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Data Quality Watchtower

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OverviewDashboardPrototypeIncident storyTelemetry/api/statsCapabilitiesMVP scopeRoadmapStage + plan

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Prototype

Data Tool · ML

Built by eleventh.Explore the fleet · 6 systems
Capabilities

Capabilities

MVP scope and target audience for Data Quality Watchtower

Connecting
Prototype
Data Tool
ML
Data Quality Watchtower
A monitoring assistant that detects schema drift, anomalies, and suspicious dataset changes before pipelines break.
Problem

Data issues usually surface downstream after dashboards, models, or reports are already wrong.

Why now

Data quality remains operationally important even for AI-native products.

MVP scope
What ships first. Each item is a hard commitment, not a stretch goal.
  • 1Profile tabular datasets and schemas
  • 2Detect drift and row-level anomalies
  • 3Generate plain-language incident summaries
  • 4Store historical validation results
  • 5Compare saved profiles for schema, null-rate, outlier, and cardinality drift
  • 6Gate releases on dataset drift thresholds
Built for

Data teams, analytics engineers, ML ops teams

Stack
Initial direction — subject to refinement once the MVP is exercised against real workload.
Python
DuckDB
Great Expectations
Pandas