Data Literacy in Practice A Complete Guide to Data Literacy and Making Smarter Decisions with Data Through Intelligent Actions
Understanding data is a powerful skill. This book will help you build a sound understanding of data literacy basics and build your confidence to work with data every day. Guided by best practices and real-world examples, you'll master the skills to make smarter decisions with data, fast.
| Main Author: | |
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| Other Authors: | |
| Format: | eBook |
| Language: | Inglés |
| Published: |
Birmingham :
Packt Publishing, Limited
2022.
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| Edition: | 1st ed |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009752728406719 |
Table of Contents:
- Cover
- Copyright
- Contributors
- Table of Contents
- Preface
- Part 1: Understanding the Data Literacy Conceptss
- Chapter 1: The Beginning - The Flow of Data
- Understanding data in our daily lives
- Analyzing data
- Searching and finding information
- An introduction to data literacy
- The COVID-19 pandemic
- The organizational data flow
- The DIDM journey
- The success story of The Oakland A's
- Summary
- Chapter 2: Unfolding Your Data Journey
- Growing toward data and analytics maturity
- Descriptive analyses and the data path to maturity
- Understanding descriptive analysis
- Identifying qualitative or quantitative data
- Understanding diagnostic analysis
- Understanding predictive analytics
- Understanding prescriptive analytics
- AI
- Can data save lives? A success story
- Summary
- Chapter 3: Understanding the Four-Pillar Model
- Gaining an understanding of the various aspects of data literacy
- Introducing the four fundamental pillars
- Becoming acquainted with organizational data literacy
- Discussing the significance of data management
- Defining a data and analytics approach
- The rapid growth of our data world
- Tools
- The rise of ML and AI
- Moving to the cloud
- Data literacy is a key aspect of data and analytics
- Understanding the education pillar
- Mixing the pillars
- Summary
- Chapter 4: Implementing Organizational Data Literacy
- Implementing organizational data literacy
- Planning the data literacy vision
- Communicating the data literacy vision
- Focusing on desired outcomes
- Adopting a systemic perspective
- Getting everyone involved in the whole process
- Developing a data-literate culture
- Managing change
- Driving resilience
- Managing the organization's skills and knowledge
- Creating a data literacy educational program
- Identifying employee roles.
- Learning levels
- Covering all moments of need
- Learning methodologies
- Including all knowledge types
- Learning elements
- Organizing content
- Searching for content
- Measuring success
- Celebrating successes
- Summary
- Further reading
- Chapter 5: Managing Your Data Environment
- Introducing data management
- Understanding your data quality
- Intermezzo - Starting to improve data quality in a small-scaled healthcare environment
- Delivering a data management future
- Data strategy
- Taking care of your data strategy
- Creating a data vision
- Identifying your data
- Discovering where your data is stored
- Retrieving your data
- Combining and enriching data
- Setting the standard
- Processes
- Control
- IT
- Summary
- Part 2: Understanding How to Measure the Why, What, and How
- Chapter 6: Aligning with Organizational Goals
- Understanding the types of indicators
- Identifying KPIs
- Characteristics of KPIs
- Leading and lagging indicators
- Reviewing for unintended consequences
- Applying Goodhart's law to KPIs
- Defining what to track
- Activity system maps
- Logic models
- Summary
- References
- Chapter 7: Designing Dashboards and Reports
- The importance of visualizing data
- Deceiving with bad visualizations
- Using our eyes and the usage of colors
- Introducing the DAR(S) principle
- Defining your dashboard
- Choosing the right visualization
- Understanding some basic visualizations
- Bar chart (or column chart or bar graph)
- Line chart
- Pie chart
- Heatmap
- Radar chart
- Geospatial charts
- KPIs in various ways
- Tables
- Presenting some advanced visualizations
- Bullet charts
- Addressing contextual analysis
- Summary
- Chapter 8: Questioning the Data
- Being curious and critical by asking questions
- Starting with the problem - not the data.
- Identifying the right key performance indicators (KPIs) ahead of time
- Questioning not just the data, but also assumptions
- Using a questioning framework
- Questioning based on the decision-making stage
- Questioning data and information
- Questioning analytic interpretations and insights
- Summary
- References
- Chapter 9: Handling Data Responsibly
- Introducing the potential risks of data and analytics
- Identifying data security concerns
- Intermezzo - a data leak at an airplane carrier
- Identifying data privacy concerns
- Identifying data ethical concerns
- Intermezzo - tax office profiles ethnically
- Summary
- Part 3: Understanding the Change and How to Assess Activities
- Chapter 10: Turning Insights into Decisions
- Data-informed decision-making process
- Ask - Identifying problems and interpreting requirements
- Acquire - Understanding, acquiring, and preparing relevant data
- Analyze - Transforming data into insights
- Apply - Validating the insights
- Act - Transforming insights into decisions
- Announce - Communicating decisions with data
- Assess - Evaluating outcomes of a decision
- Making a data-Informed decision in action
- Using a data-informed decision checklist
- Why data-informed over data-driven?
- Storytelling
- Why is communicating with data so hard?
- Three key elements of communication
- Why include a narrative?
- The process
- Summary
- Further reading
- Chapter 11: Defining a Data Literacy Competency Framework
- Data literacy competency framework
- Identifying problems and interpreting requirements
- Understanding, acquiring, and preparing relevant data
- Turning data into insights
- Validating the insights
- Transforming insights into decisions
- Communicating decisions with data
- Evaluating the outcome of a decision
- Understanding data
- Data literacy skills.
- Identifying data literacy technical skills
- Data literacy soft skills
- Data literacy mindsets
- Summary
- References
- Chapter 12: Assessing Your Data Literacy Maturity
- Assessing individual data literacy
- Assessing organizational data literacy
- Basic organizational data literacy assessment
- Robust organizational data literacy maturity assessment
- Summary
- Chapter 13: Managing Data and Analytics Projects
- Discovering why data and analytics projects fail
- Understanding four typical data and analytics project characteristics
- Understanding data and analytics project blockers
- Pitfalls in data and analytics projects
- Lack of expertise
- The technical architecture
- Time and money
- Unfolding the data and analytics project approach
- Unfolding the data and analytics project framework
- Intermezzo 2 - successfully managing a data and analytics project
- Mitigating typical data and analytics project risks
- Project risks
- Technical risks
- Cultural risks
- Content risks
- Determining roles in data and analytics projects (and teams)
- Managing data and analytics projects
- Writing a successful data and analytics business case
- A chapter layout for your business case
- Finding financial justification for your project
- Argumentation for one-time project costs
- Annual recurring costs
- Argumentation for annual recurring costs
- The quantitative benefits
- ROI
- Conclusion and advice
- Summary
- Chapter 14: Appendix A - Templates
- Project intake form
- STARR TEMPLATE
- Layout for a business case
- Layout for a business case scenario description
- A business case financial analysis
- Layout for a risk assessment
- Layout for a summary business case
- Layout information and measure plan
- Layout for a KPI description
- Table with the Inmon groups and a description of their roles.
- Chapter 15: Appendix B - References
- Inspirational books
- Online articles and blogs
- Dutch articles and blogs
- Online tools
- Online sites
- Index
- Other Books You May Enjoy.
