Question: I need a system that can ensure data quality and trustworthiness for AI and machine learning applications in the financial services industry.

For a system that can help ensure data quality and trustworthiness for AI and machine learning workloads in financial services, Collibra is a strong contender as a data intelligence platform. It has tools for monitoring data quality, tracking data pipeline reliability, discovering data, managing data in multiple systems, enforcing policies and reducing transparency and compliance risk. The platform is designed to automate data processes, minimize manual work and provide a unified view of data assets across the enterprise, making it highly scalable and adaptable.

Another strong contender is Informatica, a cloud-based, AI-infused data management platform that connects, manages and unifies data across multi-cloud and hybrid environments. It uses its CLAIRE AI engine to automate data integration work. Informatica spans data catalog, data integration and engineering, data quality and observability, governance, access and privacy, and data marketplace, so it's a good fit for companies trying to modernize their data management and get their data in the right state for AI.

Securiti also has a powerful platform for security, privacy, governance and compliance across complex data systems. Its Data Command Center offers data discovery, security, sensitive data intelligence, governance, data flow management, breach analysis and data risk management. With AI model discovery, risk assessment and compliance checks, Securiti helps companies simplify data management and improve data security while meeting global regulatory requirements.

Last, Metaplane is a data observability platform that focuses on automated end-to-end data observability. It includes ML-based monitoring and anomaly detection, data CI/CD, column-level lineage and impact analysis, usage analytics and real-time schema change alerts. Metaplane is designed to help data teams quickly identify and fix data problems, build data trust and collaborate better. It's geared for teams that want to reduce the time spent triaging data quality problems.