Monte Carlo, a data reliability company, has announced Insights, 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, Insights leverages machine learning for monitoring and ranks events and assets based on their usage, relevance, and relationship to other tables and assets. With Insights, customers can measure and optimize the reliability, performance, cost, and effectiveness of their data initiatives.

On average, companies lose over $US15 million per year on bad data, with data engineers spending upwards of 40 percent of their time tackling broken data pipelines.

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 wasting cycles trying to figure out what data sets they should be prioritizing and end up missing tables when setting up coverage.

With Insights, data teams 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.

With the release of Insights, Monte Carlo now helps companies understand:

• Which tables are most widely used
• Which tables are most frequently used
• Who uses which tables
• Which tables can be deprecated to reduce storage and processing costs
• Data incident trend analysis
• Data service-level agreement (SLA) dashboards
• Which tables drive key assets for the business, such as financial reports or executive dashboards

After teams have developed processes to address data quality incidents with Monte Carlo, they often ask for help quantifying the impact of those efforts on the organization.

Requests range from wanting to define SLAs and track performance to tracking which teams are following data quality best practices, and determining which data assets merit investment.

Insights addresses this need by providing end-to-end visibility into operational analytics across the data stack. 

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