Data Observability Software Provider Monte Carlo Launches Solution to Deliver Operational Analytics About Data Platforms

Monte Carlo has announced a new capability that helps companies understand which data is most important for the business, and in turn increase data trust. Built on top of the Monte Carlo Data Observability Platform, Monte Carlo Insights leverages machine learning for monitoring and ranks events and assets based on their usage, relevance, and relationship to other tables and assets.

According to Monte Carlo, on average, companies lose more than $15 million per year on bad data, with data engineers spending upward of 40%—or 120 hours per week?of their time tackling broken data pipelines. And all too frequently, data teams have trouble understanding what their most critical data is, preventing them from focusing on data that actually matters when it comes to ensuring quality and reliability. As a result, teams are either wasting cycles trying to figure out what datasets they should be prioritizing and end up missing tables when setting up coverage.

“Monte Carlo’s mission is to accelerate the adoption of data by eliminating data downtime—in other words, giving data teams the tools necessary to trust their data,” said Lior Gavish, CTO and co-founder, Monte Carlo. The new capability puts the metadata Monte Carlo generates in the hands of data engineers to help them answer the most important questions around how their efforts ultimately lead to higher quality data, he added.

The new Monte Carlo capability means users can access the synthesized metadata Monte Carlo generates to build dashboards, analyze data platform team performance, and even commit to and track SLAs. The data itself can be downloaded as CSVs via the Monte Carlo CLI or in the app, and, for Snowflake customers, can be accessed directly in their Snowflake environment via secure data sharing. This level of detail, common in software engineering and DevOps tooling, makes it possible for data teams to understand what data matters most to the business based on usage, access, data quality checks, and automatic lineage. Additionally, Insights makes it easy to create and share high-level reporting with CTOs and CDOs, fostering greater data trust and ownership across the company.

“Companies have tried to build this kind of solution in-house, but the efforts are often ad hoc, manual, resource-intensive, and unable to evolve with business needs. Insights is fully automated so it can cover a company’s entire data stack,” said Uri Shahar, head of data at Monte Carlo. “Our machine learning scores data assets based on active users, average daily reads, their relationship to upstream and downstream assets, and their connection to important service-level indicators and data quality checks. Such insights make it easy for teams to set data strategy and prioritize accordingly based on what data matters most to the business.”

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