Collecting, administering and leveraging meta data presents challenges and risks that must be surfaced and managed to avoid unpleasant surprises in the areas of data warehousing, data administration and the system development life cycle at large. Absent careful planning, the surprises can overwhelm the benefits of any meta data initiative. Without accurate, current, high-quality meta data, development teams in both data warehousing and transactional systems are on a slope of diminishing returns, working harder and harder to maintain many-to-many interfaces. Meta data affects system analysis, version and change control, system interoperability, intersystem visibility and transparency, and related factors at an enterprise level.

The number one risk to meta data projects is that the team will end up with documentation, not actionable insight into diverse IT systems interoperations. Of course, system documentation is generally a useful and, at times, an essential IT artifact. However, it is subject to a number of well-known shortcomings. When produced in the form of electronic documents, it is an idle wheel. Documentation does not automate or move any part of the development or maintenance process. Documents are often obsolete the very day they are published. A document does not know whether it is obsolete or not; and a labor-intensive, manual effort is required to keep documents current. Inaccurate (outdated) information is often worse than no information at all because it is misleading. In contrast, a data modeling tool from which database data definition language (DDL) can be produced or which can be imported into an ETL (extract, transform and load) or query-and-reporting tool is a mechanism that enables meta data-driven design or maintenance. Because the meta data interchange between tools is incomplete, some manual labor will be required to manage the risk of a meta data idle wheel. Teamwork and discipline remain essential to managing this situation.

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