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  1. 1. BUSINESS INTELLIGENCE: A NEW NAME OR THE FUTURE OF DSS? The ISDSS meetings began in 1991, 12 years ago. Over the years, the focus, rightly, has been on supporting individuals and groups in making decisions, taking new developments into account. In 1997, in Laussane, for example, I talked about “Data Warehouses, OLAP, Data Mining, and the New DSS”. Decision support systems, themselves, go back to the late 1960’s when people started moving past transactions and MIS, to consider how we should support the top of the organization. Our early efforts involved such things as the assumption that managers would use Iverson notation to write their own systems. Later we recognized that top decision makers would not spend their valuable time actually writing programs. By 1982 we had Sprague and Carlson’s (Sprague and Carlson, 1982) insight that DSS consists of three components: • the (analytic) model • the database • the human interface where the model was supported at the input by the data and at the output by the human interface. We also began to realize that for managers, the modeling and analysis was too tough and that they had neither the time nor the interest to actually do modeling. So we changed the paradigm. Managers would use a simplified version of the DSS that gave them outputs from the data – we called them Executive Information Systems (EIS) – that consisted mostly of pretty pictures plus some drilldown capability. As Jack Rockart, I believe, said, senior executives used EIS to find problems and then told their analysts to use DSS to solve them. 1
  2. 2. EISs were the 1980’s. By the early 1990’s it became clear that DSS groups are very good at analysis and interface, but generally were deficient in the database area. Fortunately, in the early 1990’s, data warehouses with their clean, single truth form of data came along and the buzzword was OLAP, a good name for a racehorse, but really standing for on-line data processing. An entire industry was built around these variations on a theme. Vendors ranging in name from Brio to SAS each doing multimillion dollars in business each year came on the scene, and some even survive today. The purpose of this brief retelling of history is not to make you nostalgic for the past, but to indicate that, over the years, DSS assumed many forms, changing with both the technology and our understanding of managerial work and work patterns. Moreover the buzzwords changed. The vendors changed what they produced very slowly, always claiming they had the latest, whether they did or not. I recall one firm turning out a 24 page brochure on their executive information system one year and then turning out a 28 page brochure the next year telling everyone that they supported OLAP. Of course, their product was identical from year to year. if you read both brochures, you saw that nothing had changed. The original 24 pages were reused and a claim was added that they supported OLAP, As we go into the early 2000’s, the same situation obtains. We have two “new”1 buzzwords: Business intelligence Competitive intelligence And in recent months, the term “Business Process Management” has come to the fore. A new buzzword, it looks at developing metrics on how well a business is performing. 1 The term business intelligence goes back to 1989, when it was coined by the Gartner Group 2
  3. 3. which the same vendors are now selling as their prime product rather than DSS, EIS, or OLAP but, when we look under the hood, most of the outputs look the same. At one level, they are selling something new. At another, it is just an update of what you and I have called DSS over the last 30 to 35 years. Let me begin this discussion of what business intelligence is with an interesting, but discouraging piece of data. When I searched the academic literature for business intelligence, I had a hard time finding academic publications. I checked the latest three books on DSS in my library and I found only one, Dan Power (2002) , who mentioned Business Intelligence, and even that is brief. Power, however, makes the important distinction that business intelligence is a form of Data-Driven DSS as distinguished from seven other types of DSS.2 Analyzing business intelligence products and practice shows that existing systems are broader than data-driven but that data-driven use dominates. In terms of academic literature, the number of articles is few. Examples of the references found are shown in Table 1. Of the ten references in the table, eight deal with competitive intelligence, a branch of BI. One of the eight (Rouibah and Old-Ali) deals with competitive intelligence even though it uses business intelligence in its title Table 1. Academic Articles on Business Intelligence and Competitive Intelligence Cody et. al.(2002) Rouach and Santi (2000) Vedder and Guynes (2002) Hall (200) Rouibah and Ould-ali (2002) Wiggins (2001) Markus and Lee (2000) Teo and Choo (2001) Weir (2001) Powell and Bradford (2000) 2 Powers’ complete list includes 10 types of DSS: (1) data-driven, (2) model-driven, (3) knowledge-driven,(4) document-driven, (5) communications-driven and group, (6) inter- and intraorganizational, (7 general purpose or function-specific, and (8) web-based. 3
