|  Chapter Overview This chapter covers the following topics: -
Things to consider about data mining -
Traditional analysis techniques versus data mining -
The importance of data mining -
Data sources for data mining -
The five most common data mining techniques: associations discovery, sequential pattern discovery, classification, clustering, and forecasting -
Data mining operations such as predictive and classification modeling, link analysis, database segmentation, and deviation detection -
Applications of data mining in the areas of market management, fraud detection, risk management, financial services, and distribution -
Brief descriptions of the activities involved in data mining, the deliverables resulting from those activities, and the roles involved -
The risks of not performing Step 13 Things to Consider Marketing Questions  Do we know what general classes of customers we have? Are there subclasses of customers with similar behavioral patterns? Can we use targeted marketing messages for these customers? Do we know what our best customers have in common? Is there a pattern that will verify our hunch that if we offer new products or services "just in time" to our best customers, they will buy them? Do we know how to retain our best customers? How can we predict which customers are more likely to leave us? How can we sell more to our existing customers? Which of our customers are more likely to buy more products or services from us? Do we have customers who are costing us money? Do we suspect fraudulent activities that need to be discovered ? Data Is our data clean enough for data mining? Is the data understood ? Will it be used and interpreted correctly? Is it coded properly for data mining? Is the data organized correctly for data mining? Data Mining Tool What type of data mining tool is appropriate for our organization? What type of criteria should we consider when we evaluate data mining tools? How will we determine the return on investment (ROI) for a data mining initiative and a data mining tool? Staffing Do we have statisticians or skilled analysts who can interpret the data mining results? Will we need to hire additional statisticians to perform data mining? Will a database administrator be available to create and load the data mining databases? Will we need a database administrator full-time ? Part-time? | Many organizations have accumulated massive amounts of data in their operational systems. This data constitutes a potential source of valuable business information that can be mined. Analytical models can be generated to find patterns in the data and to allow the information to be used for competitive advantage. This gives the business managers and executives the information they need in order to take action, enabling them to increase profits, reduce costs, create innovative product strategies, and expand market share.  |
