What are the most useful skills a data quality leader can possess?
As an editor of an online data quality magazine, I naturally get asked this type of question regularly at events and meetups. My answer may surprise some who are expecting a data-centric response. I firmly believe that sales and marketing are the most useful (and overlooked) skills of a data quality leader.
The reason I give this answer is that many data quality teams struggle to build on their success because they fail to educate and persuade the business about the benefits they bring to the organisation. All your efforts can be wasted in an instant if a sudden management change at the top fails to appreciate the value you bring to the company.
For a lot of people (myself included), marketing started out as an alien topic. I used to feel that if my work was valuable, then surely word would get out. Then the demand for what our data quality management team can achieve would spread.
Sadly, this "organic growth" of data quality often needs a boost in the form of a concerted sales and marketing drive. The trick is tailoring your message to the different segments of your audience, such as:
- Executives and senior managers.
- Line managers.
- Customer-facing workers and other knowledge workers.
Amongst this audience, the most important for the continued growth and development of your data quality aspirations is clearly the executive and senior management layer. And the key to engaging this audience is awareness.
In the past, I've developed this awareness by creating an analytics “wrapper” around the data quality work the team has delivered. By doing this, you can start to trace the gains from the improved data to the improved business outcome. You can create simple custom dashboards and reports that provide analytics to show the gains your data quality work has delivered.
And that's how the "yin and yang of data quality and analytics" idea was born.
For one client, we spent several weeks improving the quality of data feeding into a utility data warehouse. The net result was far better quality analytics, reduced shipping lead times and improved relationships with third-party suppliers. But rather than just move on to the next project when we finished this one, we asked the same analytics team to provide a few simple dashboards to show the benefits of our work. That provided a way to market the gains and then cross-sell the benefits to other stakeholders and sponsors.
The moral here is that it's not enough to just do a great job with improving data quality. The most successful leaders I know have a solid strategy for the sales and marketing of data quality that depends heavily on analytics. And that's the very same capability their data quality was responsible for improving in the first place.
Read – SAS is a Leader in the 2016 Gartner Magic Quadrant for Data Quality Tools
