One could argue—and probably easily win the argument—that there has been more change in analytics over the past ten years than at any other time in the history of the world. For that reason alone, a book that provides a clear-eyed assessment of the state of analytics is enormously valuable. This is that book.
Think of all the changes. We’ve moved from an almost exclusive focus on descriptive analytics—means, percentages, bar charts, and the like—to a healthy mix of descriptive, predictive, and prescriptive analytics. These “advanced analytics” not only supply the title of this book, but represent a major change in emphasis, which is shifting to new technologies, new analytical methods, and new approaches to decision-making.
Over the last decade we’ve also discovered big data (for better or worse). While the term has received a lot of hype, it really is noteworthy. The volume, structure, and flow of big data does necessitate some new approaches to storing, processing, and analyzing it. I agree with the authors of this book: we are really talking about “all data” at this point, but some types of data are still easier to analyze than others.
There have also been major changes in the technology environment for analytics. We’ve moved from computers in data centers to computers on our desks to computers in huge racks located far away. We’ve moved from batch analytics on data in a warehouse to streaming analytics on data in an Internet of Things application. Unstructured data, which never fit well into warehouses, has moved into Hadoop clusters and data lakes. Analytical software has moved from proprietary packages to a mixture of open source, traditional proprietary software, and analytical “micro-services” based on open APIs.
Perhaps the most dramatic change in analytics involves the human role played in them. As the authors of this book discuss effectively in , we’ve moved from a world in which human hypotheses govern analytics to one in which many models are generated somewhat autonomously. The authors do an excellent job at letting some hot air out of the hype about cognitive technologies, but they (and I) acknowledge that big changes in analytics are coming soon in this realm. One of the greatest challenges with analytics has always been that the targeted audiences don’t always end up making full use of the analytic technologies they receive. The rise of machine learning and autonomous decision-making may help to address that issue, but the use of these tools raise all sorts of new concerns as well.
All these changes have led to a great need for insight and guidance from managers and professionals who want to employ data-driven decision-making. They will find it here.
Such guidance is particularly important now because another big change in analytics is the “democratization” of analytic technologies. Analytics were restricted in the past to a relatively small group of professionals; now many business users can employ these tools, as they are much more user-friendly. Small and medium-sized businesses can also build analytics capabilities without needing to invest heavily. At the same time, large organizations have increased the scale and scope of their analytics activities and embedded them into operational processes and systems.
I’ve referred to this set of changes as “Analytics 3.0,” after earlier eras involving “artisanal analytics” and big data in startups and online companies. Even now “Analytics 4.0” is rapidly developing, with early adopters moving to machine learning and autonomous analytics. These analytical opportunities have opened up new possibilities for business models and strategies. The disruptive potential of these ideas may be at an all-time high.
So it’s time to read this book, get off the sidelines, and start playing the game. Executives who don’t understand analytics will see their careers limited, and companies that don’t embrace them will find themselves at a substantial competitive disadvantage. This book provides the plays, but you and your teams must execute them.