PROPOSAL FOR RESEARCH: BUSINESS INTELLIGENCE. Paul Gray Visiting Professor of Information Systems, GSM or

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1 PROPOSAL FOR RESEARCH: BUSINESS INTELLIGENCE Paul Gray Visiting Professor of Information Systems, GSM or

2 WHAT IS BUSINESS INTELLIGENCE McGeever (2000) defined business intelligence in Computerworld as: the type of granular information that line-of-business managers seek as they analyze sales trends, customer buying habits and other key performance metrics of an organization. Fuld and Sawka (CIO, 2000) define it as: Information that has been analyzed to the point where a person can make a decision As indicated by these definitions, business intelligence refers to a portion of what used to be called Decision Support Systems (DSS). Decision support classically involves three components: a data base, a model base, and an interface. Over the years, the emphasis of DSS research and practice was on the last two components. However, with the coming of the data warehouse in the 1990 s, development started focusing on the previously neglected data base component. Over time, this data base emphasis was given the name business intelligence (as in McGeever s definition). It has also been recognized that business intelligence only makes sense if it is coupled to analytics that help managers understand the implications of the data that they see. That is, the data base requires analytic models to make it useful to managers. Many of the discussions of business intelligence (such as Fuld and Sawka s) assume that the data and the modeling function are combined within the concept of business intelligence. Another area of divergence is just what is covered by business intelligence. For example, some argue that, at its core, it is really competitive intelligence. That is, it is used to anticipate external forces, prevent countless bad decisions, and reap advantages over current and future competitors" (Bresnahan 1998). Still others (e.g., Osterfeld, 2000) think of business intelligence as the input to customer relationship management. Business intelligence is a big business. The many vendors who previously offered decision support or executive information systems now offer business intelligence as their product. One estimate reported by Fulk and Sawka (2000) forecasts $100 billion in sales over the next few years. The authors point out that this optimistic estimate includes existing sales in data warehousing and data mining. Nonetheless it is quite true that business intelligence is a major product category that involves large sums and many people. BRIEF REVIEW OF WHAT IS KNOWN Not surprisingly for a new topic, almost no academic research has been published on business intelligence. Only one reference (Teo and Choo, 2001) was found in the leading information systems and decision support journals over 2

3 the last five years 1.Yet, the subject of business intelligence is all over the trade press. Significant coverage can be found in CIO, in Intelligent Enterprise, in DM Review, in the publications of the Data Warehousing Institute, and in weeklies such as Computerworld and Information Week. The conventional wisdom is that there is a four-step cycle that involves business intelligence. Exactly what these steps are differs slightly from author to author. Typical is Fuld and Sawka (2000) who divide the process into: 1. Define the problem. 2. Collect and organize the data and put it into an analytic context 3. Analyze and recommend action 4a. Report the findings and inform the organization 4b. Evaluate and refine. From a more data-oriented and CRM point of view, the Data Warehousing Institute divides the process into: 1. Capture customer and market information, cleanse it, and store it in the data warehouse. 2. Analyze the information in the warehouse through query, reporting, OLAP, analytics, and data mining tools 3. Planning, modeling, and evaluating customer campaigns and interactions. 4. Interact to manage inbound and outbound communications with the customer using the business rules, analyses, customer data, etc. obtained in the previous portions of the cycle. Business intelligence software at this point performs only rudimentary quantitative analysis of numbers and statistics. It is often based only on internal data. Yet, decision and action requires looking at qualitative as well as quantitative results. That is not to say that there is no role for information systems in true business intelligence. Rather, their role is limited in many parts of the business intelligence cycle. Thus, in step 1 - defining the problem -, most of the work is human. Information systems can answer specific queries that help humans in problem definition but they cannot anticipate questions that have not been asked. In collecting and organizing, which is a strong area, there are still limits that result from information that is not captured in electronic form or in relating multiple sources to one another. In analysis, information systems that deal with spatial analysis, timeline analysis, and relationships are just now coming on the scene. For example, visualization and geographic information systems help provide spatial analysis. Business intelligence is now recognized as being part of a constellation of 1 The journals consulted were MIS Quarterly, Information Systems Research, J. of Management Information Systems, Information and Management, and Decision Support Systems. 3

4 applications that were developed in the 1990 s that interact with one another. They include data warehousing, customer relationship management, data mining, AREAS FOR RESEARCH Prepare a detailed survey of the state of the art of business intelligence there doesn t seem to be a good one available in the academic literature or one that is not connected with a particular product. Most existing overviews are a few pages in the trade press or in corporate white papers. Perform a series of case studies with individual firms to start to understand the different (and similar) approaches being used. This work would begin by working with IAB firms who agree to participate and would provide each firm direct feedback based on their own operations. The goal of this work is to determine best practices in business intelligence. Examine and evaluate the new technologies and approaches being offered by business intelligence vendors and/or proposed by academic researchers to determine whether they have value for the business intelligence function. Among the current ideas are: Knowledge management applied specifically to business intelligence. Knowledge management introduces the qualitative component to complement the quantitative. Wireless to give access to people out in the field access to the business intelligence data available internally The application of business intelligence ideas to electronic commerce Applying data mining ideas Interacting with the supply chain Expanding IT support of business intelligence data beyond data warehouses to include document management. Data visualization to improve the interface for the manager Distributed query management, enterprise application integration, portals, and applications servers to speed responsiveness 4

5 POTENTIAL VALUE TO IAB FIRMS 1. Gain an understanding of how their business intelligence operations compare to the state of the art and to best practice. 2. Inform IAB firms about potential new applications of technologies and techniques to their business intelligence operations and, in some cases, to their product line. QUESTIONS FOR DISCUSSION 1. How do you integrate the qualitative and the quantitative aspects of business intelligence? 2. How can the knowledge management view improve business intelligence? 3. What is the role of text in business intelligence? 4. Is the zero latency enterprise (ZLE) approach, (collecting and analyzing information in real time for tactical decisions) feasible? Is it compatible with the present data warehouse/operational data store world? 5. How can business intelligence software be scaled to cope with the volumes of data involved in B2C e-business.? REFERENCES Bresnahan, J. (1998) Legal Espionage CIO Magazine (July 15) Fuld, L.M. and K. Sawka(2000) Money Can t Buy You Smarts CIO Magazine (Oct. 1) McGeever, C. (2000) Business Intelligence Computerworld (July 24) Osterfeld, S. (2000) Business Intelligence: No Guts, No Glory DM Review (October) Thompson S.H. Teo and Choo, W.Y. (2001) Assessing the Impact of Using the Internet for Competitive Intelligence. Information and Management Vol. 39, Issue 1, November 2001 pp