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Discover why today’s leading data teams rely on Monte Carlo for data observability.
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How Roche built trust in the data mesh with data observability
CreditKarma's journey to reliable generative AI models with data observability
How Sub-Zero Group put data quality issues on ice
How JetBlue used data observability to help improve internal "Data NPS" by 16 points year over year
Data teams across industries use Monte Carlo to build data trust.
“Monte Carlo gives upstream and downstream lineage for a 40,000-foot view of the entire data ecosystem — and this helps stakeholders across the company truly run.”
Mike Weiss
Nasdaq
“Monte Carlo helps you pinpoint any bottlenecks in your pipeline and streamline the effort to reduce technical debt where it’s needed most. Monte Carlo helps you maintain a high performance of your pipeline, quickly address emerging issues, and reduce cost consumption.”
Roberto Munger
Roche
“In the past, we might have to backfill six months of data to when the issue started. But if we can detect an issue in a day, then a one-day backfill can be a lot cheaper. There are lots of benefits, like engineering time, that we’re seeing from data observability.”
JM Laplante
Skyscanner
“Data trust is a human emotion, which isn’t always entirely rational. But, if you can give the analysts access to Monte Carlo where they can see the health of all the data objects they are using, you can sell them on the data’s quality.”
Brian Pederson
JetBlue
“We have 10% of the incidents we had a year ago.”
Daniel Rimon
Resident
“Data observability has become a necessity, not a luxury, for us. As the business has become more and more data-driven, nothing is worse than allowing leadership to make a decision based upon data that you don’t have trust in. That has tremendous costs and repercussions.”
Alex Tverdohleb
Fox