Trusted data for trusted AI
Enable AI initiatives with self-service and trusted AI-ready data:
Accelerate AI model development with a single source of truth for users to find accurate, governed, and contextual datasets via Intelligent Search
Ensure Quality Data by empowering users to trust datasets fed into models with data quality monitoring
Provide AI Traceability to stakeholders and auditors by cataloging AI/ML assets, including datasets, notebooks, and vector databases
Active AI governance
Future-proof and centralize your AI and data to remain safe and compliant:
Understand your AI Models by documenting model information and enforcing policies. Identify non-sensitive data for training
Adapt to the changing AI data stack with an open, agnostic platform compatible with open APIs, automation bots, and 120+ connectors
Actively govern your AI products with Alation’s AI Readiness Accelerator, ensuring compliance, transparency, and more
Drive AI literacy, collaboration, and transparency
Harness contextual and collaborative data intelligence to drive effective models:
Build Superior AI Models by using ALLIE AI-assisted and human-curated metadata from the catalog
Empower a Data Culture to collectively enhance the value of data and AI models
Streamline Data and AI Literacy by fostering an organizational culture of data literacy with Alation’s Data