Be Data Literate

The Data Literacy Skills Everyone Needs To Succeed

Jordan Morrow

• Publisher: Kogan Page
• Year: 2021
• ISBN:
  9781789668025
• Edition: 1
• Topic: Business
• Subtopic: IT Industry
• Index: Business

WINNER: International Book Awards 2023 - Business: Technology In the fast moving world of the fourth industrial revolution not everyone needs to be a data scientist but everyone should be data literate, with the ability to read, analyze and communicate with data. It is not enough for a business to have the best data if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician. With learnings to show development and real-world examples from industries implementing data literacy skills, this book explains how to confidently read and speak the 'language of data' in the modern business environment and everyday life. Be Data Literate is a practical guide to understanding the four levels of analytics, how to analyze data and the key steps to making smarter, data-informed decisions. Written by a founding pioneer and worldwide leading expert on data literacy, this book empowers professionals with the skills they need to succeed in the digital world.

Index

NB: page numbers in italic indicate figures or tables.

algorithms 188, 189

‘black box’ algorithms 69–70

Alteryx 29, 30, 97, 182, 205

Amazon 3, 4, 5

Alexa 186

analyzing data 44–47

‘analyzing’, defining 44

and creativity 148

and critical thinking 153

and curiosity 137, 139

data literacy learning 127

decision-making, framework for 165

descriptive analytics 95, 96

diagnostic analytics 98

for executives 46, 64

machine learning 189

for the product team 46

questions, answering 44–45

for the R&D team 46

anchoring 170–71

Apache Spark 29

Apple

iPhone 207

Macintosh 18

artificial intelligence 77, 109, 185–88

bias in 188

defining 185

and descriptive analytics 187

and diagnostic analytics 187–88

examples of 186

and predictive analytics 188

and prescriptive analytics 188

and productivity 187

augmented intelligence 77

Avoiding Data Pitfalls 93

Bayesian statistics 77

bias, eliminating 71–72, 169–71, 174–75

in artificial intelligence 188

confirmation bias 170

first impression bias 170–71

status quo bias 170

Big Book of Dashboards, The 58–59, 93

big data 54, 77, 190–91

business intelligence 77, 182–85

defining 183

for democratization of data 184

for descriptive analytics 184

for diagnostic analytics 184

for predictive analytics 184

for prescriptive analytics 184

tools for 182

chief data officer 120–21

cholera outbreak, 1800s London 59, 59, 100–02, 101, 104, 140, 141–43

Cloud, the 193–94

Coca-Cola 79, 81, 110

Cogito 186

communicating with data 47–49

‘communicating’, defining 47

and creativity 148–49

and critical thinking 153

and curiosity 140

data literacy learning 128

for data scientists 48

data storytelling 47–48

decision-making, framework fo...