Enterprise Data Modeling Practices - High-Performance Web Databases: Design, Development, and Deployment - page 157

Chapter 11

Enterprise Data
Modeling Practices

Wolfgang Keller

E

NTERPRISE DATA MODELING HAS RECENTLY BEEN IN THE CROSSFIRE OF

.

ATTACKS Most critics say

that such an inflexible, top-down, and centralist approach is not equipped
to deal with problems like changing environments, pressure from global
markets, and decentralization. A central data model is said to be a contra-
diction to a decentralized organization. Critics say that the rapid change
will make the data model outdated before the data analysts can bring it to
the market.

ASSERTING THAT IT IS COMPLETELY USELESS

If analyzed, most of the arguments against data modeling have their roots
in existing problems and frequent errors in practice. But this does not mean
that enterprise data modeling in general is faulty or useless. Many of the
arguments can be attributed to improper use of data modeling equipment.

Data modeling was invented to avoid the problems associated with iso-
lated, nonintegrated systems. This chapter is designed to help users avoid
some typical errors by focusing on correct data modeling practices.

ARGUMENTS AGAINST ENTERPRISEWIDE DATA MODELS

The following is a list of common arguments against data modeling.
Many financial institutions and other organizations are reconsidering their
data modeling practices in light of the bad press it has been receiving. A
financial application is used as an example throughout this article because
information processing is one of the core activities of the banking business.

Critics of data modeling assert that it:

• Is not able to keep pace with new developments. Information process-
ing systems of financial institutions must be adapted to change rapidly
because of the globalization of markets and new requirements for cus-
tomer service.