All About Analytics

To understand and be successful with analytics, it is important to be precise in understanding what analytics means, the different targets or approaches that companies can take to using analytics, and the drivers that lead to the use of analytics. For companies that use advanced analytics, the keys to success include a clear business need; strong, committed sponsorship; a fact-based decision making culture; a strong data infrastructure; the right analytic tools; and strong analytical personnel in an appropriate organizational structure. These are the same factors for success for business intelligence in general, but there are important nuances when implementing advanced analytics, such as with the data infrastructure, analytical tools, and personnel. Companies like Amazon.com, Overstock.com, Harrah’s Entertainment, and First American Corporation are exemplars that illustrate concepts and best practices. DOI: 10.4018/jbir.2013010102 14 International Journal of Business Intelligence Research, 4(1), 13-28, January-March 2013 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. BI. Or finally, analytics is the “rocket science” algorithms (e.g., neural networks) and methods used to find patterns in data (e.g., customer segmentation analysis) or to optimize performance (e.g., revenue management). It is useful to think of descriptive, predictive, and prescriptive analytics. With descriptive analytics, the objective is to describe what has occurred. With this view, reporting, OLAP, dashboards/scorecards, and data visualization are all examples of descriptive analytics. These are the core and most common BI applications. Predictive analytics focuses on what will occur in the future. The algorithms and methods for prescriptive analytics include regression analysis, machine learning, and neural networks. These techniques have been around for some time and have traditionally been called data mining. While these methods continue to evolve, the most significant development is their inclusion in analytical workbenches and applications that make them much easier to use. Prescriptive analytics is intended to show what should occur. It is used to optimize system performance. Revenue management, which strives to optimize the revenue from perishable goods, such as hotel rooms and airline seats, is a good example. Through a combination of forecasting and mathematical programming, prices are dynamically set for the good over time to optimize revenues. Another perspective is that the progression from descriptive to predictive to prescriptive analytics is a movement from hindsight to insight to foresight (Barnes et al., 2012). First companies want to understand the past, then they want to predict the future, and then they want to optimize what they do. In most cases, imprecise use of the analytics term does not cause difficulties. There is a problem, however, when discussing the requirements for success with analytics. The requirements for descriptive analytics are different in important ways to predictive and prescriptive analytics. We will refer to predictive and prescriptive analytics as advanced analytics. Returning to the issue of the different interpretations of the analytics term, this article uses analytics to describe the analysis of data and advanced analytics as the “rocket science” algorithms and methods of predictive and prescriptive analytics. With this interpretation, analytics is a subset of BI rather than an alternative term. DIFFERENT TARGETS FOR ANALYTICS Companies can have different “targets” or approaches to analytics. No one target is better for all firms, and each target can be best for a particular company depending on its situation. All of the targets can potentially deliver significant business value. These are the same targets as for BI (Wixom & Watson, 2010). One target is to develop a single or a few analytic applications. These applications are typically departmental solutions and satisfy specific business needs. For example, a company may use analytics to identify customer segments for more targeted marketing campaigns. These applications are not necessarily developed inhouse. There are a growing number of analytic applications and services that are offered by third parties, either as a service over the Internet or on a consulting basis. The services approach is an especially appealing option for smaller companies that do not have the necessary inhouse resources for advanced analytics. A single or a few applications is a common starting point for analytics in most companies. While satisfying a business need, the initial applications can also serve as a proof of concept for analytics. Over time, there are more point solutions and management becomes aware of the need to take a more holistic approach. With enterprise-wide analytics, a company puts resources, organizational structures, and processes around analytics. The infrastructure (e.g., data, software) is created to do analytics on a company-wide basis. Given this infrastructure, analytics is used throughout the organization and is often a key to business success. Later we will discuss the various component parts of this infrastructure. From a BI maturity curve 14 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/all-analytics/76909?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Business, Administration, and Management. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2