Book Review – Analytics at Work

I received a pre-release copy of Tom Davenport’s new book Analytics at Work: Smarter Decisions, Better Results. The book is a follow-on to Competing on Analytics (reviewed here) and is a shorter, pithier book than its predecessor. Once again Tom collaborates with Jeanne Harris and this time Robert Morison of the Concours group. Where the previous book focused on so-called analytic competitors, this is about “analytics for the rest of us”. It is a very readable book with some good practical advice that does not require the remaking of your company in a new image. It is also a quick read, it is only 180 pages or so, which should help get more people to read it.

And I hope people do read it. As Tom says “The unexamined decision isn’t worth making” and too many companies and organizations are making unexamined decisions, failing to apply data they have about what works and what does not, making the same mistakes over and making dumb decisions. Like Tom I think it is time for this to stop and this book will tell you how.

In the initial chapter, the book outlines the difference between areas with a history of analytic decision making and those where it is new – performance metrics may be progress in the latter but something like customer segmentation and treatment requires more advanced analytics to score and segment them. It’s important to remember this, to find the right degree of analytic sophistication to make a difference. The book’s focus is broad, covering how analytics can address key questions of information and insight in each of the past, present, future – reporting, alerts and forecasting give information in the past, present and future while modeling, recommendations and predictions/optimization do the same for insight.

For me the most useful part of the book is part one – a set of chapters describing The Analytic DELTA – Data, Enterprise, Leadership, Targets and Analysts – what Tom regards as the 5 critical elements of successful analytic adoption:

• D – accessible, high quality data
  I particularly like the focus on uniqueness as a criteria and on using the business need (decision) to drive quality and integration needs – being with the decision in mind. Focusing BI/analytics people on the quality of decisions they enable not on the data they manage like Humana’s “advocate of all matters quantitative” who relentlessly improves “corporate decision making efforts”.
• E – enterprise orientation
  The point here is not to focus on fractured analytic projects but on coherent ones across the enterprise. Enterprise-serving projects not self-serving ones. The authors make the great point that getting value from your enterprise applications means anticipating how to use the information they provide to improve performance.
• L – analytical leadership
  An organization’s leaders must care about analytical decision making, especially where it is multiplicative and delivers leverage (in highly repeatable operational decisions, for instance, where the improvement in decisions is multiplied across all your transactions).
• T – strategic targets
  A crucial element, that of focusing on using analytics to develop distinctive capabilities. This chapter has a great list of processes that lend themselves to analytics because they are data rich, asset or labor intensive, dependent on speed or consistency and more. The focus on decisions that are complex or ca be optimized, where consistency is required and those done poorly today is spot on. The “ladder of analytic applications” is a great tool for seeing how to develop from simple to more complex analytic solutions working from getting your data in order to segmentation and differentiation, becoming predictive, institutionalizing and finally optimizing. Interestingly this sequence matches exactly the pattern I have seen in research I have been doing for IBM on analytic journeys.
• A – analysts
  A nice chapter with good thoughts on how to manage analysts as a strategic resource.

Part two addresses how to stay analytical through embedding analytics in business processes, building an analytic culture, reviewing your business comprehensively and embarking on an analytical journey towards “more analytical decisions and better results”. I really like the focus on embedding analytics in business processes – this is a topic close to my heart – and like the authors agree that the use of analytics is especially valuable in workhorse or operational processes. The authors do a nice job of explaining why organizations need to adopt a test and learn mindset, to be always unsatisfied and mindful of change and to focus on an “industrial” analytic process.

While Tom and I disagree over the extent to which analytics can be used to drive fully or mostly automated decisions, we are in synch on his definition of nirvana – an organization that knows its decision points, relies on analytics, integrates them into its operations and monitors performance to close the loop. And one that MAKES DECISIONS AND TAKES ACTIONS using analytics – one that realizes it is not enough to just analyze its data.

The authors end by pointing out that becoming analytic is not a one-time activity but must be ongoing – it is a journey which organizations must begin, where they must build momentum and where they must go from thinking of analytics to thinking about decisions and decision making, from analytic management to decision management.

It’s a great book and you should buy it.