Case Study: Fox Networks Group achieves data reliability at scale with Monte Carlo

How Fox Digital Architected its Modern Data Stack

Fox Networks Group needed a way to scale self-service analytics across its digital organization without turning the data team into a bottleneck. As the company ingested data from hundreds of sources and processed billions of records each week, it needed trusted, secure, and consistent data for ad-hoc analysis and executive reporting, while keeping pace with fast-moving business decisions. Monte Carlo was used as part of the data stack to provide end-to-end data observability.

To support this model, Fox Networks Group implemented Monte Carlo alongside tools like Datadog and CloudWatch to continuously monitor data quality, alert on issues, and provide automated lineage across its modern lakehouse architecture. This helped the team catch problems before they reached production, understand downstream impact quickly, and maintain trust in timeliness, completeness, and cleanliness of the data. According to the case study, the organization receives data multiple times per day from over 200 sources, processes nearly 10,000 schemas and tens of billions of records weekly, and Monte Carlo gave the team an AI-powered overview of the data stack to manage that scale more proactively.

Fox Networks Group

Alex Tverdohleb

VP of Data Services