Economics of Data & Analytics
December 15, 2022

How thinking about the economics of data & analytics can help

By Paul Laughlin

Since reading writers like Tim Harford, I’ve become increasingly persuaded that the economics of data & analytics matter. Considering the challenges data & analytics leaders face from an economics perspective can help recast problems & identify opportunities. Indeed my time creating & leading such teams revealed that economics graduates often went on to become very able analysts.

So, I was intrigued to see the self style Dean of Big Data himself, Bill Schmarzo, publish a book entitled The Economics of Data, Analytics and Digital Transformation. He published it in 2020, but it’s taken a couple of years to get to the top of my reading list. Given the time I now spend training public sector leaders in Digital Transformation, it’s become even more relevant.

Now I have read it, I’ve not been disappointed. Particularly as it is pitched to be accessible to the novice, whilst also peppered with new ideas or approaches for experienced leaders. There is lots in here that could help both data leaders and those working to help educate their boards. Too few technical writers focus on financial measures, businesses cases and how to optimise the value of your data. So, this is welcome addition to data leader bookcases. In this post I’ll just give you an overview of why to buy it.

How this book lays out the economics of data & analytics

Bill structures his content in this book to run from the introductory to the more advanced topics. Via clearly labelled chapters (that will make this an easy reference later) this book includes:

  1. CEO Mandate (understanding maturity curve & how to make progress along it)
  2. Value Engineering (how clarity on strategic priorities & use cases should guide approach)
  3. Economic Concepts (a refresher on key economic terms/measures that can help)
  4. A research paper on the Economic Value of Data (and how to value it)
  5. 5 theorems to help you realise the economic value of data
  6. The economics of AI (from orphaned analytics to reusing AI/analytics models)
  7. 3 economic effects for Digital Transformation (how they can help guide approach)
  8. 8 laws of Digital Transformation (changes needed to survive the revolution)
  9. Creating a culture of innovation (through 5 ways to bring empowerment)

Beyond that content, this book is also a helpful guide because of the way the information is laid out. Chapters are relatively short (able to finish in one sitting & made up of short 1 or 2 page sections). Each also ends in a short (half a page on average) summary that summarises the main message. Plus, Bill includes links for further reading and “homework” by way of questions to score yourself against. That last part can be particularly valuable if you make use of the space provided to assess your score & implications.

What makes this worth reading, given all the other books on data?

It’s a good question to ask, as there are so many books available these days. My answer would be that Bill’s voice it worth hearing above the throng, as he offered something different. Most of the data books out there are either focused on technical skills, data literacy, data communication or becoming a CDO.

However, in practice, I find most data & analytics leaders are actually wrestling with three primary problems. First, recruitment & retention of the talent they need. Second, proving value, securing investment and focussing limited technical resources on what matters most. Thirdly, transforming the wider organisation to thrive in an increasingly digital & data capable competitive market. I’ve previously shared books on the the first challenge (Talent) and the third (Why Digital Transformation Fail). Bill’s book provides helpful guidance on the second & how to communicate this way to your top table.

Like all good teachers, in truth Bill has just a few messages that he gets across through repetition & application. By the end, you should have ringing in your ears that you need to:

  • Think in the terms of economics to realise value from data
  • Focus on use cases & measuring value realisation not technology
  • Obsess about spotting opportunities for reuse of data & analytics outputs
  • Prioritise where to focus to generate maximum value through reuseability
  • Create a culture of empowerment to encourage innovation across teams
  • Use the same approach to guide reuse for AI & Digital Transformation

How could this book be improved and what should I do next?

Having hopefully made clear that I think this book is well worth reading, there are a few improvements I’d like to suggest to Bill. This is a very helpful book but can also be frustrating for the reader because of two elements that I feel could be improved.

The first is the infographics included in this book. Now I love visual communication, big picture stories, and well designed data visualisation or infograhics. However, certainly in the paperback version of this book, the infographics included in the chapters are almost unreadable. They are too small, are less effective in black & white, plus too low resolution to see everything. At the end of the book an appendix reproduces all of Bill’s rich infographics, but again the resolution, size plus monochrome lets them down. It would be great if Bill could at least provide a link to where these could all be seen at higher resolution and in colour online.

The other improvement that I’d suggest is just a catch-up in terms of the language being used for such functions these days. I have a lot of sympathy for Bill here. I too come from the generation of data & analytics leaders who were making a real difference with statistical models before calling it Data Science. However, I do find the blurring of language between Analytics & Data Science is confusing at times. Having introduced a maturity index in the first chapter, the language used in the rest of the book blurs the line between Data Science & Analytics or BI work. For a number of his later recommendations it would also be helpful to recognise the role of Data Engineering & Data Ops specialisms now.

I hope it helps you increase your commercial credibility

All that said, in the interests of a balanced review, The Economics of Data, Analytics and Digital Transformation is a boom that will help many data leaders. One of the challenges that leaders who have risen through the ranks of data or analytics roles can face is commercial naiveté. Beyond all the hype on social media, there is a need to realise that boardroom decisions are driven by economic arguments. Financial or Sales leaders can hold sway because they are able to articulate how their recommended action will achieve or protect financial targets.

Following the advice in this book will help data & analytics leaders become more fluent in that language. It could also revolutionise the approach to using data, technology & technical people – as it brings more disciple and rigour to prioritisation. I encourage data leaders to follow such economic principles. Identify opportunities for reuse. Standardise methods & knowledge management to be able to maximise reuse. Plus, crucially, become as economically literate & persuasive as Bill.

You might even choose to take a leaf out of his book and present your cases more visually. A well designed infographic couple with a clear financial case, might just transform how your board sees you.