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Detect Data Anomalies Before They Breach Your SLOs

Most systems alert you after something breaks. Acceldata surfaces deviations as they emerge — across metrics, pipelines, and raw data — so your team can act before impact.

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TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Rule-based tools were built for infrastructure. Data breaks differently.

Unknown-Unknowns Hit Business Decisions

Subtle multi-column anomalies — a changed logging format, a misreporting inventory system, a stale data feed — slip past every univariate check and silently corrupt the downstream decisions that depend on them.

Context-Blind Thresholds

A 12% volume drop Tuesday after a holiday is normal. The same drop Wednesday mid-quarter close is a P1. Rules don't know the difference.

Zero Configuration

Three Steps to Detection. No Manual Threshold Tuning.

Most ML-based monitoring tools shift the burden from writing alert rules to writing training pipelines. Acceldata eliminates both.

Select Tables or Pipeline Metrics

Point Acceldata at the tables or pipeline metrics you want to monitor. No agent installation, no SDK changes.

Choose Your Detection Model

Time-series, multivariate, or both. Select the detection mode that matches your pipeline's risk profile.

Set Sensitivity Level

One slider. Low, medium, or high sensitivity. Acceldata handles the rest — including recalibration after schema changes.

Two Detection Modes. Full-Spectrum Coverage

Two complementary detection modes — covering the shape of your pipeline metrics over time and the multivariate relationships inside your data.

Metric-based
Anomaly Detection

Monitor how your data behaves over time

Detect changes in freshness, volume, and data distribution
Identify shifts in data "shape" across columns and datasets
Surface anomalies in metrics like distinct values, averages, and variance

EXAMPLE

Missing regions, delayed pipelines, or unexpected spikes — flagged instantly.

Multivariate
Anomaly Detection

Find hidden issues inside your data

Analyze full datasets across multiple correlated fields
Detect anomalous rows and segments — even when rules pass
Reveal patterns humans didn't anticipate

Customer Example

Global Retailer

Uses multivariate anomaly detection on shipping data. When the system flags an anomalous pattern, a notification reaches the business user directly — who decides in context whether it's normal variation or something that needs a new policy. No engineer in the loop for triage.