By Stephan Kudyba
Enterprises are always looking for new and higher how you can locate and deal with the mammoth quantity of data their firms come across day-by-day. to outlive, thrive and compete, businesses has to be capable of use their beneficial asset simply and conveniently. determination makers can't come up with the money for to be intimidated via the very factor that has the skill to make their company aggressive and effective. available and usable data/information is essential to company good fortune in information mining. potent decision-making thoughts are a cornerstone of any fit association that makes use of the huge quantities of data a regular foundation. using high-end analytical software program applied sciences, or information mining, can reduction choice makers in extracting details and information from their occasionally overwhelming shop of information accrued continually of their agencies. IT strategies sequence: coping with info Mining: suggestion from specialists bargains numerous chapters, every one written by means of famous practitioners and specialists in a variety of firms, that supply principles and measurements during this transforming into administration trouble.
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Is prohibited. 16 Kudyba • • • • • • Sales Force Efficiency Customer Relationship Management Credit Risk and General Risk Management Advertising and Marketing Effectiveness E-commerce Strategic Analysis Healthcare Management The authors of corresponding chapters provide insightful content on how data mining is used in these prominent business applications at some of the largest organizations in the world. The information is presented by a general descriptive approach, graphic illustrations and case studies and incorporates many of the concepts that have been introduced to you in this first chapter.
No matter how much data we have, inconsistencies or inaccuracies can seriously jeopardize the quality of any modeling effort. Data quality is a concern whether we are working with our own internal data or data from a vendor and unlike market data, credit risk data (such as financial statements) are “low frequency” (annual data in most instances). As such, almost every data point is valuable and serious effort is made to ensure that the data are accurate, while we discard as little data as possible.
Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Data Mining and the Banking Sector 29 Use of Information In 2002, market watchers saw the credit worthiness of the corporate world at a low point. Countries defaulted, there were widespread defaults in various industries, like the telecom industry, and fraud-related defaults plagued the international markets. Even in this high default rate environment, the organizations suffering the most are not necessarily the banks, as one would expect.