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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
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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

Unfolding the data and analytics project framework

As stated earlier, there are two elementary sides to the advised project approach:

  • Traditional “Waterfall” and the incremental/iterative/Scrum/Agile way
  • A “multi-disciplinary” project team

Those two elements are so critical that a project will almost certainly fail if they are not present. Intermezzo 2 contains a story about how to be successful with a specific project approach. And there is still a lot written about this topic by many experts; best practices are good ideas that you can tweak to fit within your organization.

Intermezzo 2 – successfully managing a data and analytics project

At a relatively large organization, a couple of years ago, we started out with a new team and centralized our data and analytics projects. We knew from the start (written in an implementation plan) that data and analytics projects needed a specific approach. The way in which the team was...

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