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Monte Carlo Data Snapshot
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We’re on a mission to accelerate the world’s adoption of data by reducing data downtime.
A brief history of Monte Carlo:
Barr Moses, founder of Monte Carlo, worked with Fortune 500 companies as VP of Customer Operations at Gainsight to help teams use data as a competitive advantage. Lior Gavish, Barr’s co-founder, was formerly SVP of Engineering at Barracuda where he built fraud detection systems powered by data, ML, and analytics.
They were both struck by the fact that great tools were available to identify and resolve infrastructure issues, but there was no easy way to guarantee the validity of data flowing through pipelines.
In software engineering, every team has a solution like New Relic, DataDog, or PagerDuty to measure the health of applications and ensure reliability. How come data teams are flying blind?
Together, Barr and Lior realized there was an opportunity to help data teams actually trust their data.
At Monte Carlo — named after the simulation, not the destination — we’re committed to making data reliable. We’re committed to making the lives of our customers easier. And we’re committed to working with the broader data community to realize the full potential of data.
A brief history of Monte Carlo:
Barr Moses, founder of Monte Carlo, worked with Fortune 500 companies as VP of Customer Operations at Gainsight to help teams use data as a competitive advantage. Lior Gavish, Barr’s co-founder, was formerly SVP of Engineering at Barracuda where he built fraud detection systems powered by data, ML, and analytics.
They were both struck by the fact that great tools were available to identify and resolve infrastructure issues, but there was no easy way to guarantee the validity of data flowing through pipelines.
In software engineering, every team has a solution like New Relic, DataDog, or PagerDuty to measure the health of applications and ensure reliability. How come data teams are flying blind?
Together, Barr and Lior realized there was an opportunity to help data teams actually trust their data.
At Monte Carlo — named after the simulation, not the destination — we’re committed to making data reliable. We’re committed to making the lives of our customers easier. And we’re committed to working with the broader data community to realize the full potential of data.
