Garbage in, garbage out? Measuring error rates and data quality to get ready for AI

Theme: AI and Beyond | Data

There’s a big push to implement AI everywhere, hoping for better productivity and faster decisions. To get the best out of any AI, it helps to start with good quality data.

So what are we doing to measure our error rates and data quality?

In this workshop, we will compare our thoughts on error rates, including trying a new 5-aspect framework for errors to see whether we think it is helpful.

Data quality isn’t static. We’ll consider how data might deteriorate over time or in other ways, and share our ideas about how we are measuring that, too.

We’ll wrap up with “tips and next steps”: an opportunity to consider what we now need to find out or do differently.

This is a workshop where participants will spend most of their time sharing their experiences and learning from each other. The aim is to create definitions as a group and to shape ideas for measuring and improving data quality in our services.

Participant takeaways:

• What do we think good data quality is? How are we currently defining it?
• How are we measuring error rates and data quality at the moment?
• How much do we know about the consequences of errors in our data?
• What ways of measuring error rates and data quality are practical and where can we make improvements?

This session:

• Includes interaction
• Has a number cap: 50 participants