How The Farmer’s Dog Achieved Rapid ML-Based Anomaly Detection with Monte Carlo
Companies across all industries are striving to become data-driven: making decisions based on data and building a culture of data trust and transparency. But data downtime—periods of time where data is missing, broken or otherwise erroneous—undermines those efforts and can cost companies upwards of $15 million annually.
And very often, the ability to achieve more reliable data is both time-intensive and intensely manual. Not with Monte Carlo, the world’s first SOC 2 Type 2 certified, fully automated data observability platform.
Out-of-the-box and with only 5 minutes of no-code implementation, Monte Carlo delivers full coverage across your critical data assets beyond the orchestration layer, accounting for the unknown unknowns in your data pipelines.
Recently, we spoke with Rick Saporta, Head of Data Strategy and Insights at NYC-based dog food company, The Farmer’s Dog, about his experience using Monte Carlo to achieve end-to-end data reliability across his BigQuery and Looker data stack.
As one of Monte Carlo’s earliest customers, The Farmer’s Dog experienced first-hand the immediate impact of fully automated data observability.
“Within minutes of deploying Monte Carlo, my team was up and running, and we had full visibility into our data pipelines, from ingestion in BigQuery to analytics in our Looker dashboards,” says Rick.
During this video, you’ll also learn how Data Observability through Monte Carlo makes it easy to:
?Shorten time-to-detection (TTD) and time-to-resolution (TTR) when data issues arise
? Conduct root cause analysis on your data pipelines
? Make changes to your data without causing downstream breakages
And much more!