In this episode of Digital After Dark, Matt and Andrew dive deep into data layer quality, JSON schema validation, and automated monitoring at scale. Using real-world examples from MeasureCamp and client implementations, they explore how teams can move from messy, inconsistent analytics data to a reliable, validated, and scalable data ecosystem.

Andrew focuses on how JSON schemas bring structure and confidence to data layers, empowering developers, QA, and analysts to catch issues early. Matt then builds on that foundation by showing how to operationalize schema validation at scale using tools like ObservePoint, automation, and APIs—ensuring data quality doesn’t break when changes ripple across large sites or multiple domains.

The conversation blends technical depth with practical workflows, developer empathy, and a healthy dose of humor (including an unforgettable “number two before number one” moment).

Key Takeaways

• Your data layer is the schema — the events are temporary, but the schema defines long-term data quality.
• Validate early, not after launch — catching issues in dev saves exponential time later.
• JSON Schema turns analytics specs into enforceable contracts, not just documentation.
• Data quality deserves the same rigor as UX, even if the consequences appear later.
• Manual testing doesn’t scale — automation and monitoring are essential for modern analytics stacks.
• Schema validation builds confidence across teams, from developers to analysts to stakeholders.
• Start small (MVP) — even basic type validation delivers immediate value.
• At scale, governance beats heroics — automation, APIs, and shared standards win every time.