Bill Schmarzo - The Economics of Data, Analytics, and Digital Transformation

The Economics of Data, Analytics, and Digital Transformation

The theorems, laws, and empowerments to guide your organization's digital transformation

Bill Schmarzo

BIRMINGHAM - MUMBAI

The Economics of Data, Analytics, and Digital Transformation

Copyright 2020 Packt Publishing

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First published: November 2020

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ISBN 978-1-80056-1410

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In today's digital era, every organization has data, and by the ton! Having a lot of data is neither a market discriminator for an organization, nor is it a bragging point anymore (if that ever made sense anyway). The real market discriminators are an organization's data-fueled analytics products that inspire innovation, deliver insights, inform actionable decisions, generate value, and produce mission success for the organization. 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 is not offering a set of static lecture points on analytics that you can sit and reflect upon, but it offers a dynamic progression of purpose-driven ignition points that should lift you out of your seat and that you can act upon. Bill Schmarzo is the perfect author to deliver such analytics insights through this book. He is an experienced, influential, and inspiring exemplar of data analytics leadership, and those characteristics are evident throughout its pages and chapters.

By directly addressing the economics of data, analytics, and digital transformation, Bill affirms one of the greatest defining characteristics of data. Data is not a typical business asset that depletes and depreciates with use. Its economic value is far greater and more interesting than that. Data is a renewable and reusable source, essentially never depleted (at least not depleted in a business cycle lifetime). Data can be used and reused for countless different applications, without diminishing its economic value. The use cases may have varying levels of value, which need to be assessed and evaluated, and that is exactly the theme for most of this book's chapters. We also learn that data is not only a renewable source of value, with essentially zero marginal cost for new applications, but data is also a source for asset creation that appreciates in value.

Clear steps toward achieving actionable insights, discovery from data, economic value from analytics, and forward progress toward true digital transformation are the invaluable contributions of this book. But let us not forget the marvelous collection of infographics that accompany these rich concepts. Anyone who has read Bill's articles will recognize his style of infographics and will know what a great source of knowledge they are. As someone once said (it was me, actually), "a word is worth one milli-picture." Consequently, one can be justified in and forgiven for initially skipping the words and instead first looking at the pictures in this book. They give true meaning to the label "infographic," for they contain a wealth of information, knowledge, lessons learned, and actionable insights too. They also serve as invaluable anchors to the key concepts, theorems, and performance indicators described in the various chapters, thus firmly planting each important lesson in a memorable visual context.

While there are many data analytics books available on the market, many of which help you learn concepts, applications, and use cases, this book goes beyond one-shot learning. The focus here is correctly on continuous learning, not just concepts about data, but learning continuously from datarapidly and effectively in an agile culture of innovation and experimentation! That is what machine learning is all about and what data science isinferring, learning, refining, and relearning! As Bill says, "Data science is about identifying variables and metrics that might be better predictors of performance." That is an experimental (scientific) process that involves data! Data science is only of value to a business when it creates business value. Some analytics and data science projects fail because they are isolated projects (perhaps science experiments) for the R&D department, and thus are not an integral component of business decisions, enterprise planning, and corporate mission. Continuous learning requires humility, empowered teams, and explicit corporate recognition of this truth: "If you aren't failing enough, then you're not learning enough."

One thing that a scientific process does is to separate failed hypotheses and predictions from successful ones. Bill addresses this also, within its business context, when he describes how some organizations fail in their analytics programs due to having too many use cases, not from the lack of use cases. These organizations have not used analytics on their analytics program itself. This meta-approach expertly guides you through the theorems, data asset valuations, and economic principles presented in a unique style throughout this book. The Data Science Value Engineering Framework will steer organizations toward lean, efficient, and effective analytics innovations, outcomes, and mission successes. That might require some unlearning to get started with and to get right, but most learning starts with unlearning. As Bill says, "