You have monitors. You have alerts. You have data lineage — at least for the tables that matter most. Your pipelines run on schedule, your Slack channel catches most incidents within the hour, and your on-call rotation, while not exactly popular, works.
And still. Someone in a Monday exec meeting spots a number that doesn't add up. Not your team. Not your alerting system. The CFO.
This is not a tooling failure. It is a trust failure. And the distinction matters enormously for what you build next.
Detection was the right problem for 2019
The first wave of data observability tools solved a real problem. Pipelines were becoming too complex to monitor manually. Schema changes happened silently. Freshness degraded without anyone knowing. Automated anomaly detection, freshness checks, and volume monitors were exactly what data teams needed.
Those tools did what they promised. The pipelines got more reliable. On-call engineers stopped finding out about breaks from downstream users.
But data teams did not stay the same. The stack didn't either. And the questions being asked of data changed fundamentally.
By 2026, data engineers now spend 37% of their time on AI projects — up from 19% in 2023, projected to reach 61% by 2027. The role has shifted: from keeping pipelines running, to building the infrastructure that business decisions and AI systems depend on.
The monitoring tools did not shift with them.
The gap that opened up
Here is the practical problem. A monitor fires. A table is stale. A column has drifted outside its expected range. Your on-call engineer gets a Slack notification.
Then what?
Which dashboard is in the blast radius? Which business process depends on this table? Who owns the upstream asset? Is the CFO's Monday report affected? Is the AI recommendation engine consuming this column? Of the forty alerts that fired this morning, which one actually matters?
Most data quality monitoring tools stop at the first question. They tell you something changed. They don't tell you whether it matters, to whom, or what to do about it.
That gap — between detection and operational context — is where the hours go. And it is where trust erodes.
The data team knows something is broken. The business doesn't know yet. The race to understand blast radius before someone in a meeting notices is exactly the kind of firefighting that makes data engineering jobs unnecessarily difficult.
Monitor coverage is not the same as trust coverage
This distinction is the most important reframe in modern data infrastructure.
Monitor coverage is a quantity: how much of your stack is instrumented, how many checks are running, how fast anomalies are detected.
Trust coverage is a quality: whether the data feeding key decisions can be defended. Whether the right people are alerted when the right things break. Whether root cause is findable in minutes, not days.
You can have complete monitor coverage and near-zero trust coverage. The two are not correlated.
The teams that close this gap are not the ones running the most monitors. They are the ones who have connected their observability layer to business ownership, data lineage, data product criticality, and downstream impact — so that when something fires, the blast radius is visible immediately, not after two hours of manual investigation.
This is what a control plane for Data and AI actually means. Not a better monitor. A layer that connects detection to context to action.
What this looks like operationally
When a table fails a freshness check, a trust-aware control plane surfaces the following automatically:
- Which data products depend on this table
- Which dashboards and reports are in the blast radius
- Who owns the upstream and downstream assets
- Whether there is a known schema change or deployment that explains the drift
- Which business stakeholders are relying on this data right now
- Whether any AI systems are consuming this column
Root cause stops being an investigation. It becomes a visible trail.
The data engineer on-call doesn't spend two hours checking lineage manually, looking up owners in a catalog, and cross-referencing deployment logs. That context is surfaced as part of the incident. Resolution time drops. Escalations drop. The business stops finding out about data problems before the data team does.
This is exactly what teams like BBC Studios and Carrefour Links describe: not that Sifflet reduced alert volume, but that it made every alert carry enough context to act on immediately. You can read their experiences in Sifflet's customer stories.
The AI layer
This matters even more once AI enters the picture.
Most enterprises now run AI systems that consume data at scale. The failure mode is subtle and dangerous: bad data enters a model, the model produces plausible-looking wrong outputs, and by the time anyone notices, the downstream decisions affected are too numerous to audit cleanly.
Gartner estimates 30% of AI initiatives are abandoned due to data quality. That figure isn't surprising to anyone who has tried to run a production AI system on data they cannot fully trust.
There are two philosophies for handling this. The first is to watch what AI outputs — tracing prompts, completions, hallucinations, latency. Useful. But reactive. By the time a bad AI output is detected, the underlying data was already broken. You caught the symptom.
The second philosophy is to control what AI consumes. Quality, lineage, ownership, semantic context — enforced at the source, before any AI system ever reads the table. If the inputs are trustworthy, the outputs are defensible. This is exactly what Sifflet's AI observability approach is built around, and what the AI Agents for Data Observability product operationalises.
These are not the same product. They are not even solving the same problem.
The operational model most teams are missing
Most enterprises have already invested in two of the three layers they need:
- Observability — detecting anomalies, freshness issues, schema drift
- Governance — catalog, ownership, glossary, domain structures, data products
What they are missing is the operational connection between them. The catalog knows who owns every asset. The observability tool knows something is broken. Neither talks to the other when an incident fires.
The data team is left doing that translation manually, every time, under pressure.
A control plane makes that connection operational. Ownership flows from the catalog into every alert. Glossary terms and domain criticality surface alongside every incident. The governance investment the organisation already made becomes operationally useful — not a static register consulted after the fact.
If you are evaluating how to make this connection, Sifflet's Data Observability Buyer's Guide covers the key questions to ask any vendor — including how to test whether their lineage is field-level or only table-level, and how to validate blast radius detection in a proof of concept.
A question worth sitting with
If your most important dashboard broke right now, would your current setup tell you within minutes? Or would you find out from the CFO?
Most data teams, if they answer honestly, say the latter.
That is not a monitor problem. You have monitors. It is a trust layer problem. The detection is there. The operational context: blast radius, ownership, impact, root cause... is not.
The teams that solve this are not the ones running more checks. They are the ones who have built the layer that connects monitoring to the business outcomes that depend on it.
That is what Sifflet is built to be: the control plane for Data and AI.
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