Data Integration is the process of combining two or more data sets together for sharing and analysis. It is important to network designers of both service providers and enterprise. Integration of data technologies has accelerated rapidly in recent years. Customers are leveraging investment in network infrastructure to take advantage of integrated applications such as voice applications. This may increase the productivity of users and differentiate services.

Data integration requires combining and matching information in different sources, and resolving a variety of conflict. Data integration means working within your existing application environment to ensure that high quality information arrives at new data targets during data migration or consolidation efforts.

The availability of integrated data from multiple independent, heterogenous data sources is crucial for many applications. One of the most important applications of data integration is the merging of legacy databases and infrastructure with current enterprise applications.

In order to minimize the risk to deliver the project on time and within budget, you need proper data integration techniques. Data integration is not just about matching and linking data together. Its also about gaining access to the right data sources at the right time.

Disadvantages of Data Integration are as :
1) Data integration is expensive and the commitment of management for integration is often short-lived.
2) There is little or no encapsulation of the applications functionality.
3) Data Integration enables the integration of data only. It is not well-suited for richer command or event semantics such as Customer ABC Moved, or Place Order.
4) Most data stores use a representation that is different from the underlying programming model of the application layer. The difference in representation requires a translation between the two paradigms.
5) You can easily resolve syntactic differences between systems, but resolving semantic differences can be much more difficult.