Information Discovery Inc. has written a lengthy white paper discussing different methods and variables in assessing the dollar value of information [http://www.datamining.com/infoval2.htm]. Following are excerpts of the key points of the paper:
On “Information vs. Data”
– Information is distinct from data and has a much higher value. The process of extracting information from data is data mining. While a database may include the data about the account history of a corporation’s customers, data mining provides information about the characteristics and trends that lead to customer retention and profitability — more valuable than the data itself. The transformation of the data into information significantly increases the value of the data.
On “Methods of Estimating the Value of Information”
– In their approach, the value of information is measured by observing differences in the decision-maker’s performance when provided with different types of information. The basic premise is that information affects performance. The performance of a decision-maker (or an organizational function) is measured prior to the introduction of new information, and compared to the performance thereafter.
– Thus with this method they evaluate two performance levels based on two levels of information availability. The difference in performance levels (measured in dollars) is used as a surrogate for the value of the information obtained.
On “Models of Information Usage”
In order to apply the realistic/operational value of information, you need to evaluate performance. To do so, proceed as follows:
– Build a model of a process which uses information to generate revenue.
– Work out two scenarios: one with a different information content based on the use of a data mining technique, the other without it.
– Measure the revenue difference in the two scenarios as the dollar value of the information.
On “Customer Prospecting”
– Combine knowledge of test mailing responses and profit contributions to design a customer solicitation campaign that will generate substantially better results than those gained by simple response analysis.
– The estimated conversion rate, sales volume, and profit margin of the campaign using the information derived from data mining will be considerably better than the results expected from relatively simple analysis techniques.
