Informatica Perspectives

I attended my first parent-teacher meeting the other day for my five-year old daughter. Another one of those “life stage” events done and dusted – I remember dreading the annual meeting when I was a kid. The notion of my parents and my teacher comparing notes on my behaviour was too much to bear – somebody was eventually going to put two and two together and find out I was up to no good.

It all got me thinking about a recent blog post by my esteemed colleague Garry Moroney. His post Mobilizing the Data Quality Army outlined the level of effort, thought and planning that the US Department of Education is putting into data quality.

As Garry points out dealing with data quality in a large, disconnected organization such as the US schools system is not a trivial exercise. But if you were to only read that one post you might be overwhelmed by the potential size of the data quality task in front of you.
The good news is you don’t have to do it all in one go. At the same time tactical solutions aren’t the answer either. Data quality processes can be phased in over time, but it is important to start with a holistic view and solid process-oriented approach that can be reused and ultimately deployed across the entire organization.

Data quality improvement is not just about fixing data. First you need to understand the true level of data quality within your organization, find ways to clean up the data already in use and then stop low quality data from getting into systems in the future.

Establishing a baseline of your current state of data quality; so that one can identify the critical failure points and determine improvement targets should be your starting point. But, achieving the high levels of data quality needed to improve business efficiency and transparency is an iterative process that needs to be tracked, managed, and monitored. Therefore, being able to measure and monitor data quality throughout the data quality lifecycle and compare the results over time is an essential ingredient in the proactive management of ongoing data quality improvement and data governance. So a bit of effort put into planning for measurement and monitoring at the start will pay dividends for a long time to come, and enable you to keep an eye on the big picture no matter how small you start.