Competing on Analytics: Updated, with a New Introduction: The New Science of Winning
Thomas Davenport and Jeanne Harris
Harvard Business Review Press (September 2017)
How to make “a strategic shift toward cognitive technologies in general, and machine learning in particular”
Almost everything I know about analytics I have learned from the articles and books co-authored by Tom Davenport and Jeanne Harris. This is an updated and expanded edition of a business classic first published in 2007. They focus on an important lesson from their research: “Extracting value from information is not primarily a matter of how much data you have or what technologies you use to analyze it, though these can help. Instead, it’s how aggressively you exploit these resources and how much you use them to create new or better approaches to doing business.”
Briefly, Davenport and Harris define analytics as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact based management to drive decisions and actions.” Analytics can be descriptive, predictive, prescriptive, and/or autonomous.
More specifically, consider the DELTA model developed by Tom years ago. As he explains, it continues to have five attributes:
1. Data: Analytical companies require integrated, high-quality, and easily accessible data about their businesses and markets.
2. Enterprise: Instead of managing their analytics resources in disconnected silos, highly analytical firms manage these resources — including data, technology, and analysts — in a coordinated fashion throughout the enterprise.
3. Leadership: One of the key factors driving success in analytics is strong, committed leaders who understand the importance of analytics and constantly advocate for their development and use in decisions and actions.
4. Targets: Organizations can’t be equally analytical about all aspects of their businesses, so they need to target specific business capabilities and functions to the extensive use of analytics.
5. Analysts: Analytical organizations succeed in part because they hire and train high-quality quantitative analysts and data scientists.
More information about this DELTA model is provided in Chapter 6.
It is important to keep in mind the most effective use of analytical capabilities “requires good information management capabilities to acquire, transform, manage, analyze, and act upon both external and internal data.” Business leaders must determine the right questions to ask in order to derive the greatest benefit from the answers that are provided by analytics.
For example, as Davenport and Harris explain, “Regardless of the approach, for companies to sustain a competitive advantage, analytics must be applied judiciously, executed well, and continually renewed. Companies that have analytical capabilities are:
o Hard to duplicate: It is one thing to copy another company’s IT applications or its products and their related attributes (such as price, placement, or promotion), quite another to replicate processes and culture.
o Unique: There is no single correct path to follow to become an analytical competitor, and the way every company uses analytics is unique to its strategy and market position.
o Capable of adapting to many situations: An analytical organization can cross internal boundaries and apply analytical capabilities in innovative ways.
o Better than the competition: Even in industries where analytical expertise and consistent data are prevalent, some organizations are just better at exploiting information than others.
o Renewable: Any competitive advantage needs to be a moving target, with continued improvement and reinvestment.
“One caveat: Companies in heavily regulated industries, or in those for which availability of data is limited, will be constrained from exploiting analytics to the fullest.”
Although Davenport and Harris have maintained the first edition’s chapter structure, they offer an entirely new Introduction and revised every chapter, with new content, new examples, and new research. “We’ve also added some content that has been around for a while, but that we hadn’t developed yet when we wrote the first edition.” The DELTA model was introduced in a previously published book, Analytics at Work (2010), co-authored with Robert Morison.
As I began to read this updated and expanded edition I was again reminded of an encounter that occurred years ago when one of Albert Einstein’s faculty colleagues at Princeton gently chided him for always asking the same questions on his final examinations. “Quite true. Guilty as charged. Each year, the answers are different.”
Tom Davenport and Jeanne Harris continue to prepare business leaders to make the best possible decisions based on the best available information in order to help their organizations to achieve and then sustain a competitive advantage. However different the nature, source, and extent of the given data may be each year, the process remains the same.