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Data and Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy
For many organizations data is a by-product, but for the smarter ones it is the heartbeat of their business. Most businesses have a wealth of data buried in their systems which, if used effectively, could increase revenue, reduce costs and risk and improve customer satisfaction and employee experience.
Beginning with how to choose projects which reflect your organization's goals and how to make the business case for investing in data, this book then takes the reader through the five 'waves' of organizational data maturity. It takes the reader from getting started on the data journey with some quick wins, to how data can help your business become a leading innovator which systematically outperforms competitors.
Data and Analytics Strategy for Business outlines how to build consistent, high-quality sources of data which will create business value and explores how automation, AI and machine learning can improve performance and decision making. Filled with real-world examples and case studies, this book is a stage-by-stage guide to designing and implementing a results-driven data strategy.
Beginning with how to choose projects which reflect your organization's goals and how to make the business case for investing in data, this book then takes the reader through the five 'waves' of organizational data maturity. It takes the reader from getting started on the data journey with some quick wins, to how data can help your business become a leading innovator which systematically outperforms competitors.
Data and Analytics Strategy for Business outlines how to build consistent, high-quality sources of data which will create business value and explores how automation, AI and machine learning can improve performance and decision making. Filled with real-world examples and case studies, this book is a stage-by-stage guide to designing and implementing a results-driven data strategy.
328 pages, Paperback
Published June 28, 2022
15 people are currently reading
42 people want to read
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Displaying 1 - 3 of 3 reviews
August 7, 2025
This is a good "data manual" for managers and business owners. I would even advise recruiters to read it. There is a very good chapter about the different types of data roles, and another one about forming and recruiting for a data team. My recruiter would love it, because it is clear, actionable, and accessible for someone who is not in IT or data.
Simon Asplen-Taylor, PhD and data veteran, explains the role of data in business, the different types of data-related roles (data analyst, data engineer, Chief Data Officer etc.). The book also provides a wealth of information about leveraging data (+ a special chapter about AI), data insights, and what to expect from a data team.
Some things I like
The data periodic table. It is the type of document that would be very useful if you want to communicate a data strategy to the CFO! I never heard about it before, and frankly, it is brilliant.
The chapter about data risk management and ethics is also very useful and well-written. It explains why we need to care about ethics when we store, share and process data. I worked on GDPR compliance when it was first implemented (even gave a couple conference talks on that topic). It was a big shock to the engineering community back then! Many companies and organizations, including large ones, had never thought about how their data could be used if it fell in the wrong hands. GDPR, despite its annoying aspects (who loves regulation with big fines?) brought up a real ethics conversation in the data realm. Compliance is not a curse, it is an opportunity.
The visuals! I love visuals, schemas, anything that helps a visual thinker understand complex things. There are many visuals in the book, but not so many that it turns into a collection of schemas. They are well-done, clear, professional (all of them could be printed, put next to a desk, and would be appropriate even in corporte offices).
What was missing IMHO
FAIR data principles. They are the next big thing. Easy to understand, easy to teach, and surprisingly profund. I would have liked them to be referenced in the book. Or did I miss it?
A chapter about data infrastructure and storage would also be useful.
Way too many businesses end up spending a fortune storing their data. As soon as you start collecting and storing a significant amount of data, you need to talk to IT infrastructure.
It can save a lot of money, protect your data from the most common data loss, and help you with your data inventory as well.
Why not 5 stars?
Because this book is not life changing or absolutely brilliant *for me*.
It is good. I would advise some of my colleagues to buy it. Actually, I am considering buying it for myself, as a useful reference of data concepts (and also something my colleagues could borrow).
But I am not new to data. A lot of the content of this book was hammerred into me when I was studying computer science (and that was nearly 20 years ago). I heard the rest during the many conversations I ended up having with different types of data people. So... I was already spoiled in that area. This is a rather entry-level book.
Long story short
Simon Asplen-Taylor did a really good job here.
The book was also impeccably edited. No tangents, all the examples are relevant, the visuals are great. From the table of contents, you already know what you are signing up for.
A wide audience could benefit from this book.
Thank you #NetGalley, Simon Asplen-Taylor and Kogan Page for the ARC.
Simon Asplen-Taylor, PhD and data veteran, explains the role of data in business, the different types of data-related roles (data analyst, data engineer, Chief Data Officer etc.). The book also provides a wealth of information about leveraging data (+ a special chapter about AI), data insights, and what to expect from a data team.
Some things I like
The data periodic table. It is the type of document that would be very useful if you want to communicate a data strategy to the CFO! I never heard about it before, and frankly, it is brilliant.
The chapter about data risk management and ethics is also very useful and well-written. It explains why we need to care about ethics when we store, share and process data. I worked on GDPR compliance when it was first implemented (even gave a couple conference talks on that topic). It was a big shock to the engineering community back then! Many companies and organizations, including large ones, had never thought about how their data could be used if it fell in the wrong hands. GDPR, despite its annoying aspects (who loves regulation with big fines?) brought up a real ethics conversation in the data realm. Compliance is not a curse, it is an opportunity.
The visuals! I love visuals, schemas, anything that helps a visual thinker understand complex things. There are many visuals in the book, but not so many that it turns into a collection of schemas. They are well-done, clear, professional (all of them could be printed, put next to a desk, and would be appropriate even in corporte offices).
What was missing IMHO
FAIR data principles. They are the next big thing. Easy to understand, easy to teach, and surprisingly profund. I would have liked them to be referenced in the book. Or did I miss it?
A chapter about data infrastructure and storage would also be useful.
Way too many businesses end up spending a fortune storing their data. As soon as you start collecting and storing a significant amount of data, you need to talk to IT infrastructure.
It can save a lot of money, protect your data from the most common data loss, and help you with your data inventory as well.
Why not 5 stars?
Because this book is not life changing or absolutely brilliant *for me*.
It is good. I would advise some of my colleagues to buy it. Actually, I am considering buying it for myself, as a useful reference of data concepts (and also something my colleagues could borrow).
But I am not new to data. A lot of the content of this book was hammerred into me when I was studying computer science (and that was nearly 20 years ago). I heard the rest during the many conversations I ended up having with different types of data people. So... I was already spoiled in that area. This is a rather entry-level book.
Long story short
Simon Asplen-Taylor did a really good job here.
The book was also impeccably edited. No tangents, all the examples are relevant, the visuals are great. From the table of contents, you already know what you are signing up for.
A wide audience could benefit from this book.
Thank you #NetGalley, Simon Asplen-Taylor and Kogan Page for the ARC.
August 22, 2022
I found this book a challenge to read as an ebook. It may be that I've read too many books pushing the value of data and analytics because the material felt regurgitated. As with other books I've read on this subject, the same power companies are used as examples. For those who are new to the data and analytics topic, there may be new ideas in this book.
ARC was provided by Kogan via NetGalley in exchange for an honest review.
ARC was provided by Kogan via NetGalley in exchange for an honest review.
November 16, 2023
Great to know more about data transformation, how to developl it in a company.
Displaying 1 - 3 of 3 reviews