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Be Data Analytical: How to Use Analytics to Turn Data into Value

✍ Scribed by Jordan Morrow


Publisher
Kogan Page
Year
2023
Tongue
English
Leaves
241
Edition
1
Category
Library

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✦ Synopsis


Be Data Analytical is the book organizations and individuals need to understand how to truly use analytics to turn data into valuable insights and drive smarter decision making.

Data needs analytics to turn it into value and for organizations to be truly data-driven, they need to use analytics correctly. However, most organizations do not move beyond the first, most rudimentary stage of analytics. They miss out on the powerful insights and opportunities available with all the four levels of analytics: descriptive, diagnostic, predictive and prescriptive.
Be Data Analytical reveals how to supercharge data value through all the four levels of analytics, bringing data to life and enhancing data-driven decision making.

Be Data Analyticalexamines each of these four levels of analytics in-depth: what they are, why they matter, how they can be used strategically and how they can be implemented. The book also explores how individuals and organizations can improve their skills and performance in each of these areas. Written by a global trailblazer in the world of data literacy, the book shows professionals, managers, leaders and organizations how to use analytics for the successful and strategic conversion of data into value, insight and action.

✦ Table of Contents


Cover
Contents
About the author
Acknowledgments
Preface
Introduction
PART ONE Data and analytics
1 Defining data and analytics
Mountain mining example
Data and analytical skills—data literacy
Data driven
MVP—minimum viable proficiency
Chapter summary
Notes
2 Defining the four levels of analytics
Analytic level 1—descriptive
Analytic level 2—diagnostic
Analytic level 3—predictive
Analytic level 4—prescriptive
Chapter summary
Notes
3 The power of analytics in decision making
Data
Analytics
Descriptive analytics
Diagnostic analytics
Predictive analytics
Prescriptive analytics
Framework, decision, data storytelling
Chapter summary
Notes
PART TWO The four levels of analytics: define, empower, understand and learn
4 Descriptive analytics
What are descriptive analytics?
Roles
Tools and technologies
Chapter summary
5 How are descriptive analytics used today?
Democratization of data
Democratization of descriptive analytics—tools and technology
Industry examples
Data ethics and descriptive analytics
Chapter summary
Note
6 How individuals and organizations can improve in descriptive analytics
Descriptive analytics and data-driven problem solving
Descriptive analytics and data-driven decision making
Descriptive analytics and data-driven execution
Chapter summary
Notes
7 Diagnostic analytics
What are diagnostic analytics?
The housing crash
The question why
A personal example
Diagnostic analytics and organizational roles
Tools and technologies
Chapter summary
Notes
8 How are diagnostic analytics used today?
Democratization of data—diagnostic analytics
Democratization of diagnostic analytics—tools and technology
Diagnostic analytics—data visualization
Diagnostic analytics—coding
Diagnostic analytics—statistics
Industry examples—continued from Chapter 5
Data ethics and diagnostic analytics
Chapter summary
Note
9 How individuals and organizations can improve in diagnostic analytics
Data and analytics mindset—individuals
Data and analytics mindset—organizations
Diagnostic analytics and the tridata
Diagnostic analytics and data-driven problem solving
Diagnostic analytics and data-driven decision making
Diagnostic analytics and data-driven execution
A note on learning
Chapter summary
Notes
10 Predictive analytics
What are predictive analytics?
Roles
Tools and technologies
Chapter summary
Notes
11 How are predictive analytics used today?
Democratization of data—predictive analytics
Democratization of predictive analytics—tools and technology
Data visualization, data storytelling, and more
Industry examples—continued from Chapter
Data ethics and predictive analytics
Chapter summary
Notes
12 How individuals and organizations can improve in predictive analytics
Data and analytics mindset—predictive analytics
The mindset matters
Predictive analytics and the tridata
Predictive analytics and data-driven problem solving
Predictive analytics and data-driven decision making
Predictive analytics and data-driven execution
Chapter summary
13 Prescriptive analytics
What are prescriptive analytics?
Roles
Tools and technologies
Chapter summary
Notes
14 How are prescriptive analytics used today?
Democratization of data—prescriptive analytics
Democratization of prescriptive analytics—tools and technology
Data strategy
Data storytelling
Industry examples
Data ethics and prescriptive analytics
Chapter summary
Notes
15 How individuals and organizations can improve in prescriptive analytics
Data and analytic mindset and data literacy—prescriptive analytics
Prescriptive analytics and the tridata
Data-driven problem solving and prescriptive analytics
Data-driven decision making and prescriptive analytics
Data-driven execution and prescriptive analytics
Chapter summary
Note
PART THREE Bringing it all together
16 Using all four levels of analytics to empower decision making
Six steps of analytical progression
Making a decision with the four levels of analytics
Chapter summary
Conclusion
Index


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