TopIntroduction
The growth in use of sophisticated Information Technology continues to generate huge amounts of data. The quantity of data has become so enormous that it is now often referred to simply as “big data.” Conventional wisdom says “big data” has become so big that many organizations are “data rich” but lack the tools and expertise to deal effectively and efficiently with the data, and so end up “information poor.” So how can a business filter through all those data to extract important and meaningful information? Three commonly used terms that describe various collections of methods, tools and techniques used to tame “big data” include Business Intelligence, Business Analytics and Data Mining.
Business Intelligence (BI) is a broad category of processes, methods, measurements, technologies and software applications that gathers, stores, provides access to, and analyzes an enterprise’s raw data. The intent is to help business users cut costs, identify inefficient business processes, identify new business opportunities and make better decisions. BI includes applications and activities like querying and reporting, statistical analysis, forecasting, decision support systems, online analytical processing (OLAP), Business Analytics and Data Mining (Rouse, 2006; McCabe, 2010; Mulcahey, 2013).
Business Analytics (BA) emphasizes the use of statistical analysis to methodically and iteratively investigate organizational data. The intent is to gain insights from the data that will improve decision making (Rouse, 2010). While BA is generally considered to be a subset of BI, sharing many of its methods, tools and outcomes, Sandeep Raut (2011), Director of BI and Analytics at Syntel, indicates a distinction by pointing to BA’s greater emphasis of statistical tools and techniques.
Data Mining focuses on sorting and extracting useful information from large data sets in order to identify patterns and establish relationships (Han, Kamber, & Pei, 2011; Rouse, 2008). It aims to improve organizational decision making and strategic advantage by providing a better understanding of customers, suppliers, employees and other stakeholders. Data Mining is by nature an interdisciplinary field of study, drawing from knowledge domains like statistics, business, economics and IT, and may incorporate a wide variety of tools, techniques and methods including association, classification, clustering, decision trees, forecasting, genetic algorithms, neural networks, predictive analytics, rule induction, and sequence or path analysis (Alexander, 1997; Han 2001; Rouse, 20008).
There is growing evidence that Business Intelligence, Business Analytics and Data Mining will pay off for firms willing to make a commitment and investment. For example, a review of prior research conducted by Evans and Linder (2012) revealed a strong relationship between the use of analytics and a firm’s performance as measured in terms of profitability, revenue and shareholder return. The attractiveness of investment in analytics is also supported by other research such as Nucleus Research’s report (2011) which says that each $1 invested in Business Analytics returns $10.66, and the Information Management article (2002) reporting on an IDC study that found an average 431% ROI on analytics projects.
The expanding use of analytics in business and industry has created a growing demand for qualified analytic personnel. In response, many universities have developed programs or courses in Business Intelligence, Business Analytics or Data Mining. Evidence of academia’s response can be seen at the Web site of KDnuggets, an online community for Data Mining practitioners and students. KDnuggets maintains a page entitled “Education in Data Mining, Analytics, and Data Science in USA/Canada” which lists colleges and universities providing degrees in Data Mining and other forms of analytics. The page currently lists about 50 universities with analytics-related degree granting programs (KDnuggets, 2013).