Data Literacy in Practice

Data Literacy in Practice

A Complete Guide To Data Literacy And Making Smarter Decisions With Data Through Intelligent Actions

Available on Amazon

by Angelika Klidas and Kevin Hanegan

Accelerate your journey to smarter decisions by mastering the fundamentals of Data Literacy and the mindset to work confidently with data.


Key Takeaways


Data is more than a commodity in our digital world. It is the ebb and flow of our modern existence. Individuals, teams and enterprises that work confidently with data, unlock a new realm of possibilities. And the resultant agility, growth and inevitable success have one origin. Data Literacy.

Data Literacy in Practice is a comprehensive guide that will build your understanding of data literacy basics, and accelerate your journey to independently uncovering insights with best practices, practical models and real-world examples.

Discover the 4 pillar model that underpins all data and analytics. Explore concepts such as measuring Data Quality, setting up a pragmatic Data Management environment, choosing the right graphs for your readers, and questioning your insights.

This guide is written by two Data Literacy pioneers, each with a thorough footprint within the Data and Analytics commercial world and their lectures at top Universities in the US and the Netherlands.

By the end of the book, you'll be equipped with a combination of skills and mindsets, along with tools and frameworks, that allows you to find insights and meaning within your data to enable effective, efficient data informed decision-making.

What you will learn

Who This Book Is For

This book is for data analysts, data professionals and data teams starting or wanting to accelerate their data literacy journey. Discover the skills and mindset you need to work independently with data, along with the tools and frameworks to build a solid knowledge base. And start making your data work for you today.


Chapter 1, The Beginning- The Flow of Data, The process of going from data to insights and action is a multi-step process. Understanding this process is critical for anyone who is leveraging data to make decisions. This chapter will introduce the flow of data through this process, as well as common pitfalls that can get in the way at each step.

Chapter 2, Unfolding Your Data Journey, To be able to properly turn data into actionable insights, individuals need to be able to leverage multiple steps in the analytics maturity: descriptive, diagnostic, predictive, prescriptive & semantic. This chapter will introduce those steps with practical examples of what insights you can get from each step in the process.

Chapter 3, Understanding the Four Pillar Model, Everybody knows and understands what data or a dashboard is. From that point of view we see more demand & acceptance for Data & Analytics projects and the need for Data Literacy knowledge. When we dig into some explicit points for 2022 there are four elementary pillars in Data & Analytics that we need to address in our businesses. Those 4 pillars are.

Chapter 4, Implementing Organizational Data Literacy, For individuals and organizations to be able to elicit insights and value from their data, there needs to be widespread adoption of data-informed decision making. Despite many organizations having tools and technologies and technical abilities, they are many times unable to become data-informed due to their lack of a data literacy culture. This chapter will focus on best practices related to organizational strategy and culture to support data literacy and data-informed decision making.

Chapter 5, Managing your Data Environment, Low-code/no-code solutions are maturing in an interesting way, giving all benefits to their users in building rapid data lakes, data warehouses, data pipe-lines. If we compare this technology against the more traditional solutions we notice that we are able to get a better “race pace’ in developing a Data & Analytics fundament. Due to the enormous growth (1.7MB of data is created every second for every person on the earth) and complexity of data and data environments, a good and solid data strategy and taking care of a shared data vision was never as important as its now. But in the last two- years there is a shift occurring and the necessity of a managed data environment became more important.

Chapter 6, Aligning with Organizational Goals, Key Performance Indicators (KPIs) are extremely vital in helping organizations understand how well they are performing in relation to their strategic goals and objectives. However, understanding what is truly a KPI versus what is just a measurement or a metric is important, along with understanding the right types of KPIs to track, including leading and lagging indicators.

Chapter 7, Designing Dashboards and Reports, Visualizations provide a vital function in helping describe situations. Visualizations can be used for both finding insights and also for communicating those visualization to others. Choosing the right visualization depends on both the data you are using and what you are trying to show. This chapter will focus on choosing the right chart type, as well as designing charts to make it easier for people to interpret relevant parts.

Chapter 8, Questioning the Data, Here it’s about learning to ask questions, analyze outliers (supporting story by dr. snow - death in the pit), exclude bias etc. so that you will be able to ask the right questions and develop your curiosity. Understand the difference between correlation and causation. By addressing those topics you will be able to understand what signal and noise is, and how to analyze the outliers by addressing hypothetical questions (what if for example). Next to that you will be able to recognize the good, the bad and ugly insights.

Chapter 9, Handling Your Data Responsibly, Ethics is a science in which people try to qualify certain actions as right or wrong. However, there are no unequivocal answers to ethical questions because they are often very personal. Today, data & analytics is everywhere, touching every waking moment of our lives. Data & Analytics therefore play a enormous role in our daily lives, like amazon who knows what we buy and suggest other articles that we may be interested in, or applications that show us how we will look when we are at a higher age, or Netflix and Spotify that exactly know what we listen or look and even give us suggestions what to look or listen for.

Chapter 10, Turning Insights into Decisions, Many individuals and organizations can come up with insights from their data. However, the process of turning insights into decisions and acting on them is much more difficult. This chapter focuses on what is required to support this step in the process, including introducing a 6-step framework, which is both systemic and systematic. The chapter also includes how you can manage the change related to your decisions and how you can communicate effectively to all stakeholders via storytelling with data.

Chapter 11, Defining a Data Literacy Competency Framework, The first step to increasing your own data literacy via education is to learn what exactly are the competencies that support data literacy. This chapter describes a Data Literacy competency framework, which includes the right hard skills, soft skills, and mindsets for data literacy. It also discusses how competencies have various levels, and readers can progress up the levels as they become more mature with data literacy. This chapter also focuses on best practices for how to get started learning these competencies.

Chapter 12, Assessing Your Data Literacy Maturity, Before you begin your educational journey for data literacy, you should start by assessing your current level, and then using that assessment to understand what competencies to focus on next. This chapter will introduce how you can assess your own data literacy skills and then how to interpret the results of the assessment to personalize your educational journey.

Chapter 13, Managing Data and Analytics Projects, It all starts with the development of a Data & Analytics Business Case in which you define the project scope, goals, risks but also the beneficial value that it can bring for your organization. Data & Analytics projects are often cross organizations, departments, processes of business units. They mostly contain a mix of strategic goals or have a high political content and hidden stakeholders and have specific Data & Analytics risks that you should take care of. In this chapter we will explain the way you cloud approach a Data & Analytics project and how you can manage it as a project leader and keep track of the business case and the value that it can bring.

Appendix A- Templates, Materials that we provide to help you get started with the Data Literacy journey.

Appendix B- References, This is a summary of the references, books and articles that we've read over the years. All of them inspired us and helped us to teach and write about it.