It was a summer afternoon and I was driving to the beach in my friend’s car, who was in the passenger seat (she didn’t particularly enjoy driving). It was an older edition Honda Civic that she had driven many times including the 17 miles to get to my house. It didn’t cross my mind to have any concern.
About 15 minutes later, on a busy road and approaching a red light, I had the brake pedal to the floor and the car was not slowing down. In what felt like slow-motion, I pulled up the hand brake and we screeched and smoked our way to a halt mere inches from the bus that was in front of us. Crisis (barely) averted and I live to blog about data quality.
My friend later told me that her brother assured her that the car was “fine”, which leads me to my first point in how owning a car relates to data quality:
- Don’t wait until something happens before you care about data quality - Your car dashboard provides key metrics to monitor how you’re going and regular service ensures any potential issues are identified and corrective action is taken. While your data quality might seem “fine”, without data quality metrics available and regular maintenance in place, you really don’t know the true health of your data and you don’t want the Fed to question your regulatory reporting before you have a look under the hood.
- Pay attention to ownership and stewardship of data - I was not driving my own car, nevertheless, I was the one in the driver seat at the time we were behind a bus without working brakes. When it comes to data quality, data stewardship and ownership of the processes within teams that use and influence data is important to minimize any negative impact downstream.
- Ensure the data quality solution is fit for purpose - When you’re selecting a car, the “best” car for you depends on your need. Do you need a utility truck or are you better off with a bicycle? Will you need an auto transmission instead of manual? With data quality, it’s important to first define what “quality” means for your organization and then identify the appropriate approach. What are you trying to achieve? Do you need to deploy a dedicated data quality tool right now or would another tactic be suitable?
- Data quality management is as part of wider data governance framework - While there are many different cars on the road heading to different destinations, all need to adhere to a common set of road rules and traffic laws. Don’t overlook the significance for data quality management to be part of a broader governance framework. A common understanding of critical data elements, data lineage, metric calculations and data ownership are all important to ensure the various needs of the organization can progress together.
- Provide regular maintenance and monitoring to maintain an effective data quality solution - To maintain a car, you have your regular full service but there’s also the everyday things like changing your oil, cleaning your car, checking tire pressure. Data Quality may also involve a two-prong approach – you may need a more intensive, IT-managed element but there’s also the value that business teams can add via self-service options.
- Evolve your data quality solution as the organization changes - As your lifestyle changes, the car that you previously selected may need to be upgraded or is no longer suitable. As your organization changes, so does the Data Quality solution. It is not a solution that you set up and then forget about, it needs to be constantly monitored and evolving.
Data quality management is crucial, multi-faceted and the complexity is compounded by the evolving data landscape. While there is no one-size-fits-all solution to prescribe, there are a range of tools and best practices that can be utilized to help you ensure that you’re not driving without a working brake pedal. In our coming blogs, we will explore the concepts above in more detail as well as how we have helped firms turn “data quality” from a buzz word into solutions.