‘Data quality’ can be quite a daunting prospect.
In a recent study, 94% of organisations surveyed revealed they suffer from common data errors.*
So if you think data quality sounds scary, you are not alone.
It goes a little something like this...
Marcus: “How are you moving forward with your data quality strategy?”
Customer: “We aren’t. We have hit a stumbling block in getting the business case through the board. Until we get approval nothing can happen. They just don’t see the value in the project.”
It’s not a load of typos – it’s a deliberate mess.
I bet you can read what it says…
Even when all the letters in a sentence are jumbled, most of us can still read between them and make out every word – so long as the first and last letters are correct. Aren’t we clever?
Data quality practitioners run the risk of that famous adage, “running around like headless chickens”, without convincing and tangible evidence for data improvement. Data quality management provides us with a form of reassurance, the data quality threshold, typically calculated as a percentage of poor quality records against the total number of records in a data entity.