Without data quality, your data initiatives will fail.

Chad Sanderson is passionate about data quality, and fixing the muddy relationship
between data producers and consumers. He is a former Head of Data at Convoy,
a LinkedIn writer, and a published author. He lives in Seattle, Washington,
and is the Chief Operator of the Data Quality Camp.

Without data quality, your data initiatives will fail. Despite that, data teams still struggle to gain buy-in on quality initiatives from executive teams. Here's why:

1. Too much too soon: Data teams often want to solve data problems holistically, for everyone. These types of plans are typically incredibly costly for the business and take away from top-level initiatives.

2. All process, no technology: Process doesn't scale, and every executive knows that. Making the entire organization subject to a massive velocity tax for better data isn't usually worth the opportunity cost.

3. BI & Analytics are low ROI: It's hard to prove the tangible value of BI & Analytics, therefore data quality that makes reporting better isn't clearly measurable. When the investment is so high, business teams want a directly measurable economic impact.

4. Maintenance cost: Self-explanatory, but the cost to continue supporting such a large initiative over time can often be even more expensive than the initial investment.

5. Takes too long: Doing a massive data warehouse refactor that will take two years? Unfortunately, that's no longer acceptable at most companies. You need results faster.

So how do you overcome this?

First, focus on applying data quality on particular pipelines that generate direct ROI for the business: AI/ML models, embedded data products, accounting pipelines, etc. One use case is enough to show value.

Second, come up with a plan that eliminates bottlenecks and allows for scalability of quality to all other value-generating use cases. The team should be able to maintain high quality at little additional cost moving forward. Decentralization is key.

Third, execute quickly. Find a team that has data quality as a top priority and work with them to execute and implement a solution within a quarter. Have a plan to demonstrate how improvements in DQ impacted the bottom line.

Fourth, put together a compelling one-page pitch that highlights the above: The use case, its impact, how you'll scale it, the team you are supporting, and the timeline.

Do all the above, and you will find it much easier to launch and support data quality initiatives.