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What Is Business Analytics?

In this chapter from Business Analytics Principles, Concepts, and Applications with SAS: What, Why, and How, the authors discuss what business analytics and its relationship to business intelligence.
This chapter is from the book

Chapter objectives:

  • Define business analytics.
  • Explain the relationship of analytics and business intelligence to the subject of business analytics.
  • Describe the three steps of the business analytics process.
  • Describe four data classification measurement scales.
  • Explain the relationship of the business analytics process with the organization decision-making process.

1.1 Terminology

Business analytics begins with a data set (a simple collection of data or a data file) or commonly with a database (a collection of data files that contain information on people, locations, and so on). As databases grow, they need to be stored somewhere. Technologies such as computer clouds (hardware and software used for data remote storage, retrieval, and computational functions) and data warehousing (a collection of databases used for reporting and data analysis) store data. Database storage areas have become so large that a new term was devised to describe them. Big data describes the collection of data sets that are so large and complex that software systems are hardly able to process them (Isson and Harriott, 2013, pp. 57–61). Isson and Harriott (2013, p. 61) define little data as anything that is not big data. Little data describes the smaller data segments or files that help individual businesses keep track of customers. As a means of sorting through data to find useful information, the application of analytics has found new purpose.

Three terms in business literature are often related to one another: analytics, business analytics, and business intelligence. Analytics can be defined as a process that involves the use of statistical techniques (measures of central tendency, graphs, and so on), information system software (data mining, sorting routines), and operations research methodologies (linear programming) to explore, visualize, discover, and communicate patterns or trends in data. Simply, analytics converts data into useful information. Analytics is an older term commonly applied to all disciplines, not just business. A typical example of the use of analytics is the weather measurements collected and converted into statistics, which in turn predict weather patterns.

There are many types of analytics, and there is a need to organize these types to understand their uses. We will adopt the three categories (descriptive, predictive, and prescriptive) that the Institute of Operations Research and Management Sciences (INFORMS) organization (www.informs.org) suggests for grouping the types of analytics (see Table 1.1). These types of analytics can be viewed independently. For example, some firms may only use descriptive analytics to provide information on decisions they face. Others may use a combination of analytic types to glean insightful information needed to plan and make decisions.

Table 1.1 Types of Analytics

Type of Analytics



The application of simple statistical techniques that describe what is contained in a data set or database. Example: An age bar chart is used to depict retail shoppers for a department store that wants to target advertising to customers by age.


An application of advanced statistical, information software, or operations research methods to identify predictive variables and build predictive models to identify trends and relationships not readily observed in a descriptive analysis. Example: Multiple regression is used to show the relationship (or lack of relationship) between age, weight, and exercise on diet food sales. Knowing that relationships exist helps explain why one set of independent variables influences dependent variables such as business performance.


An application of decision science, management science, and operations research methodologies (applied mathematical techniques) to make best use of allocable resources. Example: A department store has a limited advertising budget to target customers. Linear programming models can be used to optimally allocate the budget to various advertising media.

The purposes and methodologies used for each of the three types of analytics differ, as can be seen in Table 1.2. These differences distinguish analytics from business analytics. Whereas analytics is focused on generating insightful information from data sources, business analytics goes the extra step to leverage analytics to create an improvement in measurable business performance. Whereas the process of analytics can involve any one of the three types of analytics, the major components of business analytics include all three used in combination to generate new, unique, and valuable information that can aid business organization decision-making. In addition, the three types of analytics are applied sequentially (descriptive, then predictive, then prescriptive). Therefore, business analytics (BA) can be defined as a process beginning with business-related data collection and consisting of sequential application of descriptive, predictive, and prescriptive major analytic components, the outcome of which supports and demonstrates business decision-making and organizational performance. Stubbs (2011, p. 11) believes that BA goes beyond plain analytics, requiring a clear relevancy to business, a resulting insight that will be implementable, and performance and value measurement to ensure a successful business result.

Table 1.2 Analytic Purposes and Tools

Type of Analytics


Examples of Methodologies


To identify possible trends in large data sets or databases. The purpose is to get a rough picture of what generally the data looks like and what criteria might have potential for identifying trends or future business behavior.

Descriptive statistics, including measures of central tendency (mean, median, mode), measures of dispersion (standard deviation), charts, graphs, sorting methods, frequency distributions, probability distributions, and sampling methods.


To build predictive models designed to identify and predict future trends.

Statistical methods like multiple regression and ANOVA. Information system methods like data mining and sorting. Operations research methods like forecasting models.


To allocate resources optimally to take advantage of predicted trends or future opportunities.

Operations research methodologies like linear programming and decision theory.

Business intelligence (BI) can be defined as a set of processes and technologies that convert data into meaningful and useful information for business purposes. Although some believe that BI is a broad subject that encompasses analytics, business analytics, and information systems (Bartlett, 2013, p.4), others believe it is mainly focused on collecting, storing, and exploring large database organizations for information useful to decision-making and planning (Negash, 2004). One function that is generally accepted as a major component of BI involves storing an organization’s data in computer cloud storage or in data warehouses. Data warehousing is not an analytics or business analytics function, although the data can be used for analysis. In application, BI is focused on querying and reporting, but it can include reported information from a BA analysis. BI seeks to answer questions such as what is happening now and where, and also what business actions are needed based on prior experience. BA, on the other hand, can answer questions like why something is happening, what new trends may exist, what will happen next, and what is the best course for the future.

In summary, BA includes the same procedures as plain analytics but has the additional requirement that the outcome of the analytic analysis must make a measurable impact on business performance. BA includes reporting results like BI but seeks to explain why the results occur based on the analysis rather than just reporting and storing the results, as is the case with BI. Analytics, BA, and BI will be mentioned throughout this book. A review of characteristics to help differentiate these terms is presented in Table 1.3.

Table 1.3 Characteristics of Analytics, Business Analytics, and Business Intelligence



Business Analytics (BA)

Business Intelligence (BI)

Business performance planning role

What is happening, and what will be happening?

What is happening now, what will be happening, and what is the best strategy to deal with it?

What is happening now, and what have we done in the past to deal with it?

Use of descriptive analytics as a major component of analysis




Use of predictive analytics as a major component of analysis



No (only historically)

Use of prescriptive analytics as a major component of analysis



No (only historically)

Use of all three in combination




Business focus




Focus of storing and maintaining data




Required focus of improving business value and performance




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