Most organisations kick off their data quality journey with some form of localised initiative. Perhaps a data migration needs a data quality cleanup, or a customer-facing service is plagued with legacy dirty data. A time-boxed initiative is delivered and traction develops.

More projects ensue – and slowly, ever so slowly, cultural change and enterprisewide initiatives start to take root.

What are some of the key lessons learned for making this leap from local heroics to enterprise stability?

I recently had a look back through the many interviews I’ve done with data quality leaders in banks, telcos, utilities, healthcare and retail to see if there are any pearls of wisdom to share.

Here are some common themes:

1. Master the art of persuasion and negotiation

There are multiple skills that are required to initiate or launch such projects at the enterprise level. The most important ones are influencing and negotiation. If you can't master these, you'll struggle to take your initiative beyond individual projects or silos. Without question, all the enterprise success stories I’ve covered required effective salesmanship at their core.

2. Capture all your data quality use cases before choosing data quality software

A common theme was at the enterprise scale you need a solid data quality platform to help with the heavy lifting and sheer immensity of the challenge. Several interviewees expressed regret at having made poor choices initially by not envisioning all the scenarios they would need from tools down the line.

Message: Do plenty of data quality tool due diligence against different data quality scenarios in a multitude of problem domains.

3. Don’t let your enterprise team slow down enterprise momentum

A lot of companies will focus on developing a single centre of excellence. This may suffice for small projects but as your enterprise aspirations grow you need to think about how that team can share what they’ve learned and develop reusable technologies and tactics that can help other units take up the baton and run with data quality.

In short, you have to create a framework for expansion and this is something that a lot of leaders I’ve interviewed regret not doing sooner.

4. Get obsessed with re-usability

One interviewee found that his organisation had more than 50 applications that all did the same basic checks for contact data quality. Consider the maintenance overhead for this single function alone and you’ll soon realise why a top goal for many of the leaders I’ve interviewed was to foster much greater re-use of data quality functions and technology.

Certainly standardising on a single data quality platform makes sense in this regard, but the message was don't forget to standardise on the methodologies and framework content to – not to mention data quality rules and standard definitions.

5. Buid a physical and cultural focal point for your movement

I was invited to speak at a financial instiution recently, and as I was making my way through endless lines of desks and cubicles I was informed that "here is the data quality team." Sadly I couldn’t see any discernible difference from the several thousand other workstations and team zones I had passed along the way!

One thing that was repeatedly hammered home to me over these last years of running my leadership interview series is the need to build an identity and brand for your movement.

Some of the leaders create "war rooms" and "drop-in zones" where managers and frontline staff can see the work and success the team members are delivering. Others create entire websites and online resources with community portals to enable cultural exchange within the company on a more global level. Don't forget to allow external access too. Mindshare is critical.

Data quality success at an enterprise level demands cultural reform, so try and build some kind of community presence, physical or virtual, so you can start to attract some followers outside your team (or organisation). Don’t just become another faceless project. Develop a brand and personality for your initiative that speaks of your values and grand ambitions.

What pointers for enterprise data quality success have influenced your projects? Please share in the comments below.