James Koopmann

Data within your database is just that, data. It becomes information only when you can effectively extract and distribute in an understandable form. Using meta-data has come a long way; it allows for free-flowing information and is putting the ability to extract information into the hands of end-users.

When I first started my career in data processing, I began as a COBOL programmer. Back then, meta-data was strictly “data about data” and, in those days, we had to rely on s FDs, copy books, and a scarce set of documentation that comprised our meta-data. Such resources told us where data was located and what particular objects (tables) and columns meant.

Today, meta-data has grown into a complete subject area encompassing such terms as Knowledge Management, Corporate Data Dictionaries, and Enterprise Meta-Data Repositories. All of these classifications have the single goal to categorize the underlying information buried within databases so that end users can get to information faster. They do this by employing tools that allow end users to unlock the mysteries of what information is available and where it is located.

Imagine that you are cornered in the break room by the CEO or called into a sales meeting and asked to produce a report that describes demographic information for current customers. This task seems to be quick and easy, at least at face value. But, you quickly remember that you have distributed databases that control different product lines, and the way objects have been defined within those databases are quite different. To compound the issue, your CEO is starting to describe “customers” as anyone who has purchased products or services from you including potential customers that are not stored in a database yet but are in multiple spreadsheets maintained by your sales reps. No doubt, it is becoming obvious that these requirements will be changing and you begin to feel like you will be trying to hit a moving target.