By James R. Evans

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These include: queries and analysis • Database to report key performance measures • “Dashboards” visualization • Data methods • Statistical and predictive models • Spreadsheets and “what-if” analyses • Scenario • Simulation 37 38 Chapter 1 Introduction to Business Analytics • Forecasting and text mining • Data Optimization • Social media, Web, and text analytics • Although the tools used in descriptive, predictive, and prescriptive analytics are different, many applications involve all three. Here is a typical example in retail operations.

Data may be classified into four groups: 1. Categorical (nominal) data, which are sorted into categories according to specified characteristics. For example, a firm’s customers might be classified by their geographical region (North America, South America, Europe, and P ­ acific); employees might be classified as managers, supervisors, and ­associates. The categories bear no quantitative relationship to one another, but we usually assign an arbitrary number to each category to ease the process of managing the data and computing statistics.

A ­database file is usually organized in a two-dimensional table, where the columns ­correspond to each individual element of data (called fields, or attributes), and the rows represent records of related data elements. A key feature of computerized databases is the ability to quickly relate one set of files to another. Databases are important in business analytics for accessing data, making queries, and other data and information management activities. Software such as Microsoft Access provides powerful analytical database capabilities.

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