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Business Intelligence:
The Object of the Game: Continuity

  Column published in DM Review Magazine
November 2001 Issue
 
  By Susan Osterfelt

Is your company built to last? That is the question posed by a thought- provoking book written by Richard Foster and Sarah Kaplan. The book is Creative Destruction: Why Companies That Are Built to Last Underperform the Market - and How to Successfully Transform Them (Currency Books, April 2001). One of the themes of the book is that companies are built with the assumption of continuity - continuous operations. On the other hand, capital markets are built with the assumption of discontinuity - easy creation of companies and then "remorseless" destruction of them when they are no longer able to compete. The result of these two opposing assumptions is that corporations are not able to create value at the pace and scale of the markets. Wow! It's true that the capital markets have indeed been removing weaker players at an ever-increasing rate. Take a look at the average lifetime of a company on the S&P; 500. In the 1920s and 1930s, a new member of the S&P; Index could expect to remain on the list, on average, for more than 65 years. The authors contend that "by the end of 2020, the average lifetime of a corporation on the S&P; will have been shortened to about 10 years." Creative destruction is about capitalism in its purest form.

Creative Destruction seeks to expose "the inherent conflict between the need for corporations to control existing operations and the need to create the kind of environment that will permit new ideas to flourish." The key to survival is innovation. Innovation can enable a company to recreate itself, to shed businesses that don't add value and enter ones that do. For companies to survive, they definitely need to tend to operations and execute well - there is no question about that. However, they need something more - to create an ability to adapt to the new environment (whatever that ends up being).

We need to do well at both operations and innovation, but they are fundamentally different! Operations value what the authors call convergent thinking - decisions that focus on clear, well- defined problems, utilizing deductive reasoning to "zoom in" on the answer. On the other hand, innovation values what the authors call divergent thinking - decisions that focus on getting the questions right (rather than getting to the answers in the fastest possible way) and are the prelude to creativity.

However, it's not just about convergent versus divergent thinking! According to the authors, there are three types of innovation: incremental, substantial and transformational. Transformational innovation is a "historic and irreversible change in the way of doing things," whereas, substantial innovation offers less surprise than transformational, but still "significantly upsets the conventional order." Incremental innovation is the everyday change engine of most corporations, including developing new products and expanding the sales of products to new channels - many times the result of total quality management or reengineering programs. Substantial innovations produce competitive advantage for a period of time and incorporate many novel approaches to products or solutions. Incremental and substantial innovation are absolutely essential to the ongoing operations of the enterprise. Transformation innovation is absolutely essential to its future viability.

Traditional business intelligence, with its reporting, query and online analytical processing (OLAP) tools, supports incremental and substantial innovation, along with convergent thinking. It's a very necessary thing; and it supports "control," whether it is control of costs, control of resource deployment or control of capital expenditures. In a control-focused organization, control means "an informed manager can be reasonably confident that unpleasant surprises will not occur."

A particular form of business intelligence (BI), data mining, can also be used to support "execution" or the incremental improvement of operations as they exist today. Examples of how data mining can help in convergent thinking are customer personalization, product analysis, customer segmentation, "next purchase" analysis and complex logistical problem solving. All of these solutions deal with getting to the most appropriate solution as quickly as possible. However, data mining can additionally be used to support divergent thinking. Examples of this are determining what businesses to exit and potential new businesses to enter. These solutions deal with questions and possibilities, more than with solving particular problems.

Most BI tools support basic operations and execution within an organization; however, in the case of data mining tools, it's not just the tools themselves but how you use them. It takes creativity to use data mining tools for transformational innovation (beyond incremental or even substantial innovation).

Who are the divergent thinkers within your organization? How can data mining help them create ways to keep your company in business for the long haul? The end game is continuity as a business, along with providing BI that can enable our companies to reinvent themselves to stay alive. We need to use data mining tools to help foster creativity and innovation within our companies. Are we up to the challenge? Time and, more importantly, the markets, will tell.

...............................................................................

For more information on related topics visit the following related portals...
Data Mining and Business Intelligence.

Susan Osterfelt is senior vice president at Bank of America, in Charlotte, North Carolina. She can be reached at susan.osterfelt@bankofamerica.com.

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