Jan/Feb 2011

In a recent article in Interfaces, the authors (Liberatore and Luo) point to four driving factors behind the analytics movement [1]. One factor seems to be that a “focus on processes in many organizations has shifted demand for decision support and problem solving away from human decision-makers to processes.” And yet, Liberatore and Luo note, competing on analytics still requires well-rounded analysts: “In analyzing more than a thousand O.R. [operations research] job advertisements, Sodhi and Son (2008) find that employers value strong technical skills but also seek an array of soft skills.”

• Freeman Marvin
• William Klimack

The financial crisis, Hurricane Katrina and the BP oil spill have aroused many claims that quantitative risk analysis failed. Even fairly sober scientific reviews seriously criticized models in general [Scientific American, 2008] or called for a major change in their use [Lo, 2009]. Douglas W. Hubbard’s book on failures of risk management [2009] has been a popular hit, raising the most intriguing question of why modelers don’t usually apply quantitative measures to how well their models worked. (He and this reporter followed up on his main points in an article for OR/MS Today [Hubbard and Samuelson, 2010]). Even more popular and more trenchant is Nassim Taleb’s “The Black Swan,” based on the assertion that the most important events are the “unknown unknown” ones, beyond any modeling method’s scope [Taleb, 2007]. These critiques raise major questions about the uses and limitations of risk analyses, and about the ways in which managers expect to use them.

• Doug Samuelson

Experience is a valuable asset, but how do organizations capture and organize such assets for use in decision-making?

• Fiona McNeill

The growing effectiveness of digital media has changed the way marketers interact with customers. New age customers are informed and empowered. The challenge for organizations is to be a part of the customers’ conversations involving their product and to influence their choices. Hence, the adoption of digital channels such as social media, e-mail marketing and online search and display ads is growing steadily. What helps marketers get the most out of their digital marketing strategies is the ability to capture and mine data exhaustively and cost effectively through advanced analytics techniques. These techniques shed light on the effectiveness of all marketing activities, thus helping marketers fine-tune their strategy. In the long run, this enhances customer engagement, optimizes digital “marketing spend” [1] and has a direct impact on revenues.

• Arnab Chakraborty
• Prithvijit Roy
• Pritam Paul
• Girish Srinivasan

The other day, my analytical team at a major telecommunications company and I were presenting an all-new behavioral segmentation to one of our senior executives. Seeing the first slide, the executive jumped out of his seat in excitement and exclaimed, “Some of these insights took me years to uncover after repeated trial and error and hundreds of BI (business intelligence) reports.”

• Talha Omer

While return on investment (ROI) is a long used measure, Radio Frequency Identification (RFID) is a technology that has re-emerged since Wal-Mart started pushing it on its suppliers in 2003. More recently, Wal-Mart announced that it is going to use RFID intensively in the sale of merchandise (Wailgum, 2010). Taken together, many realists and doubters have wondered whether RFID has a ROI.

• Jerry Banks
• Federico Trigos