Data Quality – Technology’s Prune

Prunes. When most of us think of prunes, we tend to think of a cure for older people suffering from constipation. In reality, prunes are not only sweet but are also highly nutritious. Prunes are a good source of potassium and a good source of dietary fiber. Prunes suffer from a stigma that’s just not there for dried apricots, figs and raisins, which have a similar nutritional benefit and medicinal benefit. Prunes suffer from bad marketing.

I have no doubt that data quality is considered technology’s prune by some. We know that information quality is good for us, having many benefits to the corporation. It also can be quite tasty in its ability to deliver benefit, yet most of our corporations think of it as a cure for business intelligence constipation – something we need to “take” to cure the ills of the corporation. Like the lowly prune, data quality also suffers from bad marketing.

In recent years, prune marketers in the United States have begun marketing their product as "dried plums” in an attempt to get us to change the way we think about them. Commercials show the younger, soccer Mom crowd eating the fruit and being surprised at its delicious flavor. It may take some time for us to change our minds about prunes. I suppose if Lady Gaga or Zac Efron would be spokespersons, prunes might have a better chance.

The biggest problem in making data quality beloved by the business world is that it’s well… hard to explain. When we talk about it, we get crazy with metadata models and profiling metrics. It’s great when we’re communicating among data professionals, but that talk tends to plug-up business users.

In my recent presentations and in recent blog posts, I’ve made it clear that it’s up to us, the data quality champions, to market data quality, not as a BI laxative, but as a real business initiative with real benefits. For example:

• Take a baseline measurement and track ROI, even if you think you don’t have to
• If the project has no ROI, you should not be doing it. Find the ROI by asking the business users of the data what they use it for.
• Aggregate and roll-up our geeky metrics of nulls, accuracy, conformity, etc into metrics that a business user would understand – like according to our evaluation, 86.4% of our customers are fully reachable by mail.
• Create and use the aggregated scores similar to the Dow Jones Industrial Average. Publish them at regular intervals. To raise awareness of the data quality, talk about why it’s up and talk about why it has gone down.
• Have a business-focused elevator pitch ready when someone asks you what you do. “My team is saving the company millions by ensuring that the ERP system accurately reflects inventory levels.”

Of course there's more. There’s more to this in my previous blog posts, yet to come in my future blog posts, and in my book The Data Governance Imperative. Marketing the value of data quality is just something we all need to do more of. Not selling the business importance of data quality... it’s just plum-crazy!