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E-grāmata: Be Data Analytical: How to Use Analytics to Turn Data into Value

  • Formāts: 240 pages
  • Izdošanas datums: 03-Jun-2023
  • Izdevniecība: Kogan Page Ltd
  • Valoda: eng
  • ISBN-13: 9781398609297
  • Formāts - PDF+DRM
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  • Formāts: 240 pages
  • Izdošanas datums: 03-Jun-2023
  • Izdevniecība: Kogan Page Ltd
  • Valoda: eng
  • ISBN-13: 9781398609297

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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 Analytical examines 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.



Learn to use analytics more effectively, convert data into value, and facilitate smarter and better decision-making for your organization.

Recenzijas

"A must-read for anyone looking to harness the power of data. Be Data Analytical stands out as a comprehensive guide that empowers readers to unlock the hidden potential within their data, driving innovation and growth in any field." * Bernard Marr, Founder & CEO, Bernard Marr & Co * "If you're looking for a practical guide to learn about the four levels of analytics, look no further than Be Data Analytical. Jordan Morrow's hands-on approach to teaching data analytics makes the book an invaluable resource for anyone who wants to learn the skills needed to succeed in the field. The clear explanations, practical examples and content breakdown make this book an excellent choice for both beginners and experienced professionals." * Chandra Donelson, Washington D.C. Chapter Lead, Women in Data * "Jordan's passion and enthusiasm for data shines through. Breaking down analytics into four accessible levels means this book is for everyone. Its real-life examples and analogies bring to life the importance of understanding and implementing good analytics." * Susan Walsh, Founder & Managing Director, The Classification Guru Ltd * "This book provides an excellent framework for data-driven decision-making in organizations. By framing the analytics implementations progressively through the four levels of analytics, Be Data Analytical is easy to follow as an analytics guidebook. At each stage, the book covers key definitions, roles of the different enterprise players, numerous business examples and strategy suggestions to get the analytics job done." * Kirk Borne, Founder, Data Leadership Group * "Data Analytics is a crucial aspect of decision-making in the modern business landscape, and this book provides a comprehensive guide to understanding its nuances. The author's expertise and passion for the subject is present in every chapter, making this book a must-read for anyone seeking to improve their data literacy and enhance their decision-making skills. I highly recommend this book to anyone looking to unlock the power of data analytics in their organization." * Esther Munyi, Chief Data and Analytics Officer, Sasfin * "Be Data Analytical is a book about leadership, decision making, staying ahead and having your own built-in systems. A consummate storyteller, Jordan speaks to those who know this space and those who perhaps need to. The data environment has changed forever and the complexity and challenge for leaders means the rule book we used to follow, and our previous frames of reference, are redundant. New ways of thinking and improving decision making are therefore vital." * Mike Roe, CEO, Tensense.ai * "Ingenious! Jordan's engaging work propels the reader from data literacy to data analysis." * Major General Dustin Shultz *

