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How to become an "analytical competitor"
on 22 May 2007
This book is a brilliant development of core concepts in an article co-authored by Thomas H. Davenport and Jeanne G. Harris that originally appeared in the Harvard Business Review. In it and now in this book, they explain how to become an analytical competitor: "an organization that uses analytics extensively and systematically to outthink and outexecute the competition" through support of a strategic, distinctive capability (e.g. Netflix and Wal-Mart), taking an enterprise-level approach to and management of analytics (e.g. Harrah's Entertainment and RBC Financial Group), sustaining a commitment to analytics by senior management (e.g. Jeff Bezos, founder and CEO of Amazon, and Rich Fairbank, founder and CEO of Capital One), and having large-scale ambition (i.e. the aforementioned companies as well as others "bet their future success on analytics-based strategies"), with senior executive commitment "perhaps the most important because it can make the others possible." Davenport and Harris classify companies within five stages of analytical competition:
Stage 1: analytically impaired ("flying blind")
Stage 2: Localized analytics (isolated, fragmented, disconnected, inconsistent, etc.)
Stage 3: Analytical aspirations (sees need, begins to explore options)
Stage 4: Analytical companies (enterprise-wide perspective, eager to innovate and differentiate)
Stage 5: Analytical competitors (analytics are the primary driver of performance and value)
Obviously, the challenge is to become a Stage 5 organization but an even greater challenge is to remain one. According to Davenport and Harris, companies that successfully compete on analytics have analytical capabilities that are difficult to duplicate, unique, adaptable to many situations, better than the competition, and renewable. By design and when utilized, those capabilities must also be able to accommodate all manner of changes within the given competitive marketplace. In some circumstances, in heavily regulated industries or when the analytics support an obsolete business model (e.g. large U.S. airlines such as American and United), analytics are not enough. Still another challenge is to identify those internal applications of business analytics that are clearly strategic and involve competitive advantage.
For me, some of the most valuable material is provided in Chapter 8 as Davenport and Harris explain how to align a robust technical environment with business strategies when incorporating analytics and other business intelligence (BI) technologies into their overall IT architecture. That is, a Stage 5 organization has "a full-fledged analytical architecture that is enterprise-wide, fully automated and integrated into processes, and highly sophisticated." Effective management of data requires correct answers to questions such as these:
1. Which data are needed to compete on analytics?
2. Where can these data be obtained?
3. How much are needed?
4. How can the data be made more accurate and valuable for analysis?
5. What rules and processes are needed to manage data from their creation through their retirement?
Here's another question which at least a few of those who read this review may be asking: Why make such a substantial investment in what it takes to become - and then remain -- a Stage 5 organization? Davenport and Harris provide an answer in the book's final paragraph: "analytical competitors will continue to find ways to outperform their competitors. They'll get the best customers and charge them exactly the price that the customer is willing to pay for their product and service. They'll have the most efficient and effective marketing campaigns and promotions. Their customer service will excel, and their customers will be loyal in return. Their supply chains will be ultraefficient, and they'll have neither excess inventory nor stock-outs. They'll have the best people or the best players in the industry, and the employees will be evaluated and compensated based on their specific contributions. They'll understand what nonfinancial processes and factors drive their financial performance, and they'll be able to predict and diagnose problems before they become too problematic. They will make a lot of money, win a lot of games, or solve the world's most pressing problems. They will continue to lead us into the future."