While the results have been great - I'm able to manage, communicate and produce more effectively than I ever have before - I spend a much larger portion of my time managing and integrating data, even at this personal level. Instead of an old-fashioned and single address book, I have multiple digital address books. I have email directories duplicated (and perhaps out-of-synch) across multiple systems. Try as I might, it's simply not simple to keep information integrated and in synch.

Unfortunately, over that same period of time, most organizations have been experiencing the same type of problems, but on a much larger scale.

It's clear that information integration challenges have grown over the past few years. From one perspective, enterprises are simply dealing with more data. And that means more challenges when you're talking about data quality and data consistency. But that's not the only change that most organizations' data has undergone.

Consider the range of other factors confronting organizations. For example, in most organizations, data is not only more distributed now, but it's also more heterogeneous, from legacy systems, to ERP systems to stand alone applications running on different database platforms. Also, a significant portion of many organization's data may be replicated-across geographies or across stove-piped systems. For years, organizations have been using ETL, EAI, replication and other technologies to move-and often duplicate-data throughout an organization. While this might get the job done in the short term, data duplication can create tremendous consistency and quality issues.

On top of these issues, organizations also have to deal with on-going changes to data structures. As business needs change, the data required, collected, and managed changes. However, managing these changes, across distributed, heterogeneous data stores, can be cumbersome and difficult, especially if there's no way to analyze the impact of such changes. A simple change to one field or column in a single database can have an enormous ripple effect across downstream applications that might rely on those database tables.