How Better Collective Uses Data Products to Power Reliable Revenue Reporting

Introduction

Better Collective empowers millions of users to make informed decisions through their global network of sports media sites and affiliate partnerships. As the company scaled its U.S. operations and expanded into programmatic advertising and media subscriptions, the data platform team needed to build the foundation for trustworthy revenue data.

One of the unique challenges is that affiliate revenue depends on thousands of tracking links deployed across various domains, each linked to specific partners or campaigns.

“Data is a massive part of knowing how we’re doing as a company,” says Frederik, Head of Data Platform. “From C-level to operations, everyone depends on it.”

However, integrating with over 100 third-party data sources, many of which are scraped or API-limited, could introduce inconsistencies.

“New partners are added all the time, and we rely on scraping for a large portion of affiliate data. That means we needed a way to detect broken or missing data automatically.” 

The challenge: Messy third-party data came in countless formats and structures, making it difficult to detect issues using standard dbt tests alone. On top of that, there was no scalable way to define ownership or monitor the health of hundreds of data models, making it challenging to know what was broken, who was responsible, or how to fix it.

Simultaneously, the addition of new programmatic vendors strained existing data models. With no single source of truth and inconsistent naming conventions across systems, Better Collective saw a need for a more comprehensive solution to streamline data reporting.

The solution: A single pane of glass for data health

One of the data team’s most important responsibilities is delivering timely, accurate revenue metrics to support business decision-making. To achieve this, they built a commercial_revenue dbt model that consolidates affiliate, programmatic, and subscription revenue into a single view, spanning over 400 sources and models.

To manage this complexity, the team turned to Data Products in SYNQ, aligning their monitoring strategy to match how the business operates. They defined clear boundaries around dependencies across Ad Inventory, Sponsorships, and Affiliate Metrics. This gives the team a single pane of glass view into their 10 most critical data products and the health of all related assets across the data ecosystem. It’s a dashboard they can open each morning to instantly see if any issues need stakeholder attention. This is a major shift from combing through disconnected dbt tests to clearly visualizing data health and tests.

“With SYNQ’s Data Products, we can visualize dependencies as the business understands them, assign ownership, and apply the right level of monitoring. It’s a game changer.”

The team at Better Collective has grouped their data products into domains such as Affiliate, Paid Media, and Finance. This structure makes it easier to navigate their data assets, as users only see the assets relevant to their assigned domain. When they log in, they're presented with a simplified view tailored to the data that matters most to them.

A monitoring strategy built around Data Products

Better Collective deployed nearly 100 anomaly monitors across their most important tables, automatically mapped to their associated Data Products.

“We use SYNQ’s deployment rules to define our monitoring strategy. Our key data products automatically get the right anomaly monitors,” says Frederik.

These monitors run hourly and proactively catch issues such as data freshness or volume drops. For example, a volume drop in the r_trackers table was detected and fixed the same day, preventing broken downstream reporting.

Each Data Product has a clear owner, and issues surface in business-friendly language, complete with relevant context, making it easy for both technical and non-technical users to take action.

Better Collective had already invested heavily in dbt, but saw an opportunity to improve visibility into where things were and why, and provide contextual signals for the analysts.

“SYNQ doesn’t just show us the health of our data—it helps us better understand failures and route them to the right domain using our Data Product definitions.”

The team also made use of some of SYNQ’s more advanced monitors, like the custom segmented monitor, to track data freshness at the partner level, making it easy to spot missing data in specific segments, even when the issue was hidden in the overall trend. “It would’ve taken significant time to build this in-house without an observability tool like SYNQ,” says Frederik.

Impact: 40 hours saved weekly, fewer fire drills, and happier engineers

Before SYNQ, the data team spent up to 40 hours a week resolving scraping issues, fixing broken trackers, and filling in missing fields. Now, anomaly monitors catch critical issues before they cause business disruption.

The U.S. migration also introduced new team members. With domain-based alerting and contextual issue descriptions, new stewards were able to troubleshoot without deep platform knowledge. It’s also created a cultural shift with happier engineers across the board.

“The business used to catch issues before we did. That’s no longer the case. We now detect and fix problems proactively.”

Next up: Scaling with a reliable data foundation

With a more solid foundation, Better Collective is now expanding their central platform and distributing ownership to more technical analysts.

“With greater clarity around ownership and scalable monitoring, Better Collective is now expanding their central data platform. SYNQ helps us communicate our value and drive accountability.”

By investing in observability that speaks the language of the business, Better Collective is doing more than improving data quality. They’re saving time, reducing incidents, and building trust in data at the speed of company growth.

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