Posted by: A.R. Cherian | March 2, 2010

Business Intelligence – competing on analytics

In my previous two posts I talked about Business Intelligence (BI) and why it can be valuable to any business. I listed some companies that are using BI currently to gain a competitive advantage over their competitors. All those companies and thousands like them use BI to “compete on analystics.” BI runs a full spectrum from simple reports (which almost every company can generate) to full-bore statistcal analysis, prediction, and optimization. You can think of this as a BI ladder.

In this post I talk about what it means to compete on analytics and the benefits and drawbacks of doing so.

“Competing on analytics” was coined by Thomas Davenport and Jeanne Harris in their 2007 book of the same name. I read the first two chapters of the book and so far it is great read for any business leader thinking of employing BI.

What does competing on analytics mean?

  • Davenport and Harris describe in their book Competing on Analytics (2007) that competing on analytics means “extensively using data, statistical analysis, explanatory and predictive models, and fact-based management to drive business decisions and actions.”
  • Gaining a competitive advantage by executing your business with maximum efficiency and effectiveness and to make the smartest business decisions possible.
  • Being careful not to only equate analytics with software and hardware. It’s involves management style and organizational structure changes as well to truly differentiate itself.
  • In layman’s terms, it’s a system put in place in your business that forces decisions to be made with some supporting facts behind it rather than on gut feeling alone.

Benefits of competing on analytics

  • “With so many things becoming commoditized, business processes are among the last remaining points of differentiation and competition” – Davenport and Harris (2007).
  • Eliminate guesswork from processes and business models.
  • Reduce costs (such as Walmart, UPS).
  • Customize products and services (such as Netflix, Amazon).
  • Revenue enhancement.
  • Increase analyst productivity – now your statistical analysts can spend 20% of the time gathering the data and 80% analyzing it, instead of the other way around.
  • Fraud reduction (such as the custom algorithms many credit card companies and banks use).
  • Increase customer conversion rates – turn shoppers into buyers.
  • Decrease customer attrition – create loyal customers and win back former customers.

Drawbacks of competing on analytics

  • If time is limited to make a decision, you have to proceed with your gut sometimes. But research shows that intuition is a good guide to action only when it is backed by many years of experience.
  • Any analysis relies upon a series of assumptions. If the assumptions no longer apply, the analyses would not give accurate models. For example: credit card companies predicting willingness to pay based on good economic climates. An economic downturn can negate this assumption.
  • Takes a lot of time and money! Can take years to come to fruition. Needs extensive groundwork laid first (such as when Harrah’s started laying the software and hardware groundwork for their Total Rewards loyalty system 20 years before they started using it for BI).
  • Ongoing commitment. People have to use it and money and time have to be spent in maintaining the system.

Those are all I could think of. If you have any more advantage or disadvantages in using BI to compete on analytics, let me know.

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