Conclusion: Embracing a quality-driven data culture

After being introduced to data quality, you saw how critical the quality of data is when building effective AI models, and how you have to make it part of your overall data strategy if your organization is going to succeed in using the latest advancements in AI to drive business value.

But before you can improve your data quality, you need to measure it. You can start doing that today by simply asking your data consumers, “Do you trust your data?” By doing this regularly through surveys, you’ll gain a valuable data point that you can use to measure the quality of the data.

This will inform you about how people feel about your data quality, but you can do more. The next step is to gain greater visibility of exactly how big a data quality problem you have, and where you have it. You can do this cheaply by running data quality checks and performing one-off profiling of your data, or you can invest in a data...