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Will Best Practice Keep Your Data Quality Boat Afloat?

Some of attendees at a recent workshop that I ran told me that data quality was assured in their enterprise because they always followed industry best practice.  On the surface this seems to be a compelling argument.  If you are following best practice all must be right with your data quality.  Or is it?

If you are aboard a ship in the middle of a stormy sea will best practice keep your boat afloat? Well that depends on what best practice you are talking about. It is best practice for bailing out or for fixing leaks?

It might be more effective to practice best practice for preventing leaks, or building inherently buoyant vessels or avoiding sailing into storms.

The same holds through for data quality.  You may be very proud of your best practice for deduplication but would it not be better to have best practice that prevents duplication occurring?

Would it not be best to practice structured front-end business and data analysis and modelling that identifies and defines:

  • all core business functions
  • the data entities, attributes, relationships and unique identifiers required by these functions

It might also a good practice to have a structured design approach that effectively converted all of these data elements and structures into robust databases populated by properly modelled and implemented processes.

Would it not be better all round for best practice in your enterprise to put in place a fully integrated set of structures, processes, systems and standards that enable your team to create error free data as an automatic and painless by-product of doing their day-to-day work?

Well, most people might think so.  However, some would argue that those people in the “bailing bucket” business (data quality software?) might far rather see lots of leaky boats as opposed to buoyant vessels! I couldn’t possible comment.

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