Using Data Mining for Business Intelligence - How does data mining work?
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The technique that is used to perform these feats in data mining is called modeling. Modeling is simply the act of building a model in one situation where you know the answer and then applying it to another situation where you don't. For instance, if you were looking for a sunken Spanish galleon on the high seas the first thing you might do is research the times when Spanish treasure had been found by others in the past. You might note that these ships often tend to be found off the coast of Bermuda and that there are certain characteristics to the ocean currents, and certain routes that have likely been taken by the ship captains in that era. You note these similarities and build a model that includes the characteristics that are common to the locations of these sunken treasures. With these models in hand you sail off looking for treasure where your model indicates it most likely might be given a similar situation in the past. Hopefully, if you have made a good model, you find your treasure.
The process of creating the data mining model is directly dependent on the methodology used to feed the entire data mining process. In essence, the method used to make data available to be mined governs the process used to create the data model. If a solutions architect designed a specialized OLAP data cube in Analysis Services to serve as the primary source of data mining data, then an OLAP data mining model would be created, as opposed to a relational data mining model.
This act of model building is thus something that people have been doing for a long time, certainly before the advent of computers or data mining technology. What happens on computers, however, is not much different from the way people build models. Computers are loaded up with lots of information about a variety of situations where an answer is known, and then the data mining software on the computer must run through that data and distill the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don't know the answer.
In what areas is data mining profitable?
A wide range of companies have deployed successful applications of data mining. While early adopters of this technology have tended to be in information-intensive industries such as financial services and direct mail marketing, the technology is applicable to any company looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, well-integrated data warehouse, and a well-defined understanding of the business process within which data mining is to be applied (such as customer prospecting, retention, campaign management, and so on).
Some successful application areas include:
- Pharmaceutical companies
- Credit card companies
- Transportation companies
- Large consumer package goods companies (to improve the sales process to retailers)
Each of these examples has clear common ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These organizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them.
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