Case Study: How Mindbody Achieves End-to-End Data Trust with Monte Carlo

Challenge

Mindbody is a company with data at heart. Alex Soria, VP of Data & Analytics, is leading the charge with a team of over 25 data scientists, business intelligence analysts, and data engineers responsible for ensuring that the insights powering their product is fresh and reliable. Before Monte Carlo, Mindbody had no way of identifying data irregularities until it was too late.  

Solution

Implemented Monte Carlo to monitor and alert for abnormalities in the lifecycle such as null values and duplicate data by connecting to Redshift  and Tableau.  

Why Monte Carlo?

• No-code onboarding reduces management overhead
• Automated monitoring and alerting of data incidents lowers costs 
• Troubleshooting and playbooks reduce time wasted on data fire drills 
• End-to-end coverage brings full visibility to their data pipelines 

Results

With Monte Carlo’s data reliability platform, Mindbody can prevent broken data in their analytics pipelines and dramatically increase data trust – setting the tone for excellency in data-driven online marketplaces.

With Monte Carlo, the Mindbody data team is able to:

• Monitor 15+ high-priority tables out of 3000+ that are automated 
• Detect and alert for anomalies with schema, freshness and volume
• Ensure that data shared with customers is guaranteed accurate

“We are working with Monte Carlo to monitor and alert for abnormalities in the data life cycle, such as null values and duplicate data, that would otherwise go unnoticed until it’s too late. By partnering with Monte Carlo, my team can achieve full Data Reliability across our pipelines,” Alex said. “Monte Carlo’s field-level lineage for Tableau is out of this world.”

Interested in learning more about data observability? Reach out to the Monte Carlo team!

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