In some of my most recent expert Q&A sessions on Data Quality Pro, the benefit of “Making the Invisible Visible” has surfaced. What these interviewees were referring to was the effect that takes place when defective data, invisible to the naked eye, is finally brought onto the centre stage and laid bare for all to see.

Although these insights were from two totally different viewpoints, one from a data analytics perspective, the other from a data governance angle, both had the same result – jolting people into action.

One of the most exciting aspects of data quality consultancy for me personally has always been rapidly finding defects, linking them to some form of impact, and then presenting the findings back to the business to help spark some kind of discussion and, in many cases, a heated argument around the meeting room!

It is always fascinating to watch the body language of the business stakeholders, IT owners and all those who have some responsibility for the data. It can tell you so much about where the real issues lie. As I mentioned, I’ve witnessed numerous arguments erupt that really lay bare the lack of governance and process that surrounds the data. Political turf battles start to be played out, all because a data quality assessment paints a less than rosy picture of corporate data health.

These are important conversations and they need to be had; it’s tempting to jump in with solutions, but it’s equally important for organisations to reach their own conclusion. Don’t expect miracles in these sessions–it’s rare to agree a set of outcomes in the first feedback session. You often need a few days or even weeks for the dust to settle and decisions to be made.

Here are some tips I’ve found useful for helping to make the “invisible visible” and showcasing your data quality assessment findings to data stakeholders:

• Have full sets of data at the ready for any queries that arise (nothing worse than telling part of the story)
• Use both a data quality and a visual analytics tool – one for showing the business view (analytics), the other for linking to the data quality facts
• Try and publish the findings in a format that enables the attendees to take the data away (they will want to scrutinise your data, and a few headings in a Word document won’t suffice)
• Record a video of your desktop as you’re walking through your presentation and share it with the attendees (use Camtasia, Jing, Captivate or Screenflow on the Mac, etc.)
• Tell a story; don’t just present raw figures–identify the people who may be affected by the issue, take pictures of them, make them real, clarify the pain they’re experiencing, but don’t get into the payoff of how you can resolve the issues just yet–let that aspect play out following a discussion
• Be clear on your call to action, don’t try and close too early

The last point is the most important. You need to create a mental data quality sales process and be sure of what you’re looking to get out of the results. Sometimes awareness is an acceptable goal. Trying to leap from awareness to “Can I have some budget please?” will kill off any future discussions.

Finally, prepare to play the long game. You may not have the patience to wait two years, but it certainly pays to map out the sequence of events you want your stakeholders to follow and the tasks you need to undertake to get them there.

What about you? How are you making your invisible data quality more visible? What techniques work for you?