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Data Literacy in Practice

You're reading from   Data Literacy in Practice A complete guide to data literacy and making smarter decisions with data through intelligent actions

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781803246758
Length 396 pages
Edition 1st Edition
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Authors (2):
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Kevin Hanegan Kevin Hanegan
Author Profile Icon Kevin Hanegan
Kevin Hanegan
Angelika Klidas Angelika Klidas
Author Profile Icon Angelika Klidas
Angelika Klidas
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Understanding the Data Literacy Concepts
2. Chapter 1: The Beginning – The Flow of Data FREE CHAPTER 3. Chapter 2: Unfolding Your Data Journey 4. Chapter 3: Understanding the Four-Pillar Model 5. Chapter 4: Implementing Organizational Data Literacy 6. Chapter 5: Managing Your Data Environment 7. Part 2: Understanding How to Measure the Why, What, and How
8. Chapter 6: Aligning with Organizational Goals 9. Chapter 7: Designing Dashboards and Reports 10. Chapter 8: Questioning the Data 11. Chapter 9: Handling Data Responsibly 12. Part 3: Understanding the Change and How to Assess Activities
13. Chapter 10: Turning Insights into Decisions 14. Chapter 11: Defining a Data Literacy Competency Framework 15. Chapter 12: Assessing Your Data Literacy Maturity 16. Chapter 13: Managing Data and Analytics Projects 17. Chapter 14: Appendix A – Templates 18. Chapter 15: Appendix B – References 19. Index 20. Other Books You May Enjoy

Understanding diagnostic analysis

The exciting part of going on that data journey is when your curiosity kicks in and you start asking questions such as why? This means you are already one step further along your journey. In his book Turning Data Into Wisdom, Kevin Hanegan (coauthor of this book) describes the step for this approach as exploratory analysis. All of this implies that you want to find out why things happened.

We can see that curiosity has arisen, and we are eager to learn what has caused the differences in figures and numbers. We begin by asking questions such as the following:

  • Why are the numbers the way they are now? Why are sales this month lower (or higher) than last month?
  • Why did department X convert the most while department Y lagged?
  • Why did we sell fewer products C this month than last month?
  • What are the causes of these statistics, and why is there more absenteeism due to illness?
  • Why is department X’s absenteeism higher than...
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