How Vox Media Scales Data Reliability with Monte Carlo

As a leading independent modern media company, Vox is on a mission to build a better future for the media industry. And with a portfolio that includes its own namesake, as well as New York Magazine, The Verge, Eater, Vulture, Thrillist, popular podcasts, TV and streaming partners and beyond, Vox certainly has the firepower to do it. 

Over the years, Vox has made a name for itself building not only some of the most iconic media brands but also the technology that powers them. And part of the technologic infrastructure behind Vox Media’s success certainly includes its owned data sources—and the platform that powers them.

But an endless stream of actionable data is only valuable if you can trust it. And fortunately, Vox can. In this video, Senior Product Manager Vanna Triue shares how Vox Media uses Monte Carlo to deliver data reliability at scale. 

The data landscape at Vox Media

You don’t become one of the most iconic media portfolios on the planet without leveraging a fair bit of data. And Vox Media is as data-driven an organization as one’s likely to find.

“We use data to fuel all the insights that we generate, whether that’s internally or externally.” Vanna said. “We particularly use data to understand our audiences and think about how they’re engaging with all the platforms and all the properties that we have across the Vox Media portfolio.”

With data driving how Vox Media understands and supports its audience, data reliability is critical. And data observability through Monte Carlo enables Vox Media to seamlessly facilitate and monitor that reliability at scale. 

How Vox Media uses Monte Carlo

“So our team uses Monte Carlo to understand the health of our data sets. For us, it’s not good enough to just understand when a system breaks. We also have to understand how that impacts our data.” 

Leveraging tools like automated monitors and alerting, Vox Media has empowered their data team to discover breaks and anomalies in real time. And with field-level lineage providing end-to-end visibility, Vanna and team are able to easily understand the downstream effects of those breaks at a glance. 

“Lineage is by far my most favorite feature of Monte Carlo,” Vanna said. “If something does break for whatever reason, you can see how it impacts things across the life cycle review. So, whether it’s closer to the warehouse or all the way through to your dbt models or your analytics systems, you can see how breakages impact all the parts of your data stack.”

Vanna said he also utilizes Monte Carlo’s custom monitors to create bespoke monitors for specific use cases as well. 

With its own cosmos of user data at hand and data observability always on watch, building a better future for the media industry might not be far off for the talented team at Vox Media.

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