Data Quality Project Selection – A Missing Skill?

In this post we present the different methods of selecting data quality projects.

The aim of this post is to open up a debate on whether project selection is a key skill currently lacking from the data quality profession.

There are essentially two ways to select a data quality project: bottom-up or top-down.

Historically, I’m guessing the bulk of most data quality projects have been bottom-up. The ideas and inspiration for launching a data quality initiative typically come from knowledge workers and managers who are failing to deliver operational processes or new projects etc.

In contrast, top-down project selection focuses on launching projects that are aligned to strategic priorities.

There are benefits and drawbacks of each approach.

Taking a top-down approach obviously helps you break out of the silo mentality and identify strategic opportunities that may generate more “bang-for-your-buck” when compared to more tactical, locally delivered projects.

The drawback of a top-down initiative is typically the heavy lifting required. Executive sponsorship, larger teams, data governance, greater complexity and ultimately, increased risk.

The bottom-up approach to project selection often focuses on eliminating delays and failures in other projects and programmes that are impacted by poor data quality. This can result in quick wins and a great deal of perceived value being generated, albeit at a local level.

The main drawback of a bottom-up approach is that they are often knee-jerk, tactical style projects that are reactionary in nature. Managers who are frustrated by constant delays and failures may justify tactical projects with often flimsy intelligence on the real value delivered.

As we try and move our organisation (and ourselves) up the data quality maturity curve there comes a greater need to shift our focus from bottom-up to top-down project selection. My gut feel is that this is where a lot of initiatives comes off the rails. A lot of the techniques involved in top-down project selection are alien to the average data quality project team. Typical approaches to top-down project selection may require deep financial analysis, observation of customer needs in high-value market segments, analysis of service profit chains etc. Many organisations of course have their own corporate policies for project selection at a strategic level.

Is there a skills gap in our profession here? We’re talking a lot about tactics of delivery, those data quality techniques that get us over the finishing line but are we paying enough attention to those skills that get our projects over the starting line?

I’m not convinced we are but what do you think? Are we doing enough to educate the profession on how to select and justify projects that move up the value chain?

What are your views on this issue? Please share in the comments below.