What is needed is a unified way of managing enterprise data across the entire organization. 
The requirement is for something like the data warehouse, but one that works both for :-
1) operational systems 
2) decision support systems 
3) legacy systems 
4) new systems, both internal and external
It has to remain useful when there is yet another corporate acquisition or merger, and must perform on all hardware platforms, operating systems, database systems, and all the other engines of data (such as online analytical processing). 
A major impetus to all this is e-commerce. 
The demands of e-commerce are really shining a spotlight on the inadequacies of existing approaches to data architecture and integration. So, of course, part of the requirement is a way to manage data across new e-commerce applications along with all the existing systems and components with which they must interface. 
This can be defined as the ability to “manage data across the enterprise” which provide better ways of implementing solutions and better ways of managing the data resource. 
These should provide the following facilities :-
a) make it easy to identify and source the data needed to support a new packaged application. 
b) make it possible to define a given data element once in the enterprise. 
c) establish the derivation of a given data element from its root sources. 
d) make business rules about data and have them apply across the enterprise. 
This requires an investment in architecture, direction, and set of standards to produce order from chaos. 
Studies indicate that 30 to 40 percent of the time spent developing applications in a major enterprise was spent on “data issues” - identifying the sources of needed data, evaluating them, extracting and transforming data, dealing with data quality problems, and correcting software errors due to data-related problems. 
All these activities would be completed more rapidly and at lower cost with a good data infrastructure. 
Why is this important? 
Because we need a better approach to data integration in order to implement applications quickly and efficiently. 
INSTALLING AN EXTERNAL SYSTEM 
When you install a major purchased application, typically it brings with it hundreds or thousands of data element definitions. 
It often must interface to a hundred or more existing systems, principally to exchange data. 
Managing this in most organizations is a large, costly nightmare. And why do we buy applications, rather than build them? So that we can reduce time to implement, cost and risk. 
Purchased applications are just one example: Similar problems exist in building applications, enhancing existing systems, and consolidating systems or databases. 
e-commerce sites
data marts 
enterprise data warehouses
operational data stores
The business executive’s idea of scalability is 'a solution I won’t outgrow.' 
In our major enterprises, we have outgrown the data in- frastructure. 
That is the major driving force for an Enterprise Data Architecture.
Data warehousing is one important and valuable form of data integration. 
But as implemented, for the most part, it is focused on historical, static data for query and analysis. 
E-commerce and other changes have brought us into a new era, the era in which we need data integration 
across the enterprise. 
So we need a Data Architecture which integrates all our 'Data islands'.