On Analytics – Page 2 – Better Insights through Business Analytics

Leveraging Data in the Public Sector

Posted on February 21, 2020 by IU Creative and Web Studio

This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on insights from Tom Preston, who at the time of the article’s publishing, led Booz Allen’s Enterprise Resource Planning (ERP) Advisory Services.

Business analytics has enormous potential to increase efficiencies and improve outcomes in government operations. The federal government alone has a budget of $2 trillion and annual expenditures of approximately $3 trillion; the Department of Defense, even before war supplementals, allocates some $520 billion each year. Federal agencies are always looking for ways to save money by finding more efficient ways to operate. For example, instead of relying on the historical cost of a product or capability, the government is leveraging analytics to model and determine what something should cost and then using real-time dashboards and visualization techniques to assess their performance against industry best practices. In addition to data on these expenses, sensors and radio frequency identification (RFID) are adding date/ time/location stamps to everything from shipped packages to key components of vehicles and aircraft. These many tracking systems offer rich material for business analytics, and government departments are beginning to implement such strategies as:

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Applied Analytics in the Health Care System

Posted on February 12, 2020 by IU Creative and Web Studio

This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

Contributors to this article include: Anwer Khan, Aditya Sane, Vik Wadhwani, and Mark Zozulia, all working with Deloitte Consulting at the time of original publication.

Total health care spending in the US — by individuals, corporations, and federal, state, and local governments — continues to amount to a significant proportion of the GDP, and is expected to reach 19.5% of GDP by 2017. Individual spending averaged $8,233 per year according to a recent report by the Organization for Economic Co-operation and Development. As such, all sectors of the health care industry are actively engaging in addressing cost and quality challenges using business analytics. Health care providers and related hospital-like settings must improve quality while reducing costs. Managed care and health insurance payers also struggle to reduce costs, but attempt to differentiate themselves by launching and administering disease management and wellness programs more efficiently and effectively than their competitors. Life science organizations, meanwhile, are trying to stay ahead of patent cliffs by searching for derivate products across new therapeutic classes to serve more targeted cohorts.

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Using Context to Improve Fraud Tests

Posted on February 12, 2020 by IU Creative and Web Studio

This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

Joseph Cheriathundam contributed contributed content to this article and was with StoneTurn at the time of original publication.

One of the many ways that businesses can use analytics to improve their financial health is in the area of occupational fraud detection. This type of fraud, in which an employee abuses his or her role for personal gain, is estimated to cost organizations an average of 5% of annual revenues, or a worldwide total loss of $3.5 trillion (Association of Certified Fraud Examiners, 2012). There are many sophisticated techniques now used to detect occupational fraud including:

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Estimating Consumer Search Costs in Online Markets

Posted on December 6, 2019 by IU Creative and Web Studio

This article was originally published in the Fall 2012 edition of OnAnalytics, published by the Institute for Business Analytics at Indiana University’s Kelley School of Business.

This article focuses on Matthijs R Wildenbeest’s research, “Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior” in American Economic Review.

Theoretical models of consumer search behavior offer two primary frameworks: a fixed sample size search model, in which customers cost-compare across a fixed, predetermined number of retailers, and a sequential search model, in which customers investigate one retailer after another until they find an acceptable price and make a purchase. Both models assume that searching has a cost to the consumer, which factors into the decision to buy or continue searching.

These models were developed in the 1960s and ’70s, however, prior to the advent of the Internet. How do today’s consumers behave online, when searching requires only a few keystrokes?

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