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How ITSV Eliminated 9,000 Data Quality Rules with AI-Driven Observability

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Team of ITSV professionals collaborating at a wooden table with laptops and headphones, representing ITSV’s transformation to AI-driven data quality and observability powered by digna.

INDUSTRY

Healthcare, Social Insurance

INDUSTRY

Healthcare, Social Insurance

INDUSTRY

Healthcare, Social Insurance

USE CASES

Data Quality Monitoring, Timeliness Assurance

USE CASES

Data Quality Monitoring, Timeliness Assurance

USE CASES

Data Quality Monitoring, Timeliness Assurance

MODULES USED

digna Data Anomalies, digna Data Timeliness

MODULES USED

digna Data Anomalies, digna Data Timeliness

MODULES USED

digna Data Anomalies, digna Data Timeliness

“The effortless configurability of digna enables its use as a self-service data quality platform across the entire social insurance sector.”

Thomas Schauer

Head of Data Analytics

The Challenge: 9000 Rules. No Control.

Before implementing digna, ITSV — the IT backbone of Austria’s social insurance — was managing data quality through 9000 hand-crafted rules in its data warehouse. But this traditional approach wasn’t keeping up with the pace or complexity of modern data:

The growing volume (50GB/day from 30+ sources in 500+ structures) made rule maintenance unsustainable

Knowledge was lost due to staff turnover and insufficient documentation

Rules had to be constantly adjusted, but no team had ownership

140+ daily alerts were flooding inboxes — most ignored due to unclear meaning

Only 25% of relevant data quality cases were actually covered

In short: data quality was out of control, and trust in the system was at risk.

The Solution: AI-Powered Anomaly & Timeliness Monitoring

As early as September 2021, we started the Proof of Value with ITSV and began replacing its entire rule-based framework with digna Data Anomalies and digna Data Timeliness — making a full shift to AI-driven observability and trust.

All analysis happened securely inside ITSV's infrastructure — ensuring full data privacy compliance

No tuning, no threshold configuration, no maintenance overhead

digna Data Timeliness used machine learning to learn arrival patterns and alert when data was late, missing, or too early

digna Data Anomalies automatically learned normal patterns for 50GB/day across 30 sources, then flagged deviations — eliminating manual thresholds

Use Case: Nationwide Social Insurance Data

ITSV's warehouse integrates data from five health insurance providers, serving millions of Austrians. This includes highly heterogeneous, sensitive datasets used in billing, medical claims, patient insights, and more.

The challenge wasn’t just data quality — it was data volume, velocity, and accountability. digna made it possible to:

✦ Monitor all incoming tables without writing or rewriting a single rule
✦ Ensure data freshness across regions and pipelines — even without defined schedules
✦ Provide self-service anomaly detection across teams — boosting adoption and coverage

The Results: From Chaos to Confidence

Metric	Pre-digna (2021)	With digna (2025)	Improvement
Data Quality Rules	9,000	0	100% reduction
Case Coverage	25%	90%	3.6x increase
Daily Alerts	140 (mostly ignored)	20 (actionable)	86% fewer false positives
Rule Maintenance	Constant adjustments	Fully automated	0 hours/month

86% alert volume — only the most meaningful issues are surfaced

+260% increase in case coverage — with no added maintenance

Teams can now focus on insights, not infrastructure

By December 2022, ITSV decided to implement digna as a No. 1 data quality platform for the entire Austrian social security organization, extending beyond ITSV's data warehouse.