About Data Mining
Databases today can range in size into the terabytes —
more than 1,000,000,000,000 bytes of data. Within these masses
of data lies hidden information of strategic importance. But
when there are so many trees, how do you draw meaningful conclusions
about the forest?
The newest answer is data mining, which is being used both
to increase revenues (through improved marketing) and to reduce
costs (through detecting and preventing waste and fraud). Worldwide,
organizations of all types are achieving measurable payoffs from
this technology.
Data mining finds patterns and relationships in data by using
sophisticated techniques to build models — abstract representations
of reality. A good model is a useful guide to understanding your
business and making decisions.
There are two main kinds of models in data mining: predictive
and descriptive. Predictive models can be used to forecast
explicit values, based on patterns determined from known results.
For example, from a database of customers who have already responded
to a particular offer, a model can be built that predicts which
prospects are likeliest to respond to the same offer. Descriptive
models describe patterns in existing data, and are generally
used to create meaningful subgroups such as demographic clusters.
In addition to algorithms, data mining software usually has
features to simplify the graphic representation of the data (visualization
tools) plus interfaces to common database formats.
Data mining is only one step in the knowledge discovery process.
Other steps include identifying the problem to be solved, collecting
and preparing the right data, interpreting and deploying models,
and monitoring the results. The real key to success, however,
is to have a thorough understanding of your data and of your
business. Algorithms can provide meaningful results only when
sensibly directed.
The potential payoffs are enormous. Innovative organizations
are already using data mining to locate and appeal to higher-value
customers, reconfigure their product offerings to increase sales,
and minimize losses due to error or fraud. The list
of data mining applications is surprisingly broad.
To learn more:
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