About the author xiii
Acknowledgments xv
Preface xvii
Introduction 1(1)
PART ONE Data and analytics
1(48)
1 Defining data and analytics
9(14)
Mountain mining example
10(3)
Data and analytical skills---data literacy
13(3)
Data driven
16(2)
MVP---minimum viable proficiency
18(3)
Chapter summary
21(1)
Notes
22(1)
2 Defining the four levels of analytics
23(14)
Analytic level 1 descriptive
23(3)
Analytic level 2 diagnostic
26(3)
Analytic level 3 predictive
29(3)
Analytic level 4 prescriptive
32(1)
Chapter summary
33(2)
Notes
35(2)
3 The power of analytics in decision making
37(12)
Data
38(1)
Analytics
39(1)
Descriptive analytics
40(2)
Diagnostic analytics
42(2)
Predictive analytics
44(1)
Prescriptive analytics
45(1)
Framework, decision, data storytelling
46(1)
Chapter summary
47(1)
Notes
48(1)
PART TWO The four levels of analytics: define, empower, understand and learn
49(146)
4 Descriptive analytics
51(12)
What are descriptive analytics?
51(4)
Roles
55(4)
Tools and technologies
59(1)
Chapter summary
60(3)
5 How are descriptive analytics used today?
63(14)
Democratization of data
63(2)
Democratization of descriptive analytics---tools and technology
65(6)
Industry examples
71(3)
Data ethics and descriptive analytics
74(1)
Chapter summary
74(1)
Note
75(2)
6 How individuals and organizations can improve in descriptive analytics
77(14)
Descriptive analytics and data-driven problem solving
79(6)
Descriptive analytics and data-driven decision making
85(2)
Descriptive analytics and data-driven execution
87(2)
Chapter summary
89(1)
Notes
90(1)
7 Diagnostic analytics
91(12)
What are diagnostic analytics?
91(1)
The housing crash
91(1)
The question why
92(1)
A personal example
93(1)
Diagnostic analytics and organizational roles
94(5)
Tools and technologies
99(2)
Chapter summary
101(1)
Notes
101(2)
8 How are diagnostic analytics used today?
103(12)
Democratization of data---diagnostic analytics
103(1)
Democratization of diagnostic analytics---tools and technology
104(2)
Diagnostic analytics---data visualization
106(1)
Diagnostic analytics---coding
107(1)
Diagnostic analytics---statistics
107(1)
Industry examples---continued from
Chapter 5
108(4)
Data ethics and diagnostic analytics
112(1)
Chapter summary
112(1)
Note
113(2)
9 How individuals and organizations can improve in diagnostic analytics
115(12)
Data and analytics mindset---individuals
115(2)
Data and analytics mindset---organizations
117(1)
Diagnostic analytics and the tridata
118(1)
Diagnostic analytics and data-driven problem solving
118(5)
Diagnostic analytics and data-driven decision making
123(1)
Diagnostic analytics and data-driven execution
124(1)
A note on learning
125(1)
Chapter summary
125(1)
Notes
126(1)
10 Predictive analytics
127(12)
What are predictive analytics?
127(4)
Roles
131(4)
Tools and technologies
135(1)
Chapter summary
136(2)
Notes
138(1)
11 How are predictive analytics used today?
139(14)
Democratization of data---predictive analytics
139(3)
Democratization of predictive analytics---tools and technology
142(3)
Data visualization, data storytelling, and more
145(2)
Industry examples---continued from
Chapter 8
147(3)
Data ethics and predictive analytics
150(1)
Chapter summary
150(1)
Notes
151(2)
12 How individuals and organizations can improve in predictive analytics
153(12)
Data and analytics mindset---predictive analytics
153(1)
The mindset matters
154(2)
Predictive analytics and the tridata
156(1)
Predictive analytics and data-driven problem solving
156(4)
Predictive analytics and data-driven decision making
160(1)
Predictive analytics and data-driven execution
161(1)
Chapter summary
162(3)
13 Prescriptive analytics
165(10)
What are prescriptive analytics?
165(6)
Roles
171(2)
Tools and technologies
173(1)
Chapter summary
173(1)
Notes
174(1)
14 How are prescriptive analytics used today?
175(10)
Democratization of data---prescriptive analytics
175(2)
Democratization of prescriptive analytics---tools and technology
177(1)
Data strategy
178(1)
Data storytelling
179(1)
Industry examples
179(4)
Data ethics and prescriptive analytics
183(1)
Chapter summary
183(1)
Notes
184(1)
15 How individuals and organizations can improve in prescriptive analytics
185(10)
Data and analytic mindset and data literacy---prescriptive analytics
185(3)
Prescriptive analytics and the tridata
188(1)
Data-driven problem solving and prescriptive analytics
188(2)
Data-driven decision making and prescriptive analytics
190(2)
Data-driven execution and prescriptive analytics
192(1)
Chapter summary
193(1)
Note
193(2)
PART THREE Bringing it all together
195(22)
16 Using all four levels of analytics to empower decision making
197(20)
Six steps of analytical progression
197(8)
Making a decision with the four levels of analytics
205(3)
Chapter summary
208(3)
Conclusion
211(6)
Index 217
Jordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs. He is the founder and CEO of Bodhi Data, served as the Chair of the Advisory Board for The Data Literacy Project and has helped companies and organizations around the world, including the United Nations, build and understand data literacy. Morrow is the author of three books: Be Data Literate, Be Data Driven, and Be Data Analytical, all published by Kogan Page. He is based near Salt Lake City, Utah.