Broken data is costly, time-consuming, and nowadays, an all-too-common reality for even the most advanced data teams.

In this talk, I’ll introduce this problem, called “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate — and discuss how to eliminate it in your data ecosystem with end-to-end data observability.

Drawing corollaries to application observability in software engineering, data observability is a critical component of the modern DataOps workflow and the key to ensuring data trust at scale.

I’ll share a quick start approach to achieving data observability at scale and highlight the important role of graph analytics and metadata in driving this process.

About This Speaker

Barr Moses is CEO & Co-Founder of Monte Carlo, a data reliability company backed by Accel, GGV, Redpoint, and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team.

Before Gainsight, Barr was a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford University. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science.