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Tips for your Data Quality Business Case


Janani Dumbleton 4 minute read Data quality

"How do I build a successful business case for data quality?"

This is a frequent question I hear from businesses at our data quality events and seminars. However, in an age where data quality has climbed up the corporate agenda, it mystifies me why this seems a recurring topic.

Many businesses have some form of data quality programme in place, however, recent research we conducted reveals that data quality issues are still occurring. The Experian Data Quality 2014 Global Research found that 99% of organisations have a data quality strategy, but still 86% believe their data is inaccurate in some way, concluding that all is not well in the data quality world.1 It is worrying that while data quality is certainly a hot topic, on the ground there is an obvious struggle when trying to get the investment for the right type of solution.

In a recent blog on this very topic, it was stated that “industry commentators will be quick to point out that data quality projects are usually born out of an ‘event’ that has caused significant pain to the business.” Events that cause panic are more likely to produce a very reactive approach to data quality, often looking for the immediate fix.

What is missing here is the ability to step back and look at the bigger picture. Often when looking at a data quality problem within a silo, the true impact to the business as well as the factors that influence the problem may not be as apparent. Time can be a big constraint, forcing many to look for a quick fix as a matter of priority.

However, this is where technology can help. Technology can be seen as an outcome of any business case, however, in this case it can help gather that all important evidence. Data profiling technology has been around for a while, often linked with understanding the state of data when identifying risks in data driven projects. Data profiling can take away the constraints of time, and when applied in a logical order the true scale, impact and cause of data quality issues can be rapidly uncovered. The proof lies in the data; it is just a matter of following three logical steps:

  1. Firstly, profile data outside the silo and discover the scale of the problem. If the issues occur in more than one area, across business units or data types, you have a stronger justification that a problem exists and needs to be actioned.
  2. Quantify the impact of poor data quality by using measures that the business cares about, like revenue, profit or customer satisfaction. This will make your business sponsors sit up and notice why you need investment. Proving that 5% of poor quality records can impact 25% of your business revenue can be a powerful statement when it comes to getting approval for your business case.
  3. Investigate the root cause of issues by finding patterns in problem data. Often factors like people, processes and technology can tell you where the issues occur. Preventing a problem from occurring by investing in validation technology or training users can be more effective than just cleaning data reactively. This is often the first step that leads to more compelling recommendations in your business case.

Following these steps can create a stronger business case for data quality, moving your business from reactive measures to a more proactive data quality strategy.

Read the advisory note, “Utilising profiling technology to drive the business case for data quality” to find out more about these three steps.

1 2014 Experian Data Quality Global Research.