Enterprise Information Integration (EII) has steadily gained momentum as a must-have tool in the arsenal of data architects. Collecting information from an array of disparate sources and fusing it together in a unified view is just the ticket for a range of applications, including operational dashboards, risk management systems, compliance applications and more.

In addition to the deployment benefits of EII — it's light, flexible, and on demand — there is another key benefit: the ability to gain operational intelligence from non-traditional sources. This ability unlocks powerful advantages that companies have within their information assets, and gives an edge to companies that know how to harvest key information.

The Emergence of EII

What makes EII more than the latest acronym brewed up by vendors and their analysts? Because the technology is relatively new, there are still some skeptics. But the answer is that EII's time has come, and it is a truly useful technology that fits perfectly with today's technical landscape and economic climate.

Conceptually, the idea behind EII is quite simple. EII integrates information at the information level. If this definition seems circular, then contrast it with other common approaches. Enterprise Application Integration (EAI) integrates at the application level, moving information from one packaged application to another. Portals integrate at the presentation level, providing a collection of information to users through an integrated Web interface. What's lacking is the ability to collect information from multiple back end data sources — including packaged applications — and make it available to multiple potential user interfaces. EII integrates the information itself into one, unified view. This is not possible with EAI or by using a portal. EII fills the gap.

From a technical viewpoint, the EII approach is also quite different. It uses a distributed query approach to collect and integrate information from multiple sources. This is commonly referred to as federated query. With EII, queries are distributed to data sources and then the results are joined, or federated. This is quite different from other integration technologies. EAI typically passes messages from one application to another over a hub or bus. ETL involves moving data physically from one location to another, creating redundant copies of data in data stores, with their own infrastructure and administration costs. In many cases, the replicated data is summary data, in which case details are no longer available. Basically, EAI and ETL are both push mechanisms. EII is a pull mechanism, where a federated query goes out and finds the data needed by a user application and puts it into user view with context.

Adding Intelligence: The Next Level

What is enticing about EII is the potential to take a powerful additional step. Since the information is fetched and put into a common context, it is possible to add intelligence to the integration. In other words, why stop at integration when the data can deliver real-time intelligence? This is called 'on-demand intelligence.'

There are many situations where on-demand intelligence could be useful: in operational dashboards, where there is a desire to see how certain functions are performing; in risk management systems — especially in financial institutions — where positions can change trade by trade; in consumer or retail operations, where some hourly metrics could help plan truck routes or even advertising buys.

While this may seem heretical to some data warehouse administrators, fear not. The idea of on-demand intelligence is complementary. First, there will be no replacing OLAP for deep analytical processing. On-demand intelligence allows less complex, but timely and useful, intelligence to be gleaned. Because of this, there is no need to retrofit or adapt an existing data warehouse to perform a task for which it was not created. Finally, an EII tool could access a data warehouse as a source that could be combined with information pulled from other systems, giving new reach and usefulness to the information it contains.

The Role of XML

XML is the perfect 'information architecture' for on-demand intelligence because it spans disparate data sources. Everything from Web content to documents, to messages and structured data can be represented in XML. It is also a tagged language: the information elements are surrounded by tags that explain their meaning. Tags specified in XML schema help define specific instances of XML data and give the tags specific meaning for an application, or even for an industry. These tags ease many issues around data meaning and common naming conventions that have plagued data architects for years.