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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.
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.