Introduction

In South Africa, unlike many other countries, daylight savings time is not implemented. Rumour has it that when the idea was first proposed it was rejected by the government of the day on the basis that it “would confuse the cows”, who were used to being milked at a particular time. While this may seem unrelated to data quality, there is a valuable lesson to be learned.

The Relevance for Data Quality

Data quality issues often stem from established habits and behaviours in data capture. Fortunately, simple adjustments can significantly enhance the ongoing quality of data.

However, resistance to change is a common obstacle, often justified with statements like “We don’t do things that way here!” or “Management will never support that!” or “Our staff can’t learn to do that.” Embracing change management becomes essential for any genuine effort to improve data quality.

Leveraging Automation

Automation can play a crucial role in improving data quality by providing automated fixes to standardized errors.

For example, it can help highlight and correct misfielded data or potential duplicate records as they are being captured. This approach allows users to continue working according to their habits while programmatically addressing certain issues.

While change can bring about some initial discomfort, it is necessary for progress. Just as cows would have coped with daylight savings, the benefits of implementing changes will ultimately outweigh any temporary challenges.

Conclusion

Data quality is a vital aspect of effective data governance.

By recognizing the need for change and embracing it, organizations can unlock the true value of their enterprise information asset. Remember, don’t let the fear of “confusing the cows” hinder your pursuit of data quality excellence.