"I can't do data QA because my team hasn't defined what data quality looks like" – if this sentence sounds familiar, you're not alone.

High-quality data is AI's secret sauce, yet defining what that means can seem impossible. Who defines data quality, how does it differ for each data domain, when should you update those standards post-production, and what moves your data governance from a 60-page Word document updated last decade to a clear set of standards that serve as your data architecture's backbone?

This talk covers the basics of great data governance: The people, processes, and tools needed to automate data quality at scale. You'll learn how to define data domains, organize your data architecture, create data QA, and build more transparency into your algorithms.

Speaker: Lauren Maffeo

Prior to her civil service career, Lauren was a senior service designer at Steampunk, where she designed end-to-end experiences to make federal government systems more accessible. She most recently supported the U.S. Coast Guard's first Chief Data and Artificial Intelligence Office (CDAO), where she led design activities to align data mesh, governance, AI, and analytics with the Coast Guard's key priorities. Lauren was also an associate principal analyst at Gartner, where she covered the impact of emerging tech like AI and blockchain.

Lauren is a founding editor of Springer’s AI and Ethics journal and a former area editor for Data and Policy, an open-access journal with Cambridge University Press. She has presented at venues/with partners including Princeton and Columbia Universities, the U.S. State Department, and Twitter’s San Francisco headquarters.

Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian, among other publications. She is a member of the International Academy of Digital Arts and Sciences, where she helps judge the Webby Awards. She also serves on 2 nonprofit boards and volunteers with The Linux Foundation.