Sample these statements:
- Dow Jones Industrial Average jumped 200 points today, a 2% increase from the previous close
- The carbon footprint of an average individual in the world is about 4 tonnes per year which is a 3% increase over last year
- The number of unique URL’s as on July 2008 in the World Wide web is 1 trillion. The previous landmark of 1 billion was reached in 2000
- One day 5% VaR (Value at Risk) for the portfolio is $ 1 Million as compared to the VaR of $ 1.3 Million a couple of weeks back
Most of us buy into the idea of having a single number that encapsulates complex phenomena. Though the details of the underlying processes are important, the single number (and the trend) does act like a bellwether of sorts helping us quickly get a feel of the current situation.
As a
BI practitioner, I feel that it is about time that we formulated a way for valuing the
BI infrastructure in organizations. Imagine a scenario where the Director of BI in company X can announce thus: “The value of the BI system in this organization has grown 15% over the past 1 year to touch $50 Million” (substitute your appropriate currencies here!).
The core idea of this post is to find a way to “scientifically put a number to your data warehouse”. Here are a few level setting points:
- Valuation of BI systems is different from computing the Return on Investment (ROI) for BI initiatives. ROI calculations are typically done using Discounted Cash Flow techniques and are used in organizations to some extent
- More than the absolute number, the trends are important which means that the BI system has to be valued using the same norms at different points in time. Scientific / Mathematical rigor helps in bringing the consistency aspect.
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My perspective to valuation is based on the “Outside-in” logic where the fundamental premise is that the value of the BI infrastructure is completely determined by its consumption. Or in other words, if there are no consumers for your data warehouse, the value of such a system is zero. One simple, yet powerful technique in the “Outside-in” category is RFM Analysis. RFM stands for Recency, Frequency and Monetary and is very popular in the direct marketing world. My 2-step hypothesis for BI system valuation using the RFM technique is:
- Step 1: Value of BI system = Sum of the values of individual BI consumers
- Step 2: Value of each individual consumer = Function (Recency, Frequency, Monetary parameters)
Qualitatively speaking, from the business user standpoint, one who has accessed information from the BI system more recently, has been using data more frequently and uses that information to make decisions that are critical to the organization will be given a higher value. A calibration chart will provide the specific value associated with RFM parameters based on the categories within them. For example: For the Recency parameter, usage of information within the last 1 day can be fixed at 10 points while access 10 days back will fetch 1 point. I will explain my version of the calibration chart in detail in subsequent posts. (Please note that the conversion of points to dollar values is also an interesting, non-trivial exercise)
Am sure that people acknowledge the fact that valuing data assets are difficult, tricky at best. But then, lot more difficult questions on nature and behavior have been reduced to mathematical equations – probably, the day on which BI practitioners can apply standardized techniques to value their BI infrastructure is not too far off.
