By Stijn Dolphen, David Backx
Data issues rarely announce themselves politely. More often, they surface when business stakeholders discover inconsistencies in important meetings at which point trust is already damaged. The solution is not adding more tests, it’s an operating model to move from reactive firefighting to proactive monitoring, faster root cause analysis, and shared ownership. Of course you first need metadata and a way to monitor quality, but once the base is there, AI can become a force multiplier. It accelerates detection so teams can quickly understand what broke, why it broke, and resolve issues with confidence.
If your team is scaling data quality and wants to move from firefighting to proactive control, this framework will help you get there.
A foundational model
Before introducing fancy tools and AI, we need an approach that is operational. Here is our model approach that scales:
1) Detect the failures that matter most
Not all data quality failures are equal. Some are internal reporting annoyances, others can harm customers or business decisions. Start with checks that protect:
- customer-facing outputs
- high-impact KPIs
- critical transformations where logic is reused across domains
This is where you get the fastest trust ROI.
2) Make failures actionable
A failed test is not a workflow. It is a signal. To move from firefighting to control, each failure needs enough context that someone can act without having to perform an investigation first.
3) Assign ownership to the right people
If every failure routes through the data team, you have built a bottleneck. The goal is not a better solution team. It is a system where product teams and domain owners can resolve issues directly, because the information is clear and the responsibility is explicit.
Proactive alerts instead of apologies
Early alerting often starts with basic “pipeline failed” notifications. They create noise and panic, which is a start, but they do not yet accelerate resolution. We can evolve alerts into business-friendly incident messages that include:
- which test failed and why it matters
- sample impacted records (or a minimal reproduction)
- severity of the issue
- direct links to dashboards, queries, lineage, and documentation
These upgrades matter more than most people expect, because it turns monitoring directly into action.
A concrete example
We implemented an alerting workflow that sent alerts directly to product teams, who were not involved before. The effect was immediate.
The same day the alert went out, the owning team did not just acknowledge the issue, they fixed it. No escalation chain. No handover needed. That is the clearest signal of scaled ownership: the people closest to the domain fix the issue because the alert contains enough context to act. If you want a single proof that your data trust program is working, look for this behavior change.
One platform, many stakeholders
To scale beyond individual heroes, metadata and ownership need to live in one central place. When test results, metadata, and lineage are consolidated into one platform:
- data analysts can self-serve documented assets and investigate issues without guessing
- business users can add critical context (for example recent releases or changes in upstream systems)
- analytics engineers stop being the only gateway to what is true and what broke
This can drastically reduce handovers and improve collaboration. However, a single platform is only as useful as the metadata inside it.
Example on scaling metadata
Manual documentation does not scale, and it will lag quickly when teams are under pressure or lose track. We introduced an automated documentation workflow that explores schemas, reads table and column context, and generates high-quality documentation into the metadata layer. This way, documentation becomes a continuously updated default, not a backlog item. This matters even more when you introduce AI, because AI is only as good as the metadata layer it has access to.
AI Agents as force multipliers
AI becomes useful when your system already produces structured signals: failed checks, lineage, ownership and metadata. Instead of treating agents like a nice-to-have, we treated them like acceleration on top of the operating model.
Our first agent started with descriptive validation and basic exploration. Useful, but not solving issues at hand. The real value showed up when it performed root cause analysis across the failure, lineage, and metadata and even proposed something we had missed before. The issue was not in the downstream model that raised the alert. It lived inside a shared transformation logic. Nobody suspected it because shared functions feel “stable”, so teams often look first elsewhere.
Obviously AI should not silently change production logic. We always need to keep humans in the loop:
- the agent proposes hypotheses and next diagnostic steps
- suggestions are reviewed before any activation
- outputs are traceable and auditable
That partnership is what makes data trust scalable with AI.
Conclusion
- Start where it matters the most. Prioritize tests on customer-facing outputs and decision-critical KPIs.
- An alert without context is noise. Add sample data, severity, and direct links so owners can act immediately.
- Route failures to the team that can fix the root cause. Ownership is proven when product teams resolve issues without data team mediation.
- AI amplifies mature foundations. With lineage, metadata, and test signals in place, AI can compress root cause analysis and help fixes land at the source.
When technical rigor is aligned with business relevance and ownership, data quality stops being “engineering hygiene” and becomes a shared capability for confident decisions.
Want to scale data trust with AI and move from reactive firefighting to proactive control? Contact us to explore how this operating model can work in your organization.
Stijn Dolphen is a Team Lead & Analytics Engineer at Dataroots. Stijn works at the intersection of engineering, product, and business teams; translating complex data challenges into clear operating models that accelerate decision‑making and reduce friction across the organization. Connect with Stijn on LinkedIn
David Backx is a Data & AI Consultant at Dataroots, helping teams design scalable data platforms and reliable analytics solutions. He focuses on turning complex technical challenges into clear, actionable systems that support confident, data‑driven decisions. Connect with David on LinkedIn