Data Management500+ (Mid-market)Cross-industry

Automated data quality monitoring

Continuously monitor data quality (completeness, consistency, freshness) and alert on drift.

ROI ★★★★☆Complexity ●●○○○3-6 months500+ (Mid-market)

The problem

No reliable steering on automated data quality monitoring.

The current process is manual or inconsistent.

Decisions come too late due to weak signals.

Trust in data is insufficient.

Prerequisites: required data & tools

Required data

  • Bases de data
  • flux de data
  • règles métier

Compatible tools

  • Great Expectations
  • Monte Carlo
  • Soda
  • dbt tests

Not sure you have the data? Our Maturity Auditor can assess your situation in two weeks.

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What we implement in 3-6 months

In 3-6 months: Continuously monitor data quality (completeness, consistency, freshness) and alert on drift. with measured impact on data incident reduction.

Weeks 1-2

Diagnosis

Weeks 3-6

Build

Week 7+

Delivery

Concrete deliverables

Business framing and decision rules for automated data quality monitoring

Operational engine for automated data quality monitoring

Steering dashboard with alerts

Action playbook and governance

Expert insight

Foundation of any data/AI project. Without data quality, no AI ROI.

— Datasive, expertise terrain

Tech maturity

High

Mature solutions, fast deployment

Medium

Maturing tech, requires customization

Emerging

Cutting-edge innovation, R&D approach

Powered by specialized agents

Data Quality & Observability

Traiter la data quality comme un produit avec SLA, alerting, et runbooks.

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Maturity Auditor

Scorecard + roadmap 90 jours pour cadrer la transformation data/AI.

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Ready to solve this problem?

First step: a 30-minute call to understand your context.