The challenge of effectively getting your organization’s data quality program started is often complicated by unmanaged scope creep overwhelming your initial efforts.  You should start small by carefully selecting an initial project that is all but guaranteed to be successful.

In her recent post The Importance of Scope In Data Quality Efforts, Jill Dyché illustrated five levels of data quality delivery that can help you quickly establish the boundaries of your initial data quality project, which will enable you to implement an incremental approach for your data quality program.

“When it comes to data quality,” as Jill explained, “I fervently believe that it is destined for widespread adoption . . . just like taco trucks . . . data quality’s best days are actually ahead of it.”

With the weird way my mind works, the key phrase for me was “just like taco trucks.” 

Tacos and Ingredient Creep

Just like any high quality taco (like the ones you get from those high-end taco trucks), any initial data quality project needs to carefully establish its boundaries.

Stuffing a taco with too much beef, cheese, salsa, random vegetables, then more beef, more cheese, and more salsa, can easily overwhelm even the sturdiest of tortillas by creating uncontainable ingredient creep.

Stuffing an initial data quality project with too many requirements, business processes, data sources,  random technical challenges, then more requirements, more business processes, and more data sources, can easily overwhelm even the sturdiest of teams by creating undeliverable scope creep.

Although true return on investment (ROI) comes from tangible business impacts, such as mitigated risks, reduced costs, or increased revenue,  your initial data quality project needs to deliver ROI without too much time, effort, and cost. 

Don’t get thwarted by scope creep based on the fear that your “ROI isn’t big enough.”

When scope creep happens, then your initial data quality project, which was supposed to deliver a quick win and become the basis for an incremental, ongoing program, can instead cause the crippling effects of data quality rigor mortis.

Data Quality Rigor Mortis

Rigor can be defined as “something hard to endure” and this definition can certainly apply to data quality, where perhaps the hardest aspect to endure is managing the scope of the initial project—or the current iteration of an ongoing program.

In forensic pathology, rigor mortis, which prevents the body from completing the cycle of normal muscle contractions, thereby causing stiffness, can be used to approximate the time of death, but not its cause. 

During a post mortem performed on a failed data quality project, rigor mortis can be used to approximate both the time and most common cause of failure—unmanaged scope creep, which prevents the project from completing the iterative delivery cycles needed for success, thereby causing the “never-ending effect” that leads to failure.

Conclusion

As Jill concludes her post, “business executives and users can consume a well-scoped problem, especially if it makes their jobs easy or propels progress.  And if we solve it in a way that benefits the business—eliminating risk, ensuring economies of scale, and driving revenues—we might even get budget for a data quality tool!”

Just like taking an incremental approach by eating one properly stuffed taco at a time, incremental data quality improvements via a sustained program will build momentum to larger success over time.

What Say You?

How have you overcome the challenge of scope creep in your data quality efforts?