Accelerating digital transformation via dqm - case study
Accelerating Digital Transformation Through AI-Driven Data Quality Management
Data-driven systems in operational technology (OT) environments face two critical types of vulnerabilities:
- External threats, such as cyber intrusions into data streams, and
- Internal threats, including systemic weaknesses that lead to poor-quality data.
Among these, data quality is the most significant risk to reliable decision-making systems. In OT and critical infrastructure environments, very few technologies effectively address both internal and external data integrity challenges. Poor-quality data creates unreliable processes and is difficult and time-consuming to identify. Industry research shows:
- 80% of data professionals’ time is spent manually cleaning and validating data.
- Over 50% of operational events are missed by algorithms
Challenge
A major offshore drilling contractor was generating millions of sensor data points across over 40,000 signals, with each signal costing approximately $75 annually to manage. The estimated cost of validating sensor signals reached $3 million per rig per year.
The client needed a solution that would:
- Dramatically reduce turnaround time and service costs, and
- Enable the reliable deployment of decision systems across SCADA, ICS, maintenance, automation, optimization, and cybersecurity programs.
Solution
UTSI implemented its DQM platform, which conducts 30+ validation checks per signal. The platform uses a hybrid of AI, physics-based modeling, and analytics to build a specification profile for each signal and continuously validate that data in real time.
Because quality data must have context, the system also uses automated data labeling to identify both the dynamic state and event status of each signal—powering more accurate and resilient decision systems.
DQM Workflow:
- Verification – Detects missing or incorrect metadata.
- Intrinsic Validation – Assesses each signal’s inherent statistical properties.
- Pragmatic Validation – Applies AI/ML, physics, and analytics to determine if data is suitable for its intended use.
- Quality Scoring – Assigns each signal a score from 0–100, enabling quick prioritization and trust in system outputs.
Results
DQM enabled the client to accelerate the deployment of a large offshore IoT platform, shrinking the estimated digital transformation timeline from 2 years to just 12 weeks.
Key measurable outcomes included:
- Manual validation time per 4.5 million data points: 1 week
- Automated validation time: 6 minutes
- Deployment of decision systems in months instead of years
Conclusion
By applying DQM, the client not only reduced costs and deployment time but also strengthened cyber resilience and operational efficiency. This case highlights the transformative value of contextual, AI-driven data validation in high-stakes OT environments.