Foreword

Over the past couple of decades, analytics, for the most part, has evolved from groups of report writers somewhat resembling "typing pools" from days gone by to an assortment of organizational and operating models requiring a wide range of technical and business skills. Few have been witness to, or incited, these advancements more than John Thompson.

But at the same time, in Building Analytics Teams: Harnessing analytics and artificial intelligence for business improvement, you can sense John's palpable impatience at how so many enterprises continue to take old-school approaches to analytics, or latch onto the latest technology trends with nary a thought given to the necessary introduction of organizational change and team-based approaches. This is where this book differs from others you may have read... or skimmed hoping to find the answers.

As John's and my hometown legend Michael Jordan once quipped, "There's no I in TEAM, but there is in WIN." Analytics has become a team sport, and such teams require strong leadership to win at it. And just as any player on any winning team will tell you, the organization is bigger than just the players. It starts at the top and goes down to the support personnel. With this mindset, John shares his decades of experience, not just in building high-functioning analytics teams, but also in bringing along the entire organization, from the top down.

Moreover, the book thankfully skips past the banality of building basic business intelligence (BI) solutions and focuses exclusively on advanced analytics such as data science and artificial intelligence (AI). As those of us who have been in the business long enough have come to realize, pretty pie charts, beautiful bar charts, and dashing dashboards rarely move the needle on the business.

Over the past several years, I personally have compiled a compendium of over 500 real-world examples of high-value and innovative analytics. Only a handful of these stories were the result of some simplistic hindsight-oriented analytics. Instead, for the most part they involved teams of data curators or "wranglers," data integration professionals, statisticians or data scientists, and visualization experts with an unrelenting passion and unwavering executive support—in pursuit of proving hypotheses, performing root-cause diagnostics, predicting customer and market behavior, and prescribing strategic and operational actions.

Sure, these kinds of projects had an IT component, but they were not IT projects by any means. And if you had any doubt before reading this book as to whether analytics projects belong in the IT department or not, John makes it abundantly clear that they do not. Indeed, it is this straddling of the domains of data, technology, and business that makes analytics projects particularly challenging, and difficult to get right. Especially the contemporary tug-of-war between the traditional and emerging roles of the Chief Information Officer (CIO), Chief Data Officer (CDO), and Chief Analytics Officer (CAO) can create all sorts of unproductive drama. And the often-speculative nature of analytics, that is, hypothesis generation and testing and retesting, can render even the most seasoned leader scrambling for ways to justify projects up-front. Then there's the issue of actually implementing the results. "Mr. Iknowbetter" and "Ms. Thatsnothowwevealwaysdoneit" always seem to appear at the moment when it's time to start applying the new analytics insights.

Organizations are rife with disparate assortments of personas, which for the novice manager can seem to present roadblocks for analytics projects. Yet John shares not only how to understand these personas, but how to harness them inside and outside the immediate analytics team. While "persity" and "inclusion" may be the HR buzzwords du jour, John goes beyond the bromide to explain precisely how inpiduals from different personal and professional backgrounds can coalesce into high-functioning and innovative analytics teams—including, as he notes, hiring bright young people without preconceived notions of what cannot be done. Additionally, the book details different approaches to team operating models such as "factory," "artisanal," and "hybrid" models, and the pros and cons of each, along with sound advice on creating and managing an analytics center of excellence (COE).

While reading this book, I found myself sorrowful for the thousands of analytics leaders who have struggled over the years without this kind of experiential wisdom at their fingertips. At the same time, I'm excited for today's and tomorrow's analytics leaders who, upon reading this book, will surely become the "I" in "WIN" for their organizations.

Douglas B. Laney

Principal, Data & Analytics Strategy, Caserta