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Q5 How Do Organizations Use Data Mining Applications? 359

Unfortunately, all of this flexibility comes at a cost. If the database is large, doing the neces-
sary calculating, grouping, and sorting for such dynamic displays will require substantial com-
puting power. Although standard commercial DBMS products do have the features and func-
tions required to create OLAP reports, they are not designed for such work. They are designed,
instead, to provide rapid response to transaction-processing applications, such as order entry or
manufacturing planning. Consequently, some organizations tune DBMS products on dedicated
servers for this purpose. Today, many OLAP servers are being moved to the cloud.

Q5 How Do Organizations Use Data Mining

Applications?

Data mining is the application of statistical techniques to find patterns and relationships among
data for classification and prediction. As shown in Figure 9-20, data mining resulted from a con-
vergence of disciplines. Data mining techniques emerged from statistics and mathematics and
from artificial intelligence and machine-learning fields in computer science. As a result, data
mining terminology is an odd blend of terms from these different disciplines. Sometimes people
use the term knowledge discovery in databases (KDD) as a synonym for data mining.
Data mining and other business Data mining techniques take advantage of developments in data management for process-
intelligence systems are useful,
but they are not without their ing the enormous databases that have emerged in the last 15 years. Of course, these data would
problems, as discussed in the not have been generated were it not for fast and cheap computers, and without such computers
Guide on pages 378–379. the new techniques would be impossible to compute.
Most data mining techniques are sophisticated, and many are difficult to use well. Such
techniques are valuable to organizations, however, and some business professionals, especially
those in finance and marketing, have become expert in their use. In fact, today there are many
interesting and rewarding careers for business professionals who are knowledgeable about data
mining techniques.
Data mining techniques fall into two broad categories: unsupervised and supervised. We
explain both types in the following sections.

Unsupervised Data Mining

With unsupervised data mining, analysts do not create a model or hypothesis before running the
analysis. Instead, they apply a data mining application to the data and observe the results. With this
method, analysts create hypotheses after the analysis, in order to explain the patterns found.

Statistics/ Artificial Intelligence
Mathematics Machine Learning

Huge Data
Databases Mining

Sophisticated
Cheap Computer Marketing, Finance, Data
Processing and and Other Business Management
Figure 9-20 Storage Professionals Technology
Source Disciplines of Data Mining