Organizations, both corporate and academic, have been rushing to the table with their own BI groups and programs. Though these groups share a common title, BI, they do not share a common understanding of all that BI comprises. The establishment of their functions follows from the specific strengths and expertise within all of these organizations. This shared limitation was not based on a lack of inclusiveness, but merely a lack of cohesive vision. Further, consider that industry’s definition of business intelligence is by and large quite different from academia’s definition. More so, within industries and within academia, these definitions also vary. The definition of BI seems to depend heavily upon your particular perspective or training. What then is business intelligence...?
Despite the appearance of BI in both academia and industry, until now the field has lacked a clear definition. Not all aspects of BI will be exploited in every situation, but it is still important to know what the future holds. Within this structure, BI was broken down into three significant areas: business information intelligence (BII), business statistical intelligence (BSI) and business modeling intelligence (BMI). Specialists exist in all of these areas, but the importance of the intersection and unions of these areas needs to be emphasized. True intelligence results from the melding of all of these technologies and tools....
Figure 1 presents a cohesive vision of business intelligence as a melding of technologies, models, techniques and practices. The three circles of the Venn diagram each represent areas of study and application that had previously been considered quite distinct: 1. information systems and technology, 2. statistics, and 3. OR/MS [Operations Research/Management Science]. It serves to encapsulate the broadening definition of BI. With this new vision, we may now characterize BI from each of three viewpoints as: business information intelligence (BII), business statistical intelligence (BSI) and business modeling intelligence (BMI). Each of the viewpoints has particular business aspects, and academically speaking, courses that are independent of the other viewpoints. Conversely, each viewpoint can work together or utilize techniques/skills from one or possibly two of the other disciplines. For example, data mining, which requires a high level of statistical knowledge as well as the availably of necessary data, may require significant IT skills and/or knowledge. Further, if data mining analysis demands a systematic process of analysis, modeling skills may be required.
Business analytics (BA), within our framework, is classified as a combination of business statistics intelligence (BSI) and business modeling intelligence (BMI): BA = BSI + BMI. BI is the union of the areas of BA, BI and business information intelligence (BII): BI = BII + BA or more specifically BII + (BSI + BMI). As evidenced in the data-mining example, black and white distinctions between disciplines can quickly become gray.
Note that modeling is a central practice in BI. Follow the link below to read the entire article.
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Source: Klimberg, R K & Miori, V (2010, October), Back in Business, INFORMS, 37(5).
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