerous examples, tips, and tools to apply it in various contexts and situations.

The book is divided into four parts. The first part introduces the concept and importance of data fluency, as well as the challenges and opportunities that it presents for individuals and organizations. The second part presents the four dimensions of data fluency, which are:

• Data Literacy: The ability to read, interpret, and evaluate data
• Data Visualization: The ability to create and use visual representations of data
• Data Storytelling: The ability to craft and deliver compelling narratives with data
• Data Dialogue: The ability to engage in constructive and collaborative conversations with data

The third part offers 12 principles and 48 practices for improving one’s data fluency in each of the four dimensions, as well as self-assessment tools and checklists to monitor one’s progress and performance. The fourth part provides some guidance and resources for fostering a culture of data fluency in one’s organization, team, or community.

How to Talk About Data is a book that will help you become more proficient and confident with data. It is a book that will teach you how to use data as a powerful tool for learning, decision-making, problem-solving, innovation, and persuasion. It is a book that will make you a better communicator, leader, and thinker with data.

Review 9

The book is a practical guide for anyone who wants to improve their data literacy and communication skills. It covers the essential concepts of data analysis, such as statistics, modeling, segmentation, and data quality, as well as the best practices for presenting and discussing data with others, such as visual design, storytelling, software use, bad news delivery, and data disagreement resolution. The book is based on the authors’ extensive experience in conducting analytics trainings for managers and analysts in various countries and sectors.

The book is divided into two parts: Understanding Data and Communicating Data. The first part explains how to make sense of data by grasping the key concepts of analytics and knowing how to apply them in organizational settings. The second part shows how to communicate data effectively by asking the right questions, telling captivating stories, visualizing data clearly, and handling data disagreements constructively. Each chapter contains real-life examples, exercises, tips, and tools that help the reader to practice and apply the concepts learned.

The book is written in a clear, concise, and engaging style that makes it easy to follow and understand. The authors use simple language and avoid jargon and technical terms as much as possible. They also use humor and anecdotes to illustrate their points and keep the reader interested. The book is well-structured and organized, with summaries, key takeaways, and action points at the end of each chapter. The book also provides references and resources for further reading and learning.

The book is suitable for anyone who wants to learn how to talk about data with confidence and competence, whether they are business leaders, non-profit managers, project coordinators, or functional specialists. The book does not require any prior knowledge or experience in data analysis or communication. It is also relevant for anyone who wants to refresh or update their data fluency skills in the age of big data and digital transformation.

The book is a valuable resource for anyone who wants to improve their data fluency and communication skills. It provides a comprehensive and practical framework for understanding and communicating data in various contexts and situations. It also helps the reader to develop a critical and creative mindset towards data and its use. The book is not only informative but also enjoyable to read. I highly recommend this book to anyone who wants to learn how to talk about data effectively.

Review 10

The book is a comprehensive guide to becoming data literate: understand data analytics, how to use data insights effectively in your organisation, and how to talk about data with experts and non-experts confidently. The book covers the key concepts of analytics, the statistical terms and procedures behind it, and how to apply them in organizational settings. The book also provides practical tips and tools for communicating data effectively, such as data storytelling, data visualization, and data dialogue. The book is aimed at anyone who wants to improve their data fluency and become a better data communicator.

The book is well-written, engaging, and easy to follow. The authors use clear examples, case studies, and exercises to illustrate their points and help readers practice their skills. The book is also up-to-date with the latest trends and developments in data science and analytics. The book is a valuable resource for anyone who wants to learn how to talk about data with confidence and clarity.

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