Unlike many other countries, South Africa does not implement daylight savings time. Rumour has it that when the idea was first proposed the government of the day rejected it on the basis that it “would confuse the cows”, who were used to being milked at a particular time.
What is the relevance for data quality?
Many, if not all, data quality issues can be traced back to standard habits and behaviour in data capture. In many cases, quite simple adjustments are suggested in order to improve the on-going quality of data.
The most frequent reason given for not making these changes is a variation on “it will confuse the cows.”
“We don’t do things that way here!”
“Management will never support that!”
“Our staff can’t learn to do that.”
Change management is a critical part of any genuine effort to improve data quality.
Automation can play a role by providing automated fixes to standardised errors – for example highlighting and correcting mis-fielded data or potential duplicate records as they are being captured. Automation can allow users to continue to work according to their habit, but correct certain issues programmatically.
At the end of the day however, changes will have to be made. With change comes a degree of pain – maybe even a few confused cows. But ultimately, the benefits should exceed the pain.