AI implementation
Models stall at “proof of concept” due to poor data
Delivering documented lineage, unified semantics & feature stores
Providing the clean, labelled, bias-checked data pipelines ML demands
Data Quality
Inconsistent, error prone data undermines trust
Establishing data owners, standards, and automated quality rules and testing processes
Monitoring accuracy, completeness & freshness in real time
Monetization
Data assets hidden in silos deliver no ROI
Creating a governed data catalog that exposes high-value datasets
Enabling compliant data sharing & new data-product revenue streams
