The Economics of Data, Analytics, and Digital Transformation: The theorems, laws, and empowerments to guide your organization's digital transformation

by Bill Schmarzo, Kirk Borne

Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning

Key FeaturesMaster the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindsetAcquire implementable knowledge on digital transformation through 8 practical lawsExplore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctlyBook DescriptionIn today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise.

The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning.

By the end of the book, you will have the tools and techniques to drive your organization's digital transformation.

Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the

"Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon."

What you will learnTrain your organization to transition from being data-driven to being value-drivenNavigate and master the big data business model maturity indexLearn a methodology for determining the economic value of your data and analyticsUnderstand how AI and machine learning can create analytics assets that appreciate in value the more that they are usedBecome aware of digital transformation misconceptions and pitfallsCreate empowered and dynamic teams that fuel your organization's digital transformationWho this book is forThis book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.

Table of ContentsThe CEO Become Value-driven, Not Data-drivenValue The Secret Sauce for Data Science SuccessA Review of Basic Economic ConceptsUniversity of San Francisco Economic Value of Data Research PaperThe Economic Value of Data TheoremsThe Economics of Artificial IntelligenceThe Schmarzo Economic Digital Asset Valuation TheoremThe 8 Laws of Digital TransformationCreating a Culture of Innovati

Genres: Business

260 pages, Kindle Edition

Published November 30, 2020

Reviews

[Pablo María Fernández · Rating 2 out of 5]

I found out about the author on a YouTube interview. I prefer his book "Big Data MBA" that brings more practical advice. In this one he repeats many classic data science concepts, presents a lot of frameworks and schemes with his name on them that I didn’t find useful, neither the Further reading sections in which many times he cites his own work or Wikipedia’s entries. I skipped all the Economics basics, maybe for someone who has a more technical major they provide some value.
I don’t agree with some of his assumptions like that Tensorflow is key to Google profitability when it’s due to its monopoly on the Ad industry than began much earlier than that platform
He talks about Digital Transformation, Big Data and other hypes but in an schematic way that sounds too rigid and at least for me didn’t provide much value. Like the other book of his it can be read almost as a textbook with all the pros and cons of that: sound naive in many parts (we all know that many things he talks about only exists on Powerpoint presentations and are almost impossible to achieve in a real project). Again I miss that he didn’t show more of his three-decades-long career. It’s fine to have some theory to get a general understanding (for the new ones) but it would provide much more value if he shared his experience in the trenches: challenge and problems he encountered and how he solved them. It's a pity because both books seemed to be written by a consultant of a big firm that know very well the concepts but never applied them in real life that probably is not the case.

[Roel Peters · Rating 1 out of 5]

Poorly written, badly structured and lazy content. This book is a mishmash of introductory economics concepts, business lingo, half-baked "research" and a huge dose of invented frameworks and theorems. The latter solely serves the purpose of improving the author's personal brand.

The chapter on digital transformation starts from a very, very narrow AI-centric definition and makes no sense at all.

Don't read this crap, not even as a free whitepaper.

[Paul Laughlin · Rating 4 out of 5]

How thinking about the economics of data & analytics can help
By Paul Laughlin · December 15, 2022
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
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Book Reviews to cross post to Amazon & Goodreads
How thinking about the economics of data & analytics can help
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
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Book Reviews to cross post to Amazon & Goodreads
How thinking about the economics of data & analytics can help
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.

[Anton · Rating 4 out of 5]

Good infographics and summaries

I like how this book sums up multiple important concepts, but it would be enough to just read summaries. Chapter contents are very repetitive.

[Peter · Rating 3 out of 5]

- A couple of good ideas, however repeated so many times
- a bit dry and lacking examples

[Ramesh Ruthrasekar · Rating 5 out of 5]

Highly recommended book for introduction to digital transformation. The book gives a high level framework on how data is of relevance to generating economic value and how the tools facilitating it renders "data as an appreciative asset" I.e. data is the new oil.

+ Helps understand the correlation of AI, machine learning, deep learning, digitization, economics, Analytics in context of digital transformation
+ Data analysts & scientists are not just programmers. They facilitate economic value. Helps understand these new professions
+ Learning about phases of Data models from descriptive, Predictive, prescriptive to autonomous was eye opening

- Effectively a 100 page book printed over 200 pages. Was easy to read though