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Data Juice: 101 Stories of How Organizations Are Squeezing Value from Available Data Assets
Sometimes the hardest part of squeezing value from data is just coming up with ideas on how to do it. Well, this book is sure to get your juices flowing. Peel back the cover and you’ll find more than 100 real-world examples and expert commentaries on how organizations around the world and in every industry are monetizing their own (and others’) data in diverse ways. Data Juice is ideal for data, business, and IT leaders looking to inspire their teams or executives with ways to thrive in the Digital Age. The evolution from just doing digital stuff to being truly digital requires a level of data literacy that begins with a vision of the art of the possible. It’s storytime!
- GenresBusiness
413 pages, Kindle Edition
Published July 21, 2022
18 people are currently reading
78 people want to read
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Displaying 1 - 3 of 3 reviews
October 2, 2022
Data Juice proves just how much data has become a critical asset, across industries, spanning the full range of business objectives.
June 21, 2023
This is a good compendium of case studies explained simply if sloppily. I found it to be an enjoyable quick business oriented read.
January 26, 2023
I wasn't impressed by this text.
In a nutshell: too high level and superficial, not very applicable hence not very informative.
A few pages per example, too repetitive, it is just a collection of analytics use cases, mainly regarding predictive analytics.
Maybe it is written for data newbies to get a general understanding of what you can do with the data.
I would have appreciated more a comprehensive illustration of data use cases or some deep dives.
The schema is the same: present the company and what it does; business problem (fraud, user’s recommendations, specific predictions, supply forecast, intelligent marketing, data governance and centralization); technology implemented; benefits.
The best part of this book is actually the introduction.
The author brings forward the idea of a value compass: revenue, quality, costs, risks.
Data monetisation is both indirect and direct: improving the process, performance, reducing risk, developing new products; trading with the data, enhancing products with the data, licensing, road, data through brokers, selling insights.
If anything, it makes you appreciate how relevant and pivotal data analytics is nowadays, in order to extract value, make informed decisions and stay competitive.
"Data is non-rivalrous, non-depleting, progenitive resource, one that can be used multiple ways simultaneously, doesn't get used up when consumed, and that typically generates more data when it is used."
It maybe more suited for someone who wants to pick some general ideas about how to implement analytics.
In a nutshell: too high level and superficial, not very applicable hence not very informative.
A few pages per example, too repetitive, it is just a collection of analytics use cases, mainly regarding predictive analytics.
Maybe it is written for data newbies to get a general understanding of what you can do with the data.
I would have appreciated more a comprehensive illustration of data use cases or some deep dives.
The schema is the same: present the company and what it does; business problem (fraud, user’s recommendations, specific predictions, supply forecast, intelligent marketing, data governance and centralization); technology implemented; benefits.
The best part of this book is actually the introduction.
The author brings forward the idea of a value compass: revenue, quality, costs, risks.
Data monetisation is both indirect and direct: improving the process, performance, reducing risk, developing new products; trading with the data, enhancing products with the data, licensing, road, data through brokers, selling insights.
If anything, it makes you appreciate how relevant and pivotal data analytics is nowadays, in order to extract value, make informed decisions and stay competitive.
"Data is non-rivalrous, non-depleting, progenitive resource, one that can be used multiple ways simultaneously, doesn't get used up when consumed, and that typically generates more data when it is used."
It maybe more suited for someone who wants to pick some general ideas about how to implement analytics.
Displaying 1 - 3 of 3 reviews