Meta data management and its use in enterprise information management has become one of the critical information technology (IT) focuses for both global 2000 corporations and large government agencies. As these entities look to reduce their IT portfolio and control their escalating IT costs they are turning to the technical functionality that a managed meta data environment (MME) can provide them. The organizations that have built well-architected enterprise-wide MME have achieved a tremendous amount of success. Unfortunately, like most popular IT trends, companies are making key mistakes in building and moving forward on their meta data management investments.

According to a 2003 study by the University of California at Berkeley, about 5 exabytes (an exabyte is roughly the equivalent of 1,000 petabytes, 1 million terabytes, or 1 billion gigabytes) of unique analog and digital information were produced worldwide in 2002, twice the amount produced in 1999. That’s a data explosion equivalent to half a million new libraries the size of the print collection of the Library of Congress, and this number will continue to expand exponentially. Although we haven’t seen any further studies, today – in 2009 – and after the massive use of social networks, such as FaceBook, YouTube, MySpace and Twitter, this number must be incredible! IBM estimates that about 85 percent of all data is unstructured and about 50 percent of the unstructured data is duplicated. Therefore, any discussion about a data strategy is incomplete without formulating a tactic for maintaining unstructured data.

Many enterprise information management (EIM) or data management projects don’t live up to their potential. EIM technology (data dictionaries, meta data management products, data modeling, data warehousing and business intelligence, data quality) have been around for a long time. Enterprise Data Management is a mature field, even if it has been called by different names, and the field is founded on strong principles. The approaches are well-structured, cover a wide variety of situations and have worked well for many organizations. Additionally, project management processes, tools and technologies are mature and well established. So the question arises, why do data management projects / programs fail?