July/Aug 2012

Competition in analytics is a familiar concept on an organizational level. An “analytics gap” exists between the predictive haves and have-nots: an insurance company that accurately predicts a new customer’s actuarial risk, a mortgage lender that better predicts the probability of default, a retailer that better predicts its churn rate, a social network its number of followers or an ad platform its click-through rates. If any of these companies can accomplish their predictions better than the rest of their industry, they gain a distinct competitive advantage – survival of the analytic fittest.

• Margit Zwemer

Forecasting is the foundation of virtually all business planning. Scheduling production requires a demand forecast. Deciding between short- and long-term investments requires a cash flow forecast. You need a customer usage forecast in order to buy enough servers to support a growing cloud-based software application. The list of forecasts that a company must prepare is endless and, moreover, the forecasts are intertwined.

• Brian Lewis

Time series data is frequently collected and analyzed, and then forecasts are made, often with clever headlines or leads. These vary from the important to the mundane. Recently, we have seen time series data and forecasts for unemployment figures, gas prices, mortgage loan rates, stock indices, approval values for President Obama vs. Mitt Romney, high school science and math scores, television ratings for “Dancing With the Stars” and many other examples.

• Jack Yurkiewicz

A quick quiz: What is a good nine- or 10-letter description of the emerging interest in business analytics and big data that ends in “-al”?

• Gary Cokins

In 1951 the celebrated British hydrologist H.E. Hurst published a paper titled, “The Long-Term Storage Capacity of Reservoirs.” The paper dealt specifically with the modeling of reservoirs, but as it turned out, the results also held valid for a number of other natural systems. Hurst was looking for a way to model the levels of the river Nile so that architects could construct an appropriately sized reservoir system. While his recommendations were not implemented (the 1952 Egyptian revolutions saw to that), he gave life to a statistical methodology for distinguishing random from non-random systems and to identify the persistence of trends, a methodology known as Rescaled Range analysis or R/S analysis.

• Subir Mansukhani

The IBM Corporation has a long and deep history as a company that both uses operations research and, more generally, advanced analytics to run its business. It is also a place where practitioners and innovators in the field have chosen to work for decades. Many important contributions to the field have been made over the years, and those contributions continue to this day.

• Arnold Greenland