Nov/Dec 2011

The intersection of statistically based insight and the realization that information can be an asset has had and will continue to have serious reverberations in the business world. Being smarter has always meant being successful; as far back as the 19th century, analytics was already generating competitive advantage.

• Evan Stubbs

Decision-makers do not lack information, although they often complain that they lack the information they need. We are awash in data and have increasing trouble making sense of it. Artificial intelligence (AI) has been promising to help us for four decades. Computers are fast at processing large amounts of relevant data following human-developed rules, but they struggle to turn data into information and information into evidence that makes sense of our situation. What we need is intelligent augmentation (IA), combining humans and computers to make sense of what data we have by using the strengths of each.

• Russell R. Vane III

Practically all organizations achieve their objectives by building a portfolio of activities subject to budgetary and other relevant constraints. Industrial firms, for example, allocate resources to research and development projects (R&D), expecting that these projects lead to profit-generating products. Municipalities commit funds to initiatives that offer social and educational services to citizens. Regulatory agencies impose controls to counter threats to human safety and environment. Even individual decisions can be viewed analogously. For instance, students must decide which courses and recreational activities to take on, realizing that time is a limited resou

• Ahti Salo
• Jeffrey Keisler
• Alec Morton

The application of analytics in financial services is advanced and pervasive; the use of credit risk scoring techniques permeates the entire customer lifecycle management – from credit grant to account management to loss reduction and recovery. Without automated credit scoring techniques, the large-scale, modern day consumer credit industry as we know it would not exist. By comparison, analytics in consumer retail marketing is still relatively undeveloped. Except in the area of direct marketing, where consumer response-based targeting models are widely used and well accepted, analytics applications in other areas of the business is less systematic and varies widely across companies and sophistication of executives in charge of marketing. This article briefly reviews the successful evolution of analytics in the consumer credit business, while exploring four areas in consumer retail marketing that are not as well developed but hold great potential for substantial business impact.

• Ming Zhang
• Clay Duan
• Arun Muthupalaniappan

In today’s turbocharged digitized world, there seems to be an “application” (“app”) for almost everything. The intent of the app software is to help the user perform singular or multiple related specific tasks on a repetitive basis. In the field of analytics and decision analysis, the apps analogy can be made when we think about the entire list of “hard and soft” skills that are used when we are working to bring discipline and analytical rigor to business decision-making.

• David Leonhardi

Predictive models are a key component of every effective sports analytics program because these models translate raw data into useful information. For example, there is very little value in the motion capture data described in Part 1 without skilled analysis that transforms that data from millions of raw records into actionable information a decision-maker can understand and trust. Data sources alone are clearly unusable, as no decision-maker would be able to draw conclusions from these mountains of raw data. Once such data is analyzed, however, the analysis results have the potential to become a valuable and unique tool to aid decision-makers in making better decisions.

• Benjamin Alamar
• Vijay Mehrotra