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Data Observability: The Next Frontier of Data Engineering

Posted Mar 28
# Explainability and Observability
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SPEAKER
Barr Moses
Barr Moses
Barr Moses
CEO & Co-Founder @ Monte Carlo

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 built the data/analytics team. Prior to that, she 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.

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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 built the data/analytics team. Prior to that, she 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.

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SUMMARY

As companies become increasingly data driven, the technologies underlying these rich insights have grown more nuanced and complex. While our ability to collect, store, aggregate, and visualize this data has largely kept up with the needs of modern data and ML teams, the mechanics behind data quality and integrity has lagged. To keep pace with data’s clock speed of innovation, data engineers need to invest not only in the latest modeling and analytics tools, but also ML-based technologies that can increase data accuracy and prevent broken pipelines. The solution? Data observability, the next frontier of data engineering. I'll discuss why data observability matters to building a better data quality strategy and tactics best-in-class organizations use to address it -- including org structure, culture, and technology.

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