Case Study Cross-Industry

Automating Data Quality, Governance & Predictive Insights on Informatica IDMC

Embedding AI and ML across data quality, governance, and integration on Informatica IDMC for a data-driven enterprise with no in-house platform expertise

Data quality errors down 35%, compliance issues down 25%

Summary

A data-driven organization lacking in-house Informatica IDMC expertise partnered with aiDataWorks to embed AI and ML across data governance, quality, and integration. aiDataWorks automated real-time data quality checks, built AI-powered metadata and governance capabilities for compliance and lineage, added predictive analytics for forecasting, and applied ML-driven dynamic data integration. The engagement cut data quality errors by 35%, reduced compliance issues by 25%, boosted forecasting accuracy by 20%, and cut manual integration effort by 30%.

The challenge

  • Data quality at scale. Managing large volumes of diverse data while ensuring quality across complex datasets was a constant strain on the organization.
  • Compliance without automation. Maintaining regulatory compliance was difficult without automated policy enforcement and lineage tracking across data domains.
  • Insights buried in complexity. The complexity of the data landscape made it difficult to harness actionable insights from the organization's data.
  • No in-house IDMC expertise. The organization had no internal skills on Informatica Intelligent Data Management Cloud, leaving much of the platform's capability untapped.

The solution

Solution architecture: source systems → IDMC AI/ML data quality & governance layer → predictive analytics → dynamic data integration Download diagram (PDF)

How we built it

Automated data quality assessment

  • Deployed machine learning models directly within IDMC to run continuous, real-time data quality assessment across the organization's datasets.
  • Used ML-driven anomaly, outlier, and inconsistency detection to surface data quality issues as they occurred rather than after the fact.

AI-powered metadata & governance

  • Built AI-powered metadata management and governance capabilities to establish clear data policies and lineage across the environment.
  • Applied machine learning to identify relationships between datasets, strengthening lineage tracking and supporting regulatory compliance.

Predictive analytics

  • Implemented predictive models on top of the governed data to forecast trends, customer behavior, and market shifts.
  • Gave the business forward-looking insight to inform decisions, rather than relying solely on historical reporting.

AI-driven dynamic integration

  • Applied AI-driven dynamic data mapping and transformation to the integration layer.
  • Trained machine learning models on historical integration patterns so mappings adapted automatically, reducing the manual effort required to integrate new sources.

Outcomes

35% Data quality errors reduced fewer anomalies, outliers & inconsistencies
25% Compliance issues decreased stronger policy & lineage tracking
20% Forecasting accuracy increased trends, behavior & market shifts
  • Real-time, ML-driven quality checks cut data quality errors by 35%, giving downstream teams more confidence in the data they work with.
  • Automated policy enforcement and lineage tracking reduced compliance-related issues by 25%, easing the burden of regulatory reporting.
  • AI-driven dynamic data mapping cut manual integration effort by 30%, freeing the team to focus on higher-value work, while predictive models lifted forecasting accuracy by 20%.

Solving something similar?

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