Executive Report Anomaly Detection
Bad data gets into reports more often through a quiet upstream change than through a dramatic failure: a source system update changes how a field is populated, an ETL job silently drops rows on a join, a currency conversion job stops updating and every figure is technically 'correct' at last week's exchange rate. None of these break the report visually, the dashboard still loads, the numbers still look like numbers, so they get caught late, usually when someone in a meeting asks why a metric looks wrong and someone else has to dig backward through the pipeline to find out what changed and when.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 3-6 hrs/week plus avoided cost of decisions made on bad data.
How the automation works
We build automated anomaly detection directly into your reporting pipeline, checking key metrics against expected ranges derived from historical patterns before the data reaches a dashboard or report, with the range models built to understand genuine seasonality and trend rather than flagging every normal fluctuation. When a metric moves outside its expected range, given the time of year, day of week, and recent trend, it's flagged with a specific explanation of what changed and by how much, and routed to whoever owns that data source for a quick check before stakeholders see it. Each anomaly is logged with resolution status, building a running record of which upstream changes have historically caused data quality issues, so recurring problem sources get surfaced over time instead of being rediscovered from scratch each time.
Process flow
- 01
New data lands in the pipeline trigger
Data arrives from a scheduled ETL run, a source system sync, or a report refresh trigger.
- 02
Compare against seasonality-aware baseline ai
Key metrics are compared against expected ranges built from historical patterns that account for day-of-week, seasonal, and trend effects, not a flat threshold.
- 03
Flag genuine anomalies ai
Metrics outside the expected range are flagged with a specific description of the deviation, distinguishing likely data issues from genuine business changes where possible.
- 04
Route to data owner output
Flagged anomalies are routed to whoever owns that data source or pipeline stage for a quick check, with the deviation detail attached.
- 05
Log resolution and build pattern history output
Once resolved, the anomaly and its root cause are logged, building a running history of recurring problem sources for future reference.
Inputs
- Historical metric data for baseline modeling
- Live data from reporting pipeline
- Data ownership mapping by source/pipeline stage
- Seasonality and trend patterns
Outputs
- Flagged anomalies with deviation detail
- Data owner routing and alert
- Anomaly resolution log and pattern history
- Reduced false-alarm rate over time as baselines learn
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Anomaly detection that doesn't account for genuine seasonality is close to useless in practice — a retail business seeing a normal holiday spike, or a B2B company seeing a normal summer slowdown, will trigger constant false alarms against a flat historical average, and teams quickly learn to ignore an alert system that cries wolf.
- Not every anomaly is a data quality problem — a real business event (a major customer churning, a one-off large deal closing) produces the exact same statistical signature as a broken pipeline, so the alert needs to present the deviation with context for a human judgment call, not auto-classify it as an error.
- Anomaly thresholds tuned once at setup go stale as the business grows or changes shape — a threshold that made sense for last year's revenue scale will either miss real problems or fire constantly a year later unless the baseline model is retrained on a rolling basis.
- The most dangerous data quality failures are the ones that produce a plausible-looking but wrong number rather than an obviously broken one, a currency rate that stopped updating still produces real-looking figures, so detection needs to check pipeline freshness and source system heartbeats, not just whether the final numbers look statistically reasonable.
Frequently asked questions
How does this avoid flagging normal seasonal changes as anomalies?
The baseline model is built from historical patterns that account for day-of-week, monthly, and seasonal trends specific to your business, rather than comparing against a flat average.
Can it tell the difference between a real business event and a data problem?
Not automatically with full certainty — it flags the deviation with context so a human can make that call quickly, since a large real event and a broken pipeline can look statistically identical.
What metrics can this monitor?
Any metric that flows through your reporting pipeline with enough historical data to build a baseline, revenue, usage counts, conversion rates, or operational KPIs.
Does this replace our existing data quality tooling like dbt tests?
It complements tooling like dbt tests, which check structural rules, by adding statistical anomaly detection on the actual values, catching issues that pass structural checks but still look wrong.