Applicability to Business Analytics of New Science Discoveries in Stem-Cell Biology and Astrophysics

In the last week, there were two major scientific discoveries: scientists “trick” cells to change identities; and there are three times more stars in the universe than previously thought. What are the lessons than we can learn and apply from these discoveries?

First, the methodology used by scientists for transforming a specialized cell into a different specialized cell is a “reverse and conversion “approach. In other words, molecular biologists reversed the process of a specialized cell development and turn that cell into a basic or stem-cell. Once this “reverse” step is completed, then the scientists guided the new basic cell to transform into a different specialized cell. In the area of retail and consumer goods/services companies are always coming with new strategies to increase sales and profitability by “converting” a competitor’s consumer into one of their own. In other words, the emphasis is in “conversion”, but there is no prior “reverse” methodology. Business analytics techniques can be used to identify actionable “reverse” methodologies that can be used with conversion methodologies to increase revenues and profitability. In other words, companies must have a methodology to determine how they can get a potential customer to reverse an established purchasing pattern, before attempting to “convert” that customer.

Second, the study that concluded that the number of stars in the universe had previously been undercounted and that the correct number is three times the previous estimate is a case study of applying good science to known facts. One of the reasons for the undercount was the assumption that the distributions of most galaxies are spiral like our own Milky Way galaxy. This recent study found out this to be an incorrect assumption. Moreover, the study found that the assumption that the ratio of dwarf stars to sun-like stars is 1,000 times greater in elliptical galaxies than in spiral galaxies. What is the application of these discoveries in the area of business analytics? There are two main lessons. One, assumptions of patterns, ratios, distribution, and correlations in large data sets based on the observation of a limited set (and non statistically valid random sample) could end up been not supported by reliable scientific methods. Lastly, it is imperative to have a methodology that deals with missing data before reaching conclusions and recommendations.

In summary, companies should be looking at methodologies and discoveries in the sciences and apply that knowledge into their business analytics strategy and tactics.