Does One-Size-Fits-All Data Management Work?

“All Data Management initiatives are essentially the same.”

“Every Data Management initiative is different.”

BOTH of these statements are TRUE, at the same time.

Can you think of any examples where either of them are false?

THE SAME: Every data management initiative I have ever seen, was setup to address one or more business issues or opportunities, which could be categorised as being one of a set of "common" challenges that many businesses face, and they have also all involved implementing one or more of the standard data management capabilities needed to solve those issues.

DIFFERENT: However, the specific ways in which the issues manifested themselves, for each company, and the specific business impacts, along with the specific ways in which data management capabilities needed to be implemented, to directly and effectively address the issues, were always different.

Any attempt to just generically implement a set of “standard” data management capabilities, without taking into consideration the specifics of the organization, WILL ALMOST CERTAINLY FAIL.

Unfortunately, I have seen this attempted too many times to count, including several examples where very experienced and capable data professionals have fallen into this trap. Often with data professionals, it's either because they've seen something work somewhere before and assume it will work everywhere if it's implemented in exactly the same way, or because they get too wrapped up in the theory and lose their connection to the things that will deliver any real tangible business impacts.

All of the data management capabilities that a data professional will talk about are most likely absolutely the right things to be doing (metadata management, data quality management, reference data management, etc...) However, if they are just implemented in a paint-by-numbers way, they are generally a waste of time.

It's always important to invest the time into understanding the business and its priorities, then develop a strategy for execution that directly addresses those priorities.

Have you seen any examples where paint-by-numbers data management has actually worked? (Or has failed really badly?)

#Data #Value #Success #Practical #Expert #Tips #DataValueSuccess #DataValueBytes #DataManagement



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