Book Review - "Elements of Data Strategy"
by Boyan Angelov about "A Framework for Data and AI-Driven Transformation"
While creating a data strategy can be a complex task and you need to understand what you do, how to use tools and methods and how to guide the company and understand when to use what - be aware the book gives you an elementary overview, how to do that.
I think the title of the book is therefore well choosen by Boyan Angelov to show, that it is more about finding a start and orientation than a handbook to really do data strategy. He described the book as to “𝐭𝐚𝐤𝐞 𝐭𝐡𝐞 𝐜𝐡𝐚𝐧𝐜𝐞 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞 𝐭𝐡𝐞 𝐛𝐨𝐨𝐤, 𝐭𝐡𝐚𝐭 𝐈 𝐦𝐲𝐬𝐞𝐥𝐟 𝐰𝐨𝐮𝐥𝐝 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐫𝐞𝐚𝐝.“1 The book comes from a consultant perspective, mentioning many methods useful to try out. The title “Elements” shows, that it gives an idea of the many methods, systems and and tools you can use to understand, define, measure and communicate a data strategy for your company or your customer. Indeed often it goes not beyond the idea itself what could make it hard to apply it.
The following view will give an overview about the content, structure and topics covered:
We see an interesting part at the beginning to open the toolbox of a consultant. The concepts mentioned are interesting and relevant, but in general need much more understanding is needed, than such a book can cover.
Furthermore after the Design part the Delivery is very thin and after a promising depth for helping to understand how things work, Angelov came back to an rather overview level when it comes to delivery and make a strategy work.
For a data strategy book I also miss some areas, if, only referenced in the book. While business alignment is an important part things like formulating a vision and mission are not part of a target picture. The focus of the book is rather on an technology and architecture level. I wouldn’t have the aspiration to complete the book at least with an outlook on a necessary operating model and data governance, which is a topic on itself but would complete it somehow.
An essential closing part of the book are the interviews with pracical tipps and insights from people in the field. In the following I put together some essentials from the interviews:
So all in all, taking the book title as a orientation for the content, the book delivers what is promised - a large collection of elements of a data strategy - structured along the 3D methodology of the author (Due Diligence, Design Delivery). Regarding the readers and target group, I would recommend the book to people in implementation of data & analytics and to young data strategy consultants wanting base information and a cheat sheet, while making projects together with experienced consultants.
For me, reading the book feels sometimes like a collection of tools and approaches with brief description. While the many short interviews gives an idea about the practical side, I had loved more vital and storytelling aspects and description in the first part to help with the transfer into a project. Furthermore I thought you feel a little bit the data science backround of the author. While AI/ML is becoming a hype topic and building a data strategy which includes AI/ML is incredibly important for me this focus was sometimes to much. As I like to read real books and no ebooks, I was a little bit surprised of the quality in my hand as the book is a Amazon print on demand book which leads to a lower quality of the reading experience. While I can understand the practical aspect in general my expectation of a strategy book would to be a little bit a higher quality version, but the price justifies this decision.
A last word about the usage of GenAI (ChatGPT 3.5) to support standard definitions. It is for sure an interesting aspect in todays world and the author was very transparent how he use it. It is at least something worth trying, even if it didn't quite meet my taste, as it produces what we already know - very general descriptions. I would prefer to hear this definitions from the words of the author himself with more context. But good try!