In the hubbub of MDM and data governance, everyone’s lost track of the necessity of data standards and practices. All too often when my team and I get involved with a data warehouse review or BI scorecard project, we confront inconsistent column names in tables, meaningless table names, and different representations of the same database object. It’s as though the concepts of naming conventions and value standards never existed.
And now the master data millennium has begun! Every Tom, Dick, and Harry in the software world is espousing the benefits of their software to support MDM. “We can store your reference list!” they say. “We can ensure that all values conform to the same rules!” “Look, every application tied to this database will use the same names!”
Unfortunately this isn’t master data management. It’s what people should have been doing all along, and it’s establishing data standards. It’s called data management.
It’s not sexy, it’s not business alignment, and it doesn’t require a lot of meetings. It’s not data governance. Instead, it’s the day-to-day management of detailed data, including the dirty work of establishing standards. Standardizing terms, values, and definitions means that as we move data around and between systems it’s consistent and meaningful. This is Information Technology 101. You can’t go to IT 301—jeez, you can’t graduate!—without data management. It’s just one of those fundamentals.
