When a company applies data analytics or business intelligence it is typically with a business goal in mind. Perhaps they look to improve spend analytics, fraud detection, logistics streamlining, product line rationalization or any other number of business outcomes.
So often when I speak to companies embarking on the road to data quality maturity their business outcomes are far less clear.
You’ll often hear that errors were detected in a particular system, or there are regulatory concerns, but tangible business results, and a clear roadmap of how they will achieve them, have far less clarity.
It’s also not uncommon to witness a data quality initiative kicking off without a clear business case and figures to back up the investment.
Why is this a problem you say? If executive management recognize that data is an asset does a business case even matter?
The answer of course is a resounding yes. All data quality initiatives, I believe, need a clear set of business outcomes to aim for. This will become the yardstick for success. If you create perfect quality data yet the business has not experienced any tangible gains then you will find it increasingly difficult to get buy-in for subsequent initiatives.
Particularly in the early days of your data quality initiative you need to set business goals that are achievable because coming up short is so easily perceived as a failure.
Also be mindful that executive leadership can change. I know of at least one team that routinely used data quality management as a mechanism for saving business units millions of pounds each year. A new management structure was brought in and they were shut down overnight in a “cost-cutting exercise”.
The lesson here is that you can never stop justifying the value that data quality brings to the business and you need a constant business mission or goal to direct the priorities and performance of your team.