Monte Carlo

Monte Carlo gives data teams confidence that their data is reliable and trustworthy through fully automated, end-to-end Data Observability.

MonitoringData Governance

What need does Monte Carlo fulfill?

Monte Carlo continuously monitors your critical data assets and proactively alerts stakeholders to data quality issues. Data teams can get end-to-end visibility into their data pipelines via a 20-minute, no-code implementation process and achieve out-of-the-box coverage for data Freshness, Volume, Distribution, Schema, and Lineage. 

Monte Carlo’s machine learning-first approach gives data teams broad coverage for common data issues with minimal configuration. Business context-specific checks and root cause analysis ensure that each stage of the data pipeline is taken care of.

Monte Carlo’s end-to-end lineage for your data pipelines monitors critical data assets from the point it enters your warehouse/lake (or further upstream!) down to the analytics and business intelligence layer. Data teams are able to assess the business impact of data quality issues and are able to prevent decision-making based on bad data.

What are the benefits of using Monte Carlo?

  Know when data breaks, as soon as it happens
  Find the root cause of data issues, fast
  Immediately understand the business impact of bad data
  Proactively prevent broken pipelines

What are the core features of Monte Carlo?

  No-code implementation and onboarding
  End-to-end lineage from the point of ingestion to end-user analytics
  SOC-2 Type I and Type II certified

Which teams does Monte Carlo cater to?

Data Engineering
Analytics
Data

Authored By

Molly Vorwerck's profile on Data-led Academy

Molly Vorwerck

Head of Content & Community