Symbiotic Mining1: An Antidote for Corporate Insanity




                                        Paper submitted
        ...
Introduction: Many companies today are inundated with information and the increase in the
magnitude of information is expo...
polarities between the expertise. Thus data mining today often ends up providing too little and
too late for managerial de...
Figure: 1 Configuration of Symbiotic Data Miner



                                           Relational
                 ...
mining tools for reliable and effective decision-making. Today, even the very life of any business
depends on fast and eff...
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Symbiotic Mining : An Antidote for Corporate Insanity

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Symbiotic Mining : An Antidote for Corporate Insanity

  1. 1. Symbiotic Mining1: An Antidote for Corporate Insanity Paper submitted To Plenary Poster/Presentation High Performance Computing HiPC 2002 Bangalore, India By Dr. Kuriakose Athappilly Professor of Information Systems and Quantitative Methods BIS Department Haworth College of Business Western Michigan University Kalamazoo, MI 49008 USA Ph: 269 387 5409 e-mail: Kuriakose.athappilly@wmich.edu 1 The word, ‘symbiotic’ is used to emphasize the co-existence of business intelligence and technical expertise in one and the same person. 1
  2. 2. Introduction: Many companies today are inundated with information and the increase in the magnitude of information is exponential. Subsequently, it is hard for decision makers to make sense of the data they have and arrive at actionable decisions. Traditional methods of data base queries do not lend themselves to provide insight into the data. Those queries respond only to questions they ask. Business executives are often confronted with a problematic situation. They know what they want, but they do not know what to ask, more importantly how to ask. The gold rushers of the past always knew what they wanted. Gold! But questions such as where to dig, how to dig, and with what tools were pretty much unknown to them in the early stages. Today, knowledge is recognized as the most important asset, the glow diamond, of any corporation. But to dig out this asset, we began to use data mining tools. However, even in the data mining practices, major improvements were introduced to break away from the traditional/classical data mining methods and practices thanks to the application of Artificial technologies and sophistication of statistical and mathematical methodologies. Yet data mining is still a mysterious territory to many of the user managers. The authors believes that time has come for user-managers to take this challenge in their own hands. They will be able to start mining data, understand the hidden relationships, patterns and trends, and make effective, actionable-decisions, all by themselves. This ability to handle data mining process independently has already become a necessity for managers to keep their businesses ahead of the game in today's highly competitive and fast changing corporate climate. In the 1960’s business used computers for data collection. It provided with a collection of static data. This stored data, however, was limitedly used in decision support. In the 1980’s, the emergence of relational model for data storage and structured query language for data retrieval and manipulation capabilities got closer to user managers’ need. The 1990’s saw the emergence of data-warehousing and data mart technologies. However, to derive meaningful information by way of relationship and patterns, statisticians employed elaborate methodologies. Soon after, machine-learning techniques based on human learning models emerged. Thus the convergence of database technologies, machine-learning techniques, and computational methodologies has lead to the development of current business data mining technology. Typical Data Mining Model Today Today, data mining is a joint undertaking of the statistical analysts and data-mining technologists. The technologists prepare the data, run the databases and the data mining software and statisticians identify the relevant variables, create models and interpret results. Then they forward the results to the decision makers. The decision makers after studying the results in the context of the company’s environmental and internal factors arrive at some actionable decisions. Once actionable decisions are implemented, often the data-mining department conducts impact analysis periodically. In the present setting, data miners and lack business savvy. The decision maker often lacks both the technical knowledge to handle the database and related data mining software as well as the statistical knowledge to analyze the data. Thus the process of data mining as practiced today is extremely time consuming, is exorbitantly expensive, and it is often misleading due to the 2
  3. 3. polarities between the expertise. Thus data mining today often ends up providing too little and too late for managerial decision makers. Businesses Looking for Pragmatic Mining Data mining as a huge undertaking, with high demands on cost, time and talents is not often the business people’s best choice. They look for pragmatic solutions for urgent problems, which do not allow them the luxury of time and money. ‘If data mining is the tool to bring about desirable outcomes, let it be manageable, meaningful and feasible’, they would argue. This demand from the business sector necessitated a paradigm shift in data mining principles and practices. First, the practitioners of data mining began relying more on the heuristic knowledge derived from the historical data for predictive accuracy, than on the total dependence of hypothesis-based validation criteria, based on classical statistical methods. Second, they were willing to accept a trade-off between generality and accuracy in the interest of applying the results to greater population. As a result, data mining has now become more manageable, feasible and usable. And it often makes good business sense too. Conducive Technology Climate New data mining software packages are very powerful and easy to use and at the same time they do not demand highly sophisticated statistical knowledge. They are visual, interactive, and iterative. Above all, they help us arrive at sensible business solutions. Because the user managers are not experts in data modeling visualization helps them understand the results easily. The user, therefore, can absorb large sets of data, easily access data and perceive patterns and navigate through complex sets of data without the help of a statistical expert. Toward Symbiotic Data Mining At present, there are three major participants in data mining scenario, the technologist and the methodologists doing the mining process and the user manager making decisions based on the mining results. Often the scenario leads to a situation similar to that of 'the six blind persons' each one of them describing a major part of an elephant and yet never getting a clue as to what an elephant is like. The problem here in today’s data mining, is that of not seeing the whole from a unified perspective. The author suggests that time is just right for a new generation data miners to take the roles of the technologist, the methodologist and the business expert, all the three in one. We may call these miners, the emerging symbiotic data miners! When the symbiotic data miners start entering the corporate world, data mining will become a business executive's handy tool just as the spreadsheets and word processing are today. But to arrive at this envisioned state of data mining; a combined campaign by the business people, technologists, and academicians is necessary. The businesses, as they become convinced of the usefulness of the new approach would be willing to support the epiphany of the symbiotic miners by providing research funds and grants. The technologists, as they are always, should continue making data mining software more and more easy to understand, interactive and visual. The academicians should promote this movement by incorporating the state-of-the-art data mining software, such as SPSS Clementine as a part of students’ learning tools. The academicians should emphasize the importance of the working knowledge in statistical and mathematical tools and models. They should also provide opportunities and incentives to undertake data mining projects for real world problems as class projects. The approach is beneficial for the students in their job search and the industries in employing the newly trained symbiotic data-mining candidates. 3
  4. 4. Figure: 1 Configuration of Symbiotic Data Miner Relational Database Corporate Needs AI Tools 1. Business Savvy 2. Methodologies 3. Technologies Model Building SW Actionable Interactive Decisions Tools Environment al Math/Stat Tools Changes Visualization Tools The author has depicts the configuration of a symbiotic data miner in Figure 1. It highlights the factors that are in play while the mining takes place in a corporate environment. Conclusion In today’s global corporate environment, data mining technology plays a crucial role as a decision support tool. Before long, the businesses will witness increasing dependence on powerful data 4
  5. 5. mining tools for reliable and effective decision-making. Today, even the very life of any business depends on fast and effective decisions. Fast and effective decisions require thorough but fast and easily accessible supporting information. The emergence of symbiotic data miners, equipped with working knowledge and skills in technologies, methodologies and sufficient business savvy are the solutions to the critical problem of making immediate and effective decisions and of keeping today’s corporations successful in the fast changing environment. References: Cox Kenneth C., Eick Stephen G., Wills Graham J., and Brachman Ronald J. 1997. Visual Data Mining: Recognizing Telephone Calling Fraud. Data Mining and Knowledge Discovery. 225-231 Shae, Jesica 2001. Visual Data Mining, < http://www.adguard.co.jp/mjjapan/jesica/abstract.htm.> (September 2002). SPSS Inc. Introduction to Clementine, P: 4-15, 2001 Edition, < http://www.spss.com/spssbi/applicatons/datamining > (September 8, 2002). SPSS Inc. Introduction to Clementine, P: 6-8, 2001 Edition, < http://www.spss.com/spssbi/applicatons/datamining > (September 8, 2002). SPSS Inc. Introduction to Clementine, P: 6-19, 2001 Edition < http://www.spss.com/spssbi/applicatons/datamining > (September 8, 2002). Thearling, Kurt 1995. An overview of Data Mining at Dun & Bradstreet, < http://www.thearling.com/text/wp9501/wp9501.htm > (September 5, 2002) Thearling, Kurt 1997. Understanding Data Mining: It’s all Interaction, < http://www.http://www3.primushost.com/~kht/text/dsstar/interaction.htm > (September 5, 2002) Thearling, Kurt, Becker Barry, DeCoste, Dennis, Mawby Bill, Pilote, Michel, and Sommerfield, Dan. 2001. Visualizing Data Mining Models. Information Visualization in Data Mining and Knowledge Discovery. <http://www3.primushost.com/~kht/text/dmviz/ modelviz.htm > (September 5, 2002) 5

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