Data Literacy in Practice

Data Literacy in Practice

by Angelika Klidas, Kevin Hanegan
Released November 2022
Publisher(s): Packt Publishing
ISBN: 9781803246758

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Book description

Accelerate your journey to smarter decision making by mastering the fundamentals of data literacy and developing the mindset to work confidently with data

Key Features

  • Get a solid grasp of data literacy fundamentals to support your next steps in your career
  • Learn how to work with data and extract meaningful insights to take the right actions
  • Apply your knowledge to real-world business intelligence projects

Book Description

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

This comprehensive guide is written by two data literacy pioneers, each with a thorough footprint within the data and analytics commercial world and lectures at top universities in the US and the Netherlands. Complete with best practices, practical models, and real-world examples, Data Literacy in Practice will help you start making your data work for you by building your understanding of data literacy basics and accelerating your journey to independently uncovering insights.

You’ll learn the four-pillar model that underpins all data and analytics and 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.

By the end of the book, you'll be equipped with a combination of skills and mindset as well as with tools and frameworks that will allow you to find insights and meaning within your data for data-informed decision making.

What you will learn

  • Start your data literacy journey with simple and actionable steps
  • Apply the four-pillar model for organizations to transform data into insights
  • Discover which skills you need to work confidently with data
  • Visualize data and create compelling visual data stories
  • Measure, improve, and leverage your data to meet organizational goals
  • Master the process of drawing insights, ask critical questions and action your insights
  • Discover the right steps to take when you analyze insights

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. If you’re looking to develop the skills and mindset you need to work independently with data, as well as a solid knowledge base of the tools and frameworks, you’ll find this book useful.

Table of contents

  1. Data Literacy in Practice
  2. Contributors
  3. About the authors
  4. About the reviewers
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. Conventions used
    4. Get in touch
    5. Share Your Thoughts
    6. Download a free PDF copy of this book
  6. Part 1: Understanding the Data Literacy Concepts
  7. Chapter 1: The Beginning – The Flow of Data
    1. Understanding data in our daily lives
    2. Analyzing data
      1. Searching and finding information
    3. An introduction to data literacy
      1. The COVID-19 pandemic
    4. The organizational data flow
      1. The DIDM journey
      2. The success story of The Oakland A’s
    5. Summary
  8. Chapter 2: Unfolding Your Data Journey
    1. Growing toward data and analytics maturity
    2. Descriptive analyses and the data path to maturity
      1. Understanding descriptive analysis
      2. Identifying qualitative or quantitative data
    3. Understanding diagnostic analysis
    4. Understanding predictive analytics
    5. Understanding prescriptive analytics
      1. AI
    6. Can data save lives? A success story
    7. Summary
  9. Chapter 3: Understanding the Four-Pillar Model
    1. Gaining an understanding of the various aspects of data literacy
    2. Introducing the four fundamental pillars
      1. Becoming acquainted with organizational data literacy
      2. Discussing the significance of data management
      3. Defining a data and analytics approach
      4. The rapid growth of our data world
      5. Tools
      6. The rise of ML and AI
      7. Moving to the cloud
      8. Data literacy is a key aspect of data and analytics
      9. Understanding the education pillar
    3. Mixing the pillars
    4. Summary
  10. Chapter 4: Implementing Organizational Data Literacy
    1. Implementing organizational data literacy
    2. Planning the data literacy vision
    3. Communicating the data literacy vision
      1. Focusing on desired outcomes
      2. Adopting a systemic perspective
      3. Getting everyone involved in the whole process
    4. Developing a data-literate culture
      1. Managing change
      2. Driving resilience
      3. Managing the organization’s skills and knowledge
    5. Creating a data literacy educational program
      1. Identifying employee roles
      2. Learning levels
      3. Covering all moments of need
      4. Learning methodologies
      5. Including all knowledge types
      6. Learning elements
      7. Organizing content
      8. Searching for content
    6. Measuring success
    7. Celebrating successes
    8. Summary
    9. Further reading
  11. Chapter 5: Managing Your Data Environment
    1. Introducing data management
    2. Understanding your data quality
      1. Intermezzo – Starting to improve data quality in a small-scaled healthcare environment
    3. Delivering a data management future
      1. Data strategy
    4. Taking care of your data strategy
      1. Creating a data vision
      2. Identifying your data
      3. Discovering where your data is stored
      4. Retrieving your data
      5. Combining and enriching data
      6. Setting the standard
      7. Processes
      8. Control
      9. IT
    5. Summary
  12. Part 2: Understanding How to Measure the Why, What, and How
  13. Chapter 6: Aligning with Organizational Goals
    1. Understanding the types of indicators
    2. Identifying KPIs
      1. Characteristics of KPIs
      2. Leading and lagging indicators
    3. Reviewing for unintended consequences
      1. Applying Goodhart’s law to KPIs
    4. Defining what to track
      1. Activity system maps
      2. Logic models
    5. Summary
    6. References
  14. Chapter 7: Designing Dashboards and Reports
    1. The importance of visualizing data
    2. Deceiving with bad visualizations
    3. Using our eyes and the usage of colors
    4. Introducing the DAR(S) principle
      1. Defining your dashboard
    5. Choosing the right visualization
    6. Understanding some basic visualizations
      1. Bar chart (or column chart or bar graph)
      2. Line chart
      3. Pie chart
      4. Heatmap
      5. Radar chart
      6. Geospatial charts
      7. KPIs in various ways
      8. Tables
    7. Presenting some advanced visualizations
      1. Bullet charts
      2. Addressing contextual analysis
    8. Summary
  15. Chapter 8: Questioning the Data
    1. Being curious and critical by asking questions
      1. Starting with the problem – not the data
      2. Identifying the right key performance indicators (KPIs) ahead of time
      3. Questioning not just the data, but also assumptions
      4. Using a questioning framework
    2. Questioning based on the decision-making stage
      1. Questioning data and information
      2. Questioning analytic interpretations and insights
    3. Summary
    4. References
  16. Chapter 9: Handling Data Responsibly
    1. Introducing the potential risks of data and analytics
    2. Identifying data security concerns
      1. Intermezzo – a data leak at an airplane carrier
    3. Identifying data privacy concerns
    4. Identifying data ethical concerns
      1. Intermezzo – tax office profiles ethnically
    5. Summary
  17. Part 3: Understanding the Change and How to Assess Activities
  18. Chapter 10: Turning Insights into Decisions
    1. Data-informed decision-making process
      1. Ask – Identifying problems and interpreting requirements
      2. Acquire – Understanding, acquiring, and preparing relevant data
      3. Analyze – Transforming data into insights
      4. Apply – Validating the insights
      5. Act – Transforming insights into decisions
      6. Announce – Communicating decisions with data
      7. Assess – Evaluating outcomes of a decision
      8. Making a data-Informed decision in action
      9. Using a data-informed decision checklist
      10. Why data-informed over data-driven?
    2. Storytelling
      1. Why is communicating with data so hard?
      2. Three key elements of communication
      3. Why include a narrative?
      4. The process
    3. Summary
    4. Further reading
  19. Chapter 11: Defining a Data Literacy Competency Framework
    1. Data literacy competency framework
      1. Identifying problems and interpreting requirements
      2. Understanding, acquiring, and preparing relevant data
      3. Turning data into insights
      4. Validating the insights
      5. Transforming insights into decisions
      6. Communicating decisions with data
      7. Evaluating the outcome of a decision
      8. Understanding data
    2. Data literacy skills
      1. Identifying data literacy technical skills
      2. Data literacy soft skills
      3. Data literacy mindsets
    3. Summary
    4. References
  20. Chapter 12: Assessing Your Data Literacy Maturity
    1. Assessing individual data literacy
    2. Assessing organizational data literacy
      1. Basic organizational data literacy assessment
      2. Robust organizational data literacy maturity assessment
    3. Summary
  21. Chapter 13: Managing Data and Analytics Projects
    1. Discovering why data and analytics projects fail
    2. Understanding four typical data and analytics project characteristics
    3. Understanding data and analytics project blockers
      1. Pitfalls in data and analytics projects
      2. Lack of expertise
      3. The technical architecture
      4. Time and money
    4. Unfolding the data and analytics project approach
    5. Unfolding the data and analytics project framework
      1. Intermezzo 2 – successfully managing a data and analytics project
    6. Mitigating typical data and analytics project risks
      1. Project risks
      2. Technical risks
      3. Cultural risks
      4. Content risks
    7. Determining roles in data and analytics projects 
(and teams)
    8. Managing data and analytics projects
    9. Writing a successful data and analytics business case
      1. A chapter layout for your business case
    10. Finding financial justification for your project
      1. Argumentation for one-time project costs
      2. Annual recurring costs
      3. Argumentation for annual recurring costs
      4. The quantitative benefits
      5. ROI
      6. Conclusion and advice
    11. Summary
  22. Chapter 14: Appendix A – Templates
    1. Project intake form
      1. STARR TEMPLATE
    2. Layout for a business case
    3. Layout for a business case scenario description
    4. A business case financial analysis
    5. Layout for a risk assessment
    6. Layout for a summary business case
    7. Layout information and measure plan
    8. Layout for a KPI description
    9. Table with the Inmon groups and a description of their roles
  23. Chapter 15: Appendix B – References
    1. Inspirational books
    2. Online articles and blogs
      1. Dutch articles and blogs
    3. Online tools
    4. Online sites
  24. Index
    1. Why subscribe?
  25. Other Books You May Enjoy
    1. Packt is searching for authors like you
    2. Share Your Thoughts
    3. Download a free PDF copy of this book

Product information

  • Title: Data Literacy in Practice
  • Author(s): Angelika Klidas, Kevin Hanegan
  • Release date: November 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781803246758