This month's DAMA column is contributed by John Schley.
My kids (ages 9, 7 and 5) love the book "Old Hat New Hat," a classic Berenstain Bears book by Stan and Jan Berenstain. In it, the main character goes into a hat store looking to replace his tired hat with something more fashionable. The clerk brings him one hat after another, but none seem to be what the client is looking for. One is too tall, and the next is too short. Too fancy. Too plain. On and on it goes, each hat failing in turn until the client tries on the hat he came in with. This one is just right, and off he goes, much to the dismay of the frustrated clerk.
One night, as I was reading this book for maybe the 3,287th time, it occurred to me that "Old Hat New Hat" matched some data modeling experiences I've had. Like the clerk in the hat store, I come in to the meeting with a pretty good idea of what kind of data structure the client needs. I'm up on all the latest modeling techniques and am eager to show off my proficiency with my chosen data modeling tool. I'm eager to produce a stunning data model diagram, jam-packed with important details such as the table and column names (in standard abbreviation format!), constraints, and indexes. And I'm sure they'll love the time savings they get when I automatically generate the data definition language (DDL) script that creates all these structures in their chosen database management system.
However, I am making the same mistake that the clerk in the hat store makes - I haven't considered the customer's needs and am only focusing on the products I have to offer. This mismatch manifests itself in delayed project timelines and increased maintenance costs. Done this way, data management can provide little business value, and the client ends up walking away, perhaps a purchased package, an outsourced solution or a minor "enhancement" to the existing system.
True business value lies in using the data model as an entry point for a program of data management that treasures data as the most valuable corporate asset. For a data processing organization, data is the raw material, and data management is the procurement area that secures high-quality input to the operation.
Developing and implementing an enterprise-wide data management strategy is not an overnight task. Like "getting fit" and "saving more," it is more journey than destination and each accomplishment invites new challenges.
One way that we as data professionals can effect this change is by "getting out" more.
- We need to get out of the physical data model and allow the semantic richness of a conceptual model or a logical data model to more fully document the business requirements of the project or enterprise.
- We need to get out of the implementation phase of the project and get involved in the design and ultimately planning stages so that the data structures we need will meet tomorrow's needs.
- We need to get out of the relational database management system (DBMS) focus that has defined our profession for too long. This leads not only to newer environments such as object orientation and unstructured data, but to the hierarchical, network and flat file systems that run many of our business-critical applications.
- Beyond this, we need to stop thinking only of data and support the conversion of this into knowledge and then wisdom. It is not data that differentiates competing organizations, but the ability to understand and act on that data that will determine winners and losers in the marketplace.
- Finally, we need to get out of our cubicles and see where our data management skills can solve real business problems. This means we must understand our company and its industry and be able to adjust our approach to apply our skills wherever necessary.
Which brings us back to "Old Hat New Hat." In this version, the clerk asks about the client's needs for the new hat. She understands his budget and uses for the hat. She helps the client define what he is looking for and works with him to design a hat that matches those requirements. The client leaves with a new hat that is exactly what he was looking for. And that's how I read it on the 3,288th time.
John Schley is vice president of Chapter Services for DAMA (Data Management Association).
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