The Origins, Purpose, and Practice of Data Observability

Abstract: 

Data Observability (DO) is an emerging category that proposes to help organizations identify, resolve, and prevent data quality issues by continuously monitoring the state of their data over time. This talk is a deep dive into DO, starting from its origins (why it matters), defining the scope and components of DO (what it is), and finally closing with actionable advice for putting observability into practice (how to do it).

Bio: 

Kevin Hu is co-founder and CEO of Metaplane, a data observability company based in Boston focused on helping every team find and fix data quality problems with as little setup as possible. Metaplane is backed by leading investors including Y Combinator and the founders of Okta, HubSpot, and Lookout, and is used across high-growth teams and large enterprises.

Kevin has over a decade of experience working in data. Most recently, he researched the intersection of machine learning and data science at MIT, where he collaborated with Fortune 500 companies while earning his PhD, SM, and SB. His research has been published in top computer science venues like ACM CHI, KDD, and SIGMOD, and featured in the New York Times, Wired, and The Economist.

Open Data Science

 

 

 

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