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Big Data

Dig deep: Data mining resources for consultants

Recent reports predict that the customer relationship management (CRM) market will remain one of the least affected by the downturn in IT spending. Brush up on one of the essentials of CRM—data mining—with these Web resources.


Consulting Central recently reported that customer relationship management (CRM) revenues have skyrocketed—in some cases up by 40 percent or more—over last year’s level. Despite slower spending trends in other technology-related arenas, CRM still means big business for consulting firms over the next fiscal year. Consulting Central also reported that PricewaterhouseCoopers, the known leader in the CRM space, expects the number of its consultants devoted to CRM to increase by 46 percent.

Remaining a viable business in the consulting space may depend on your ability to run with current trends. If you want your slice of the CRM revenues, you need to know about the essentials of CRM. One of those key areas is data mining (DM). We’ve gathered some of the best resources from TechRepublic and the Web to enhance your knowledge of this anchor technology.

How will data mining help my clients?
Essentially, data mining involves using a set of statistical formulas to analyze data to find undiscovered relationships and identify patterns and trends. As a CRM application, data mining draws information from a database of customer information to pinpoint customer behaviors. Through careful scrutiny and the application of complex algorithms, a user can discover correlations between, for instance, the probability that a customer who buys Product A will also buy Product B. According to whatis.com, DM results include:
  • Associations—when one event can be correlated to another event. (For example, beer purchasers also buy peanuts a certain percentage of the time.)
  • Sequences—one event leading to another later event (for example, a rug purchase followed by a purchase of curtains)
  • Classification—the recognition of patterns and a resulting new organization of data (for example, creating profiles of customers who make particular purchases)
  • Clustering—finding and visualizing groups of facts not previously known
  • Forecasting—discovering patterns in the data that can lead to predictions about the future

What data mining tools are available?
According to Data Mining 101, an article from About.com, the most popular methods for data mining include:
  • Decision trees and rules
  • Nonlinear regression and classification (including neural networks)
  • Example-based methods (nearest-neighbor classification, case-based reasoning)
  • Inductive logic programming

Useful data mining requires knowledge of statistics and the algorithms that identify useful information. “Vendors of complementary technologies sometimes contribute to the confusion, marketing reporting and online analytical processing (OLAP) tools as data mining products…. A genuine DM tool must support [semi] automatic discovery of patterns.”

To begin your research for a tool that matches the needs of your clients, About.com suggests several sites that offer a full range of DM tools:

DM resources from TechRepublic
TechRepublic has covered data mining from many angles; from case studies to strategic implications. Here are a few of our offerings:

Data mining resources from the Web
To gain a well-rounded understanding of data mining’s key components and mechanisms, check out KDnuggets (Knowledge Discovery Nuggets). The site features all sorts of information, from jobs in the field to recent publications.

If you’d like to dig deeper to find out more about data mining, try the following Web resources:

Strike gold with data mining?
Have you helped a client implement a data mining strategy? We’d love to hear about your results. Send us an e-mail or post your comments below.

 

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