Prioritising data quality projects

It’s likely going to be difficult to get a project prioritised if the goal is defined only as “improving data quality”. So what? What’s the business value that will provide or enable? And does that align with the organisations wider goals?

Will improving the quality speed up the delivery of your data science team so they can deploy revenue-generating ML features sooner? Will it improve the reliability of a key business process? Will it reduce costs?

Similar to my note yesterday about knowing your audience, you need to argue the case for improving data quality by explaining how it benefits the person you are speaking to. So choose the outcome they are most likely to be excited about and frame your argument around that.