Monte Carlo: Using Data Observability to Improve Data Reliability
Hashmap On Tap, Ep. 76
“The notion of making sure that you are ingesting clean data no longer exists because even if you ingest data that is accurate it can break anywhere along the pipeline and cause issues upstream. To truly address data trust we need to view it end-to-end.”
— Barr Moses CEO & Co-Founder at Monte Carlo
Today it is no longer enough to simply collect and store data. Everyone realizes that data is highly valuable. The challenge is figuring out how to harness data to make decisions that drive positive outcomes.
Ensuring proper data reliability and data quality is important across every single industry and it’s not going away any time soon. However, data observability is helping to address some of these challenges.
On one of the latest episodes of Hashmap on Tap, Barr Moses shares a chai tea with Kelly Kohlleffel and discusses how Monte Carlo is changing the landscape of data observability.
She talks about how they are leveraging machine learning to evaluate and identify data quality and discoverability issues by using automated monitoring, alerting, and triaging to create healthier data pipelines and more productive teams. She also shares her perspective on Monte Carlo’s origins, their recent $25M Series B round, and their overall growth and future roadmap.
Hear Barr’s Perspectives on:
- The origins of Monte Carlo
- The importance of accurate & reliable data
- The 5 pillars of data observability
- The challenge of creating a new category in the data space
- Her path from Israel to the US and founding a tech startup
Listen to the Episode:
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