TwinShip Innovation 7
| 🔍 Innovation Spotlight | TwinShip Innovation 7: Data Quality Governance Strategy (DQGS) In the maritime sector, poor data quality can cost up to $300,000 per ship annually, a staggering loss driven by undetected or unresolved data anomalies. Current industry methods often fall short in identifying complex anomalies in extreme datasets and offer no path for recovery once errors are flagged. 🚢 TwinShip is setting a new course with its advanced DQGS framework, designed to not only detect but also correct anomalies across massive, high-velocity maritime datasets. 📊 The DQGS introduces: • Dual-layer Data Anomaly Detection Filters (DADF 1 & 2) • A structured Data Anomaly Knowledge Database • Intelligent Classification & Recovery Mechanisms • A powerful Data Anomaly Recovery Filter (DARF) These are supported by tools from previous EU projects and platforms, which simulate secure data pipeline deployments across heterogeneous systems. By addressing the 5Vs of data (Volume, Velocity, Variety, Veracity & Value), TwinShip’s DQGS ensures reliable, trusted data for better operational decisions and system resilience. 💡⚓ |
 |