21. May 2008 by Rick Abbott.

Enterprise Information Integration is a hybrid of service oriented architecture (SOA), enterprise application integration (EAI), virtual data integration, and physical data integration, with a little meta-data management thrown in for kicks.  The concept is to throw a layer on top of physical data storage, in order to provide a single interface to end users or other applications. (Wikipedia entry)

This layer generally includes interfaces into physical data stores, message buses, and other sources of data, with a meta data component to tie it all together.  A conceptual data layer is then defined which is modeled based on the consumer desired view of the world.  The final piece is the interface methods for end users or applications to access the conceptual information view.

That’s the technical view of EII, but the real business benefit is in providing real or near-real-time access to business information without having to navigate the underlying data stores.  The value is realized on two levels:

1. Integration of data elements across disparate systems - this is the grunt work associated with mapping a customer name between two systems when one stores the name in a single field and the other stores it in three separate fields (First, Middle, Last)
2. Providing contextual understanding of information - this is where the meta-data comes into play, by providing the end user with background and additional meaning to the information

Of course there are a number of companies claiming to provide a complete EII solution, but in my mind a true EII solution is too broad for any one product.  It should be treated as a business solution by starting with the benefits and working back to the appropriate technologies required to deliver those benefits in the most cost effective manner possible.

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21. May 2008 by Rick Abbott.

I almost didn’t bother with this one, since it’s almost too generic now to be useful, but it does deserve a few sentences if nothing more than to pay respects.  (Wikipedia has a good definition and some of the history around the term if you want background.)

Nowadays, I find that the term is hardly used anymore, probably because of the proliferation of more specific terms that describe the individual components (e.g., ETL, data quality, EII).  I think another reason is the move to more real-time analytics, and the term “data warehouse” conjures up visions of static information sitting in an Oracle (or Teradata) database. 

Ten years ago, all you had to do was say I’m building a “data warehouse” and most people knew what you were talking about.  Now, it could mean a dozen different things, which makes communicating more difficult.  It would be nice to have all of this wrapped up into one nice term that everyone can agree upon, but I doubt that’s going to happen, which is actually a good thing.  It means that people realize (both business and technical) that data driven solutions are not one-size fits all, and that there are a myriad of implementation options available.

The “Data Warehouse” isn’t dead, it just lives on in it’s numerous children and grandchildren.

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