At its core, data integration is the process of combining data from different sources so that users and applications see it as coming from a single source. There are many ways to do this, and there are just as many possible applications too. It can be used for everything from research to marketing and corporate mergers.
The process has to be business-oriented, instead of being a purely IT initiative. In other words, it has to be a solution to enhance business processes rather than as a better or cheaper way to store and manage information. Many systems based on this principle are already in use and quite common in the corporate world.
For example, a company that has separate databases for their marketing/sales and service departments may want to integrate all of it into a centralized repository. Otherwise known as a CRM system, this allows the marketing and sales people to target existing customers based on information collected from the other departments. Two companies entering into a merger or acquisition will likewise need to integrate their respective systems.
This can be done on the application level or on a middleware layer. It can also be done using virtual integration to offer simplistic views, or by going in for full-fledged physical warehousing. Let's take each of these one at a time, in order to gain an understanding of how they work and find out which one would be more appropriate under what conditions.
If the application has the built-in logic to extract and combine information stored in different sources, there is no need to create a new centralized database. The same applies for a solution on a middleware layer. In this case, the logic in the middleware will provide every application with whatever information it needs from any and all sources at the back end.
Virtual integration doesn't actually involve creating a separate tool or software solution. It merely defines a set of views that can help users pull together information from disparate sources. For instance, if an employee wants to view one customer's profile, the pre-defined query simply calls in all the records for this unique customer ID from every department and presents it together as a single unified view.
Warehousing is a completely new system which can siphon and store information from any number of sources. This is mostly done only at an enterprise level, where vast amounts of data coming in from all of a company's departments and locations can be collected, stored and managed in massive data centers. This centralized system can then be used by applications and users to gain enterprise-wide access, reporting and analysis capability.
The choice of a data integration technique and the scope of the process have to be made after careful consideration of a whole lot of issues. The intended usage and the number and type of sources are primary factors, along with the cost. Not to mention the security and backup implications. Other related issues to consider include migrations, synchronization and MDM (master data management).
The process has to be business-oriented, instead of being a purely IT initiative. In other words, it has to be a solution to enhance business processes rather than as a better or cheaper way to store and manage information. Many systems based on this principle are already in use and quite common in the corporate world.
For example, a company that has separate databases for their marketing/sales and service departments may want to integrate all of it into a centralized repository. Otherwise known as a CRM system, this allows the marketing and sales people to target existing customers based on information collected from the other departments. Two companies entering into a merger or acquisition will likewise need to integrate their respective systems.
This can be done on the application level or on a middleware layer. It can also be done using virtual integration to offer simplistic views, or by going in for full-fledged physical warehousing. Let's take each of these one at a time, in order to gain an understanding of how they work and find out which one would be more appropriate under what conditions.
If the application has the built-in logic to extract and combine information stored in different sources, there is no need to create a new centralized database. The same applies for a solution on a middleware layer. In this case, the logic in the middleware will provide every application with whatever information it needs from any and all sources at the back end.
Virtual integration doesn't actually involve creating a separate tool or software solution. It merely defines a set of views that can help users pull together information from disparate sources. For instance, if an employee wants to view one customer's profile, the pre-defined query simply calls in all the records for this unique customer ID from every department and presents it together as a single unified view.
Warehousing is a completely new system which can siphon and store information from any number of sources. This is mostly done only at an enterprise level, where vast amounts of data coming in from all of a company's departments and locations can be collected, stored and managed in massive data centers. This centralized system can then be used by applications and users to gain enterprise-wide access, reporting and analysis capability.
The choice of a data integration technique and the scope of the process have to be made after careful consideration of a whole lot of issues. The intended usage and the number and type of sources are primary factors, along with the cost. Not to mention the security and backup implications. Other related issues to consider include migrations, synchronization and MDM (master data management).
About the Author:
Peggie K. Lambert loves working and researching the world of data integration. If you are looking to learn more about data integration solutions then she recommends you refer to www.liaison.com.
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