From the course: Automating Data Quality in Dev Environments

Choose a high-priority project

From the course: Automating Data Quality in Dev Environments

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Choose a high-priority project

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- [Presenter] One of the biggest mistakes I see data leaders make is not connecting their work back to business value. With Gen AI on everyone's mind, the rush for data teams to prove their worth is stronger than ever. So if you want to see your data QA put into practice, your best bet is applying that QA to a high priority project that your C-suite cares about. If you're not sure where to start, ask yourself, what's the biggest problem that our business faces? If something threatens our business long-term, what is it? Maybe you can't hire the talent needed to build competitive AI products. Maybe your churn rate has been higher than normal these past few months, but you can't figure out why. The big data space moves extremely fast, and it's easy for the latest tool, framework, or technique to distract you, but your job is to get the right data to the right audiences to solve the right problems. If you're fixated on buying Amazon SageMaker but can't explain how it will help your users and improve your data team's workflow, you've lost the plot. To avoid this, choose a proof of concept for a data product to help solve your most pressing business problem. If that problem is the churn rate going up, then define a data product you can use to shed more light on why that churn rate keeps increasing. To get started, answer these questions. Which data do we need to learn why the churn rate is increasing? Which systems does that data live in today? How often is that data shared with the stakeholders who need it? And who's in charge of managing that data? You'll get the answers to these questions throughout our next few lessons. They'll chart your path to build a high-quality data product that offers instant value and shows you that you can use data for collective good.

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