Data Mining Efforts Increase Business Productivity and Efficiency
Interview with Stephan Kudyba - President of Null Sigma Inc.

The use and acquisition of information is a key part of the way any business makes money. Data mining technologies provide greater insight into how this information can be better used and more effectively acquired. Stephan Kudyba, an expert in the field of data mining technologies, shares his expertise with us in the following interview.

NOTE: Publisher grants permission to reprint this entire interview. Kindly forward us a tear sheet of any published pieces regarding this interview.

Q. What exactly is data mining?

A. To put it simply, data mining is the process by which business analysts utilize analytical technology (e.g. quantitative methodologies) to gain greater insights into important factors that drive their processes. Data mining identifies reliable relationships between variables that drive a process and a certain performance measure. With this information, managers can better control their processes and more effectively carry out strategies. Processes refer to any type of business activity (e.g. Sales and marketing, pricing, costs, customer relationship management, manufacturing and more).

Q. What is "tangible corporate productivity" and how does data mining contribute to it?

A. Tangible corporate productivity refers to measurable productivity or in other words, measurable changes in performance measures. For example, if data mining analysis determines that a certain marketing program should increase sales for a particular product, the results can be calculated tangibly, (e.g. what is my change in sales resulting from a particular marketing program?). The key to implementation and evaluation of technology is to measure its impact on business performance. Data mining gives managers the ability to monitor the effectiveness of analytical technology by monitoring the results which were a result of the analysis.

Q. Why is it important for the guidelines for implementing technology to be "business friendly" and useable by all levels of management?

A. Business or user friendliness is an essential element to technological success. Certain technologies can possess powerful capabilities but if they are too difficult to use or understand, they have no value added. The information economy largely involves a wider diffusion of information and knowledge creation to a greater population in the corporate world. Business friendly technology augments this essential process. The bottom line to gaining efficiency is to empower decision makers with information and technology that is usable and understandable to process information and create knowledge.

Q. What are the differences in achieving success with technology implementation for "Brick and Mortar" versus E-Commerce centric organization?

A. The core to success for each of these environments revolves around understanding the markets you face and the variables that affect your activities. So for the big picture, many of the factors for success are common to both types of organizations. However when considering the more detailed activities of the two, one must consider the character of the consumer. The pure e-commerce or on-line organization needs to focus on effectiveness of facilitating client needs via a mechanism which defines its character, the Internet. Therefore they must manage such issues as site layout, inventory status and supply chain and consumer activity on the Web. "Brick and Mortar" companies, on the other hand, monitor activity in retail outlets and in manufacturing processes. The real difference between the two lies in the means by which business is conducted.

Q. What are some of the common pitfalls involved in implementing Data Mining and how can they be overcome?

A. Common pitfalls of data mining implementation largely involve:

1. Not being able to effectively communicate mining results within an organization.
2. Not having the right data to conduct effective analysis.
3. Not using existing data correctly.
4. Improperly skilled individuals conducting analysis

The first point can be overcome by increasing the exposure of data mining results throughout an organization and preparing data mining analysis in a business friendly (report manner). Number 2 requires communication between analytical personnel and those individuals that decide on data collection and storage (e.g. the IT department). One of the essential elements to affective mining is the availability of business relevant data; "your analysis is only as good as the data you use". Finally, numbers 3 & 4 require properly skilled individuals to conduct mining analysis. This can be accomplished by hiring experienced personnel or training existing employees.

Q. What are the current problems with applying academic theory to business strategy?

A. Academic theory is often mislabeled as dry and inapplicable in the business world. Many accepted principles are difficult to comprehend and interpret by business practitioners since they are written using technical jargon. However, one must keep in mind that many common business strategies are grounded in academic theory (e.g. micro or macroeconomic theory). It is a shame that there is a disconnect between the two. Just as innovative technology needs to be business friendly, perhaps complex theoretical principles should be described in a more comprehendible and practical manner.

Q. How can the ability to access and retrieve data answer business questions and be useful to a business?

A. The bottom line to business success is to increase the knowledge of decision makers at all levels of an organization. The process of knowledge creation and enhancement comes from information and information is nothing more than data that has been collected, accessed, formatted and analyzed. The more efficiently organizations can access value added data, the better they can gain greater insights into what drives their activities which enables them to better devise business strategy.

About the Editor:

Stephan Kudyba began his career in the investment banking industry where he spent over a decade of his life analyzing the state of the global economy. His experience has included such activities as International Economist/Market Analyst and Risk Exposure Management, which involved the creation of sophisticated models that identified trends in securities prices. Over the years, he has worked in such institutions as Citibank (New York), Dresdner Bank (Frankfurt, Germany and New York) during which he obtained a Masters in Business Administration with a finance concentration. In order to fully grasp the changing nature of economic activity as a result of the evolving information age, Dr. Kudyba attained a PhD in economics at Rensselaer Polytechnic Institute with a special focus on information technology and firm level productivity. He is now an economic consultant with www.nullsigma.com.where he applies data mining and business intelligence technology to devise productivity enhancing strategies for organizations around the globe. He also has combined his knowledge of the information economy with his investments experience and provides in-depth analysis of global investment markets, which is available on his Web Site www.MarketDr.net.

His new book entitled, Data Mining and Business Intelligence: A Guide to Productivity addresses the importance of data mining and provides practical guidance for business people, academicians and practitioners alike. This book is available from IGI Publishing, Hershey, PA (180 pages, Copyright 2001, ISBN: 1-930708-03-3)

Copyright IGI Global 2001

For additional information about the publication or to arrange an interview with the author, please contact: Ms. Carrie Stull IGI Publishing Tel: 717-533-8845 Fax: 717-533-8661 Email: cstull@idea-group.com URL: http://www.idea-group.com