TRENDS IN DATABASE TECHNOLOGY - Database Design and Development: An Essential Guide for IT Professionals - page 656

The user issues a query.
The results of the query get stored in a small, local multidimensional database.
The user performs analysis against this local database.
If additional data are required to continue the analysis, the user issues another
query. And the analysis continues.
Data Mining
Most certainly you have heard about data mining. Most of you know that the tech-
nology has something to do with discovering knowledge. Some of you possibly know
that data mining is used in applications such as marketing, sales, credit analysis, and
fraud detection. All of you know vaguely that data mining is somehow connected
to data warehousing. You are right. Data mining is used in just about every area of
business from sales and marketing to new product development, inventory man-
agement, and human resources.
There are perhaps as many variations in the definition for data mining as there
are vendors and proponents. Some experts include a whole range of tools and tech-
niques from simple query mechanisms to statistical analysis as data mining. Others
simply restrict data mining to mean knowledge discovery techniques. A workable
data warehouse, although not a prerequisite, will give a practical boost to the data
mining process.
Why is data mining being put to use in more and more businesses? Here are
some basic reasons:
• In today's world, an organization generates more information in a week than
most people can read in a lifetime. It is humanly impossible to study, decipher,
and interpret all those data to find useful competitive information.
• A data warehouse pools all the data after proper transformation and cleansing
into well-organized data structures. Nevertheless, the sheer volume of data
makes it impossible for anyone to use analysis and query tools to discern useful
patterns.
• In recent times, many data mining tools have appeared in the market that are
suitable for a wide range of applications. We are seeing the maturity of the tools
and products.
• Data mining needs substantial computing power. Parallel hardware, databases,
and other powerful components are becoming very affordable.
• As you are aware, organizations are placing enormous emphasis on building
sound customer relationships, and for good reasons. Companies want to know
how they can sell more to existing customers. Organizations are interested in
determining which of the customers will prove to be of long-term value to them.
Companies need to discover any existing natural classifications among their
customers so that the classifications may be properly targeted with products
and services. Data mining enables companies to find answers and discover pat-
terns in their customer data.
• Finally, competitive considerations weigh heavily on your company to get into
data mining. Perhaps your company's competition is already into data mining.