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.
One such example is a CRM system, which integrates information from different departments such as customer service, social media and sales/marketing. This enables the sales and marketing people in targeting an existing customer based on previous purchasing history and other contacts with the company that are listed in the profile. Another case where integrating an information storage system becomes essential is when two companies enter into a merger.
This process can be implemented at the middleware layer as well as on the application level. Physical warehouses can be set up as a new system, or users can simply be provided unified views through virtual integration. It might be better to look at these choices individually in order to understand each one and determine which is more suitable for specific situations.
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 is actually the simplest way to integrate because it just needs to execute pre-defined queries to display all the required information as a view. For example, this can be done when employees need to see a customer's profile. This is a classic pre-defined query which calls records from different sources with the customer ID as the main index field, and then displays all of it in one 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.
One such example is a CRM system, which integrates information from different departments such as customer service, social media and sales/marketing. This enables the sales and marketing people in targeting an existing customer based on previous purchasing history and other contacts with the company that are listed in the profile. Another case where integrating an information storage system becomes essential is when two companies enter into a merger.
This process can be implemented at the middleware layer as well as on the application level. Physical warehouses can be set up as a new system, or users can simply be provided unified views through virtual integration. It might be better to look at these choices individually in order to understand each one and determine which is more suitable for specific situations.
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 is actually the simplest way to integrate because it just needs to execute pre-defined queries to display all the required information as a view. For example, this can be done when employees need to see a customer's profile. This is a classic pre-defined query which calls records from different sources with the customer ID as the main index field, and then displays all of it in one 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.
0 comments:
Post a Comment