Getting buy-in for data quality can be a real challenge, and a big part of the problem is the language barrier. If you’re on the data quality side, the financial and strategic terminology used by management may seem alien to you. Similarly, there is probably nothing more confusing to a senior manager than your talk of data quality dimensions and profiling statistics.

So how do we overcome these barriers?

The answer lies in creating a common environment. You need to create a platform where data quality practitioners can provide information in a context that is meaningful to business leaders. This enables management to drill down into the world of data and the unknown land of information chains and quality metrics that they never get to see.

To begin the process of creating this common environment, you need to carry out one critical activity that is often overlooked by data quality teams: You must link business metrics and data quality metrics to sustain an engaged business audience.

What do I mean?

Let’s take the example of a typical data quality assessment. You examine the completeness, validity and structural integrity of your customer data. You could simply present this information back to the business in a standard format listing the overall completeness of certain important fields such as name, address, email and so on.

But this isn’t the language of the business. They want to know things like:

• “Are these customers current or terminated contracts?”
• “How will these data quality issues impact future direct marketing campaigns in terms of profitability, reach, segmentation?”
• “Which department is generating the most defects and what are the trends?”
• “Which are the 10 most profitable or long-term customers with these issues?”

You need to have these answers close to hand or, more importantly, you need to give the business access to this information on their terms using tools they understand.

To do this, you need to stop thinking of data quality, financial and performance metrics as separate islands of information. They are all connected. They all tell you something about how the business is operating and, more importantly, what actions need to be taken.

When I have created an environment where business metrics are linked to data quality metrics, the business engagement increased dramatically. Managers would often ask to be given access to the dashboard there and then in the meetings. Within minutes they were drilling down, slicing, dicing and making informed decisions on where to focus improvement.

To make this happen, look at your data and identify where you can pull in additional datasets to provide richer meaning. For example, in one utilities company I overlayed their equipment on a UK map so they could see their data quality levels by region and location. Instantly they could see that some of their prime new installations were showing red triangles, a sign that data quality was not being adhered to by one of their external contractors.

Context comes from enrichment, so use the data matching capabilities in your data quality tool to pull in useful sources of information from around the organisation or external sources. Get this combined intelligence into a simple dashboard that your leadership can use, and let them make the decisions on what most matters to them.

When you do this, you’ll find the barriers to buy-in will start to crumble away.