  4. 4. Thus, although the vendors are pushing it and trade magazines such as Intelligent Enterprise and DM Review are full of it, business intelligence seems to have flown under our radar as academics. My remarks, therefore, are based principally on what is in the trade literature so that you can gain a picture of what practice is doing and to urge you to do research that moves forward from where industry is The purpose of my talk therefore is to tell you what the shouting is all about and to help you to decide whether business intelligence is simply a new name, a repackaging if you will of DSS in a more appealing wrapper, or whether it represents the true future of DSS. WHAT IS BUSINESS INTELLIGENCE Whereas many of our previous efforts were focused on the decision and how it is made, over the years we have come to realize that our real contribution comes from creating the knowledge climate in which decisions can be made. As a group we are analysts at heart—trying to find out what the situation is and presenting it to people who make the choice. Thus, we create knowledge – both about our own firm and about our competition. That knowledge is intelligence about where we have been, where we are, where our competitors are, and most important about where things are moving in our firm, in our competitors, and in the global business and government that is our environment. We use the following as a working definition of business intelligence systems: Business Intelligence systems combine data gathering, data storage, and knowledge management with analytical tools to present complex corporate and 4
  5. 5. competitive information to planners and decision makers. The objective is to improve the timeliness and quality of the input to the decision process Implicit in this definition is the idea (perhaps the ideal) that business intelligence systems provide -actionable information and knowledge -at the right time -in the right location, and -in the right form. Sometimes business intelligence refers to on-line decision making. Most of the time, it refers to shrinking the information time window so that the intelligence is still useful to the decision maker when the decision time comes. In all cases, business intelligence is viewed as being proactive. SO WHAT IS NEW? Two fundamental sea changes have occurred: (1) in the technology and (2) in the DSS process. Let’s begin with the essential components of proactive BI( Langseth and Vivatrat, 2003). 1. real-time data warehousing, 2. data mining 3. automated anomaly and exception detection, 4. proactive alerting with automatic recipient determination, 5
  6. 6. 5. seamless follow-through workflow, 6. automatic learning and refinement 7. geographic information systems (Sidebar 1) 8. data visualization (Sidebar 2) When you look at these enhancements you see that they are the result of advances in technology. For example, -the data warehouse with its single truth, -data mining which lets the data tell you rather than requiring you have a hypothesis -automatic tasks previously done manually or by face-to-face interaction, -AI-based tasks using bots These advances are not just incremental, they are step increases and they change what can be done. SIDEBAR 1 A TECHNOLOGY FOR BUSINESS INTELLIGENCE: GEOGRAPHIC INFORMATION SYSTEMS (GIS) In the narrow sense, a Geographic Information Systems (GIS) is a software package that links databases and electronic maps. At a more general levels, the term GIS refers to the capability for analyzing spatial phenomena. These systems are an important business intelligence tool for exploiting the increasing amount of two (and more) dimensional data available in a form that can be understood by analysts and managers. 6
  7. 7. In addition to collecting, storing, and retrieving spatial location data, GIS are used to identify locations which meet specified criteria (e.g., for new store location), exploring relations among data sets, assessing alternatives and aiding in decision making, and displaying elected environments both visually and numerically. In practice, a GIS consists of a series of layers, each presenting a particular two-dimensional feature, that can be superimposed accurately on top of one another. Some examples: • a marketing group overlays customer locations, school locations, distribution centers and existing retailers selling their own and/or their competitors products. • A telecommunications company selects the number and location of switching centers and routes in a communication network. The system displays such quantitites as traffic, costs, and transmission times. Users can redefine the network on the screen, can create multiple views, see the effect of ‘what if’ changes and new data because the system re-computes for each change, take constraints into account, and see where the proposed solution fails to meet criteria. SIDEBAR 2 A TECHNOLOGY FOR BUSINESS INTELLIGENCE: VISUALIZATION With the flood of data available from information systems, business intelligence analysts and decision-makers need to make sense out of the knowledge it contains. Visualization is the process of representing data as visual images. Unlike Geographic Information Systems which typically deal with physical 7
  8. 8. spaces, the underlying data could, for example, represent abstract objects, such as profit, sales, or cost. If the data is abstract, then a visual analog must be created. And visual analogs today go far beyond the pie chart and the bar chart (Tegarden 1999). Visualization has been used to create advanced dashboard in which large amounts of information are presented on a single screen. Visualization allows: • Exploiting the human visual system to extract information from data, • Provides an overview of complex data sets, • Identifies structure, patterns, trends, anomalies, and relationships in data, • Assists in identifying the areas of "interest" That is, visualization allows BI analysts to use their natural spatial/visual abilities to determine where further exploration should be done and where action is required. . Visualization technologies already have been deployed in finance, litigation, marketing, manufacturing, training, and organizational modeling BI is also a process that involves humans and how they act, and much research still needs to be done here. In simple terms, to obtain high-impact analysis (Morris, 2003) you have to: 1. Recognize the application imperative and focus on an important business issue. 2. Individualize intelligence to each person who makes decisions 3. Build discipline in the decision making processes 4. Recognize new skills are required for knowledge workers 5. Deal with the complexity of closed loop, adaptive systems 8
  9. 9. Sidebar 3 is a case example of the use of technology and the use of process for BI. It involves two DSS companies whose names should be familiar to you. SIDEBAR 3 EXAMPLE OF USING BUSINESS INTELLIGENCE FOR AN ACQUISITION An example of using BI is the acquisition of Execucom of Austin TX by Comshare of Ann Arbor, MI several years ago. Both firms were arch competitors in developing and selling 4th generation languages, which are standard business intelligence products. The President of Comshare at the time, Crandall by name, used a service that monitored newspaper articles about specific competitors. One day his screen showed an article that reported an interview with Execucom’s Chairman, Anderson, in the Austin newspaper in which Anderson unburdened himself. Anderson discussed the miserable relation between his company and its owner, a firm in an adjacent state in a different business who had acquired Execucom to get into the computer business but which, he said, did not understand what Execucom did. Reading the way Anderson unburdened himself to the reporter, Crandall recognized that Execucomm could be bought, probably relatively cheaply. The acquisition would eliminate a major competitor, broaden Comshare’s product line, and perhaps most important, move a cadre of very smart people into Comshare. Acting on this intelligence information, Comshare bought Execucom. Although the business intelligence led to the acquisition, the end result was ironic. Comshare rapidly stripped the Austin operation, moved key personnel to Ann Arbor, and a couple of years later closed down Execucom’s premier product in favor of its own. Lesson: Good intelligence may not lead to good decisions in the long term. 9
  10. 10. WHAT DO FIRM’S USE BI FOR? A survey by the Gartner Group (Imhoffe, 2003) of the strategic uses of business intelligence, found these uses were ranked by firms in the following order: 1. Corporate performance management 2. Optimizing customer relations, monitoring business activity, and traditional decision support. 3. Packaged standalone BI applications for specific operations or strategies 4. Management reporting of business intelligence. One implication of this ranking is that ordinary reports about a firm’s performance and that of competitors, which is the strength of many existing software packages, is not enough. Extensive analysis is required. A second implication is that too many firms still view business intelligence (like DSS and EIS before it) as an inward looking function. SIDEBAR 4 BI APPLICATIONS The following examples come out of the special case of business process management. The data are not publicly available yet, so the company names are disguised. A company that provides natural gas to homes created a dashboard that supports operational performance metric management and allows real time 10
  11. 11. decision making. In one application of the dashboard, the number of repeat repair calls was reduced, resulting in a saving of $1.3 million At a large member-owned distributor to hardware stores, using a dashboard reduced the amount of inventory that must be liquidated or sold as a loss leader from $60 million to $10 million. Their BI system also allows their member stores to see their own performance relative to similar stores. The foregoing examples deal with the special BI case of Business Process Management. Because the data are not yet publicly available, the company names are disguised. RETURN ON INVESTMENTS Like most information systems, BI up-front costs are high as is upkeep. Unfortunately, although reductions in information systems costs from business intelligence efficiencies can be forecast, the IT savings are only a small portion of the payoff, which comes from the opportunities seized and the difficulties avoided. It is rare for a BI system to pay for itself through cost reductions. Costs. Most firms do some form of business intelligence, although only a few have complete BI systems. Putting a BI system in place from scratch includes costs of additional hardware and large amounts of software. In addition to buying a BI software package, external data needs to be paid for and often a dependent data mart specifically for Business Intelligence is established. And then, of course, there are the costs of the analysts and their support staff, the maintenance and update of hardware and software, and the time spent by the user community reading and thinking about the outputs of BI. Benefits. 11
  12. 12. Most BI benefits are soft. The hope is that a good BI system will lead to a “big bang” return at some time in the future. However, it is not possible to forecast big bangs because they are fortuitous and infrequent. Despite the softness of the benefits, companies are willing to invest because they need to understand what their business and their competitors business is about. One indicator that firms believe the benefits are worth it is the shift from providing BI for specialists to providing BI for the masses of employees as a way of closing the gap between operations and analysis. Established analytic practice, especially with online analytic processing (OLAP) typically involves a solitary user exploring data in what’s usually a one-off experience (Russom, 2003). Decisions are made at many organizational levels not just the executive level, the emergence of new analytic tools purports “BI for the masses”. Rolling out BI tools like data mining to the masses has shown improvement (McKnight 2003). Examples of large deployments include deploying BI tools to 70,000 at France Telecom, 50,000 users at US Military Health System, and to several other firms at the 20,000 user level (Schauer 2003). COMPETITIVE INTELLIGENCE Competitive intelligence (CI) is a specialized branch of Business Intelligence. It is “no more sinister than keeping your eye on the other guy, albeit secretly” (Imhoff, 2003). A more formal definition is given by the Society of Competitive Intelligence Professionals ( 3) Yes, there is such a society! SCIP’s Definition: Competititve Intelligence is a systematic and ethical program for gathering, analyzing and managing external information that can affect your company’s plans, decisions and operations. 3 “What is CI?” 12
  13. 13. In other words, CI is the process of ensuring marketplace competitiveness through a greater understanding of competitors and the overall competitive environment. “You can use whatever you find in the public domain to ensure that you will not be surprised by your competitors.” (Imhoff 2003). SIDEBAR 5 EXAMPLES OF COMPETITIVE ANALYSIS *Texas Instruments made a $100 million acquisition based on their knowledge of a competitions potential bid, (Lavelle, (2001)). * Merck & company developed a counter strategy to its competitor’s forth- coming product based on competitive intelligence reports, a savings of $200 million, (Imhoffe, 2003) * Illuminet, a company that delivers advanced network, data base, and billing services, stayed a step ahead by using a vendor (QL2 software) to retrieve information posted on their competitors web sites (Moores, 2002) Competititve intelligence is not as difficult as it sounds. Much of what is obtained comes from sources available to everyone, including: * Government information * online databases, * Interviews or surveys, * special interest groups (such as academics, trade associations, and consumer groups), * private sector sources (such as competitors, suppliers, distributors, customer) * media (journals, wire services, newspapers, financial reports, public speeches by competitor executives) The challenge is not lack of information; it is the ability to differentiate useful competitive information from chatter or even disinformation. 13
  14. 14. Of course, once you start practicing competitive intelligence, the next stage is to introduce countermeasures to make the CI task about you more difficult for others. The game of measure, countermeasure, counter- countermeasure, and so on to counter to the nth measure is played in industry just as it is in politics and in international competition. THE MARKET AND THE VENDORS AMR research estimates the current BI market at $6 billion with projection to reach $12 billion by 2006 (Darrow, 2003). At a time when demand for most IT products is soft, demand for BI applications continues to grow. Gartner forecasts that this growth will push demand for consulting and systems integrators pushing the worldwide market for professional and support services from $10.5 billion in 2002 to $15.6 billion in 2006, a 10% average annual increase (Soejarto 2003) As we indicated earlier, a large number of firms are involved in aspects of the BI business. For example, ratings of companies to watch business intelligence in 2003 published in Intelligent Enterprise (Stodder,2003) included Aydatum, Brio software Decisions, Cognos, Crystal Decisions, E-Intelligence, Fair Issac, Hyperion, MicroStrategy, ProClarity, Siebel, and Spotfire. In the over-all category of “most influential” the companies4 rated were Teradata, SAS, IBM, Outlook Soft, Business Objects, Microsoft, Manhattan Associates, PeopleSoft, Oracle, Ilog Inc., Insight Software, and Open Souce/Linux. Whereas in the past a large number of companies built their own systems, the trend is toward buying packages. Gartner Research found a reduction in the number of firms that plan to manage their BI integration internally dropped from 49% in 2001 to 37% in 2002 (Soejarto, 2003). The reason for this change is that the traditional custom design, build, and integrate model for BI systems takes too 4 Companies were listed only once, rather than the same company being repeated in several categories, which some might well have been. 14
  15. 15. long (at least six months) and costs too much (2 to 3 million) (Rudin and Cressy, 2003). In contrast, pre-built analytic applications result in lower total cost of ownership. Implemented quickly, they deliver rapid return on investment, and provide, performance, scalability, and flexibility (Rudin and Cressy, 2003). MANAGERIAL QUESTIONS AND ANSWERS 1. Is business intelligence an oxymoron? A shorthand for cloak and dagger spying on competitors and government? An important, legitimate activity? Despite its name, business intelligence is about trying to understand your own position, your customers, and your competitors. It is neither ethical nor legal to spy on competitors. BI is an important part of a firm’s planning and operational decision making. 2. What is new about today’s business intelligence compared to previous systems? Business intelligence is a natural outgrowth of a series of previous systems designed to support decision making. The emergence of the data warehouse as a repository, including the advances in data cleansing that lead to a single truth, the greater technical capabilities of hardware and software all combine to create a richer business intelligence environment than was available previously 3. What types of business intelligence are there? Business Intelligence is used to understand: • The capabilities available in the firm; • the state of the art, trends, and future directions in the markets, • the technologies, and the regulatory environment in which the firm competes; and • the actions of competitors and the implications of these actions. 15
  16. 16. 4. What will you be able to do if you invest in BI? Business Intelligence systems present complex corporate and competitive information to planners and decision makers. The objective is to improve the timeliness and quality of the input to the decision process. 5. Who uses BI? Business intelligence is used by managers throughout the firm. At senior managerial levels, it is the input to both strategic and tactical decisions. As lower managerial levels, it helps individuals to do their day-to-day job. 6. How do you gather and transfer BI? Business intelligence is a form of knowledge. The techniques knowledge management for generating and transferring knowledge (Davenport and Prusak 1995) apply. Some knowledge is bought (e.g., scanner data in the grocery industry) while other knowledge is created by analysis of internal and public data. Knowledge transfer often involves disseminating intelligence information to many people in the firm. For example, sales people need to know market conditions, competitor offerings and special offerings. 7. Do you need a separate organization for BI? Most medium and large firms assign people, often full time, for planning and for monitoring competitor action. These people are the ones who form the core groups for business intelligence initiatives. Whether they are centralized or scattered through SBU’s is a matter of organizational style. 8. What technologies are available? Most of the technologies needed for business intelligence serve multiple purposes. For example, the World Wide Web is used for both knowledge generation and knowledge transfer. However, specialized software for doing analysis is the heart of business intelligence. This software is an outgrowth of the 16
  17. 17. software used for decision support and executive information systems in the past. It incorporates many of the technological advances that we discussed above. FINAL THOUGHTS We titled this article “Business Intelligence: A New Name Or The Future Of DSS?”. I hope this paper gives you enough insight that you can answer this question for yourself. However, I’m sure you would like to know where I think we are. My view is that both alternatives are true. Much of what is going on is the result of new technology, particularly data technology, being made available, for the kinds of studies that DSS performed, and performed well, over the years. So, in one sense, the business intelligence name is simply a replacement for the DSS name that overcomes the old view in industry that DSS is principally about modeling. A new name gives us a new skin—and makes people think about us in a different way. Semantics matter. Yet, there is also something deeper going on. • We are getting better at solving business problems and becoming more aligned with the imperatives of the firm. • We are better at dealing with the really large amounts of high quality data becoming available and in incorporating new technologies and new analysis methods. • We are democratizing the dissemination of our results and moving to support people from the bottom to the top of the organization. I therefore view business intelligence and competitive intelligence as steps along the way. The terms are the short term future. However, as we become more adept at the business intelligence function we will inevitably find new ways of thinking about decision problems and new technologies to incorporate. We will have new names for our field but, at heart, we will be solving the next generation of decision support problems. 17
  18. 18. REFERENCES Cody, W.F., J.T. Kreulen, V. Krishna, and W.S.Spangler (2002) “The integration of business intelligence and knowledge management”, IBM Systems Journal, Vol. 41, No. 4, pp. 697-713. Davenport, Thomas H. and Laurence Prusak (1998) Working Knowledge: How Organizations Manage What They Know Boston, MA: Harvard Business School Press Darrow, Barbara (2003) ”Making The Right Choice—Solution providers are evaluating a plethora of options as they puzzle over the future of business intelligence”, Computer Reseller News, Feb. 3, 2003, pp. 16 Hall, Hazel (2000) “Online information sources: Tools of business intelligence?”, Journal of Information Science, Vol. 26(3), pp. 139-143). Imhoff, Claudia (2003) “Keep your friends close, and your enemies closer,” DM Review, 13(4), pp. 36-37, 71 Langseth, Justin and Nithi Vivatrat (2003) “Why proactive business intelligence is a hallmark of the real-time enterprise: outward bound,” Intelligent Enterprise, Vol. 5(18), pp. 34-41 Lavelle, Louis (2001) “The case of the corporate spy in a recession: Competitive intelligence can pay off big”, Business Week, 26 November 2001, Vol 56 (#3759) Markus, M. Lynne and Allen S. Lee (2000) “Special issue on intensive research in information systems: Using qualitative, interpretive, and case methods to study information technology”, MIS Quarterly, Vol. 24(1), pp. 3-41. 18
  19. 19. McKnight, William (2003) “Bringing data mining to the front line, part 2,” DM Review, 13(1), pp. 50 Moores, L. (2002) “WebQL Harvests Competitive Prices for Illuminet” DM Review, 12(7) Morris, Henry (IDC) (2003) “The 5 Principles of high-impact analytics,” DM Review—sponsored supplement, vol. 13, No. 4, pp. 2-8 Powell, J.H. and J.P. Bradford (2000) “Targeting intelligence gathering in a dynamic competitive environment”, International Journal of Information Management, Vol. 20(3), pp. 181-196 Power, Dan (2002) Decision Support Systems: Concepts and Resources for Managers, Westport, CT: Quorum Books Rouach, Daniel and Patrice Santi (2001) “Competitive Intelligence adds value: Five intelligence attitudes”, European Management Journal, Vol. 19(5), pp. 552-559. Rouibah, K and S. Ould-ali (2002) “Puzzle: A concept and prototype for linking business intelligence to business strategy” The Journal of Strategic Information Systems ,11(2) June pp. 133-152 Rudin, Ken and David Cressy (2003) “Will the Real Analytic Application Please Stand Up?” DM Review, Vol 13(3), pp. 30-34 Russom, Philip (2003) “Decision support: Two heads are better than one”, Intelligent Enterprise, Vol. 6 (2), pp. 14-16 Schauer, Valerie (2003) “Business Objects”, DM Review, vol. 13(1), pp. 34-36 Soejarto, Alex (2003) “Tough times call for business intelligence services, an indisputable area of growth”, March 31, 2003, pp. 76. 19
  20. 20. Sprague, Ralph H. and E.D. Carlson Building Effective Decision Support Systems” Englewood Cliffs, N.J.: Prentice Hall Stodder, David “Enabling the intelligent enterprise: The 2003 editors’ choice awards”, Intelligent Enterprise, Vol 6 (2), pp. 22-33 Tegarden, David P. (1999) “Business Information Visualization” Communications of the Association for Information Systems (1)4 default.asp Teo, Thompson and Wing Yee Choo (2001) “Assessing the impact of using the Internet for competitive intelligence”, Information & Management, Vol. 39(1), pp. 67-83. Vedder, Richard G. and Stephen C. Guynes (2002) “CIOs perspectives on Competitive Intelligence”, Information Systems Management, Vol. 19(4), pp. 49-55. Weir, Jason (2000) “A Web/Business Intelligence Solution”, Information Systems Management, Winter 2000, pp. 41-46. Wiggins, Bob (2001) "The Intelligent Enterprise Butler - Competitive Use of Intelligence in Business", International Journal of Information Management, Vol. 21(5), pp. 397-398. 20