In my last post, I gave you a fairly trivial example of a decision based on belief instead of data. However, there is a long way from simple fact checking to a considered approach to data governance and data quality.
The next steps in this data quality journey are also fairly straightforward.
1. If it’s broken, then fix it.
Throughout your organisation, this is undoubtedly happening. Good, conscientious team workers are spotting errors and inconsistencies and, without fuss or fanfare, get on and fix them. If you are really lucky, then those people will train their teams and successors to also look for these errors and inconsistencies, and get on and fix them. If you are really, really lucky, then you have people all around the organisation fixing those errors and inconsistencies wherever they occur.
At some point though, you might wonder if it’s worth paying a load of people who create errors, and a load more people to fix them.
2. If you fix it early, it’s cheaper and easier.
We are moving now to doing some root cause analysis. We encourage our people to flag up where there are data problems, with a view to getting them fixed at source. Things are much better now, and you may now be ahead of some of your competition, but we are still relying on reactive behaviour and the best endeavours of your people.
3. We introduce the concept of data quality.
The good news about starting this stage is that it doesn't involve anyone doing any more work. That is because your first move will be to acknowledge and take credit for all the great work that is already being done.
In my next article, I’ll move on to an overview of the elements which make up a structured data quality programme.
Until next time,
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