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5/16/2007
01:07 PM
Rajan Chandras
Rajan Chandras
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BI and the Law of Diminishing Differentiation

I recently read a good book that explains how to use analytics to gain competitive edge. The book got me thinking on what constitutes "competitive advantage" in the context of BI. All technology has the potential to provide competitive advantage… until, that is, your competitors use it too, at which point it becomes a commodity. But that's not the case with BI. Here's why.

I recently read a good book that explains how to use analytics to derive competitive edge. The book is brimming with success stories in analytics-driven competition and is well worth the price (though I will give away a free copy of the book to one lucky winner - see drawing details below). The book also got me thinking about what constitutes "competitive advantage" in the context of BI.

All technology has the potential to provide competitive advantage…until, that is, your competitors also begin using the technology, at which point it becomes a commodity. Companies are then faced with two options, and most companies choose both: discover ways to get more from the technology and find other technologies to exploit. We have seen this happen ad infinitum. Barcodes, ERP, data warehousing, geo-spatial applications - technology provides the differentiation, only until others catch up. (Which raises the interesting question: perhaps technology can also provide a competitive disadvantage, e.g. RFID? But that's another story.)At first sight, it would seem that BI also adheres to this Law of Diminishing Differentiation, but that's not the case. In these never-ending cycles of technology-driven competition - not unlike the Mobius strip where now you are on the "inside" of that elusive competitive edge and now you are on the "outside" of it - what makes analytics technology unique is that nobody can ever take away your advantage, the simple reason being that nobody else has your data. Only you have that set of current and historical data across your product and service lines, and short of stealing it (or getting into a collaborative arrangement, e.g. through data aggregators) nobody will ever get to see it or learn from it. The learning and intelligence you derive from the data, the strategies that the data will suggest (and decimate) are exclusively yours. Your data is your crown jewel and USP combined in one: its value cannot be overstated, and nobody has or can ever create one just like that.

But real-life examples are so much better than theory or pontification, and you can find an impressive variety of examples of companies that have used analytics to stand up to or get the better of competition, in a recently published book titled "Competing on Analytics: The New Science of Winning" from Harvard Business School Press. (Although the book is new, the theme is not, and you will find a Harvard Business Review article with a similar title by one of the authors written early last year.) In particular, three of the chapters that relate analytics to business performance, internal business processes and external processes make compelling reading.

The book tells some good stories, but there are others waiting to be told. Do you have a story in applying analytics to gain competitive mileage that you would like to share with us? Write to me with your experience, and I will enter you in the lucky draw. The winner gets the book free, courtesy of the Harvard Business School Press. Write to me at rchandras@gmail.com and be sure to include your name, title, company name and corporate e-mail address (I may need some additional clarification on success stories and, of course, the mailing address for the winner of the drawing).I recently read a good book that explains how to use analytics to gain competitive edge. The book got me thinking on what constitutes "competitive advantage" in the context of BI. All technology has the potential to provide competitive advantage… until, that is, your competitors use it too, at which point it becomes a commodity. But that's not the case with BI. Here's why.

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