How a large UK non-profit cut data triage time by 30%

A large UK non-profit delivers essential public services. Behind that work sits a data team whose job is simple to state and hard to do: get the right data to the right people at the right time.

Data shapes nearly every decision the organisation makes, from operational readiness and resource allocation to supporter engagement and strategic planning. But the team was spending 25–30 hours a week just tracking down data issues, and trust in its reports was slowly wearing down.

Then the team rolled out data observability with Bigeye. Within weeks it was live on the most critical datasets, and triage time fell by about 30%.

The Challenge

The organisation ran a modern cloud data stack alongside legacy systems, spread across three functions: Data Engineering, Insight & Analytics, and Data Services & Integration. Its monitoring told the team whether a pipeline had run. It said nothing about whether the data that came out was complete, accurate or reliable.

"We lacked visibility into where and why issues occurred," says the organisation's Head of Data.

When something broke, nobody could see where it started. Each issue meant pulling engineering, analytics and integration into the same hunt, and developers lost hours to triage and reactive fixes instead of the work they were hired to do. By the time a problem surfaced in a downstream report, trust in that report had already taken a hit.

Why Bigeye

The team first saw Bigeye at a sector conference. It followed up with analyst research and a formal evaluation of three data observability products.

Bigeye came out ahead on usability, and it fit the existing stack, including the integration platform and BI tools that were non-negotiable requirements. It also measured the quality of the data itself, not just whether it arrived. Just as important, the team wanted a partner that would work alongside it rather than a transactional vendor.

"The chosen solution was the most usable and aligned with our values around partnership and integration."

Starting Where It Matters Most

Rather than switching everything on at once, the team began with its highest-impact datasets and agreed success measures with stakeholders up front. The platform was live within a few weeks, and automated metric deployment let the team extend coverage without configuring every check by hand.

Alerts go straight into Microsoft Teams, so problems reach the people who can fix them in the tools they already use. Manual monitoring gave way to an automated workflow.

"Early stakeholder engagement, clear success metrics, and a phased rollout were critical," the Head of Data says. "We recommend starting with high-impact datasets and expanding iteratively."

Lineage: Seeing Where Problems Start

Of everything the team now uses, lineage has made the biggest difference. When a check fails, lineage traces the issue back across systems to its source and shows what sits downstream.

"Lineage has been the most useful to date."

That shared picture changed how the three functions work together. Engineering and analytics no longer piece an issue together through rounds of messages; they look at the same map and know where to act.

From Reactive to Proactive

The shift has been measurable. Triage time is down about 30%, giving back roughly 7.5–9 hours of engineering time every week. Issues surface before they reach reports, downstream errors are falling, and subject-matter experts across the organisation can now see quality metrics for the data they depend on.

With the first phase complete, the team is extending observability to more datasets, departments and use cases.

For any organisation whose decisions carry real-world consequences, the lesson is the same: finding out about bad data after the fact is too late. With Bigeye, this team catches it first. And as more organisations put AI to work on their data, that foundation matters even more: AI is only as trustworthy as the data underneath it, and you can't trust data you can't see.