Skimming through 20 years of my archived research tonight with a Diet Mountain Dew in hand, I came across an interesting white paper written in 1997 by Petrie Parsaye called “Measuring the Dollar Value of Mined Information“. Estimating potential value and ROI of an analytical project is often a guestimate at best since there are many perspectives, unknowns, subjective opportunity costs and countless other variables to consider. Sophisticated modelers may turn to a prescriptive analytics solution like Frontline Systems Solver for simulating or optimizing complex scenarios. In Parsaye’s work, he illustrates a few simple theories, concepts and patterns to compute the value of decision information that are still being used today.

Analytical decision support systems in a business setting should “deliver value by bringing the internally constructed versions of reality closer to the real world.” By closing the “Perceptive Gap” aka moving away from relying heavily on gut instinct, the effect of decision alternatives can begin to be measured.

Perceptive Gap

Today we may hear the phrase data driven culture used. This concept is similar to Parsaye’s described perceptive and information gaps. Keep in mind that the shift towards a data driven culture does have an undeniable positive organizational impact. In a recent report by the Aberdeen Group, “The Executive’s Guide to Effective Analytics,” data-driven organizations experience a 27-percent year-over-year increase in revenue, compared to 7-percent for other organizations. Additionally, 83 percent improved their process cycle times compared to 39 percent of organizations not classified as data driven, and 12 percent reduced operating expenses from the prior year, compared to 1 percent of other organizations. I am certain that if you searched, you would find many more compelling stats and reasons to embrace a data driven culture.

So how can you measure the impact of providing information to the right people at the right time? Parsaye measures the value of information by observing the differences in decision-maker performance. Basically he is using a type of A/B lift analysis where he models business processes and measures revenue differences.

A/B testing
Technology may have evolved dramatically since this white paper was developed in 1997 but the solution patterns have not changed much. The more things change, the more they stay the same… It is no wonder that I can’t seem to satisfy my insatiable brain these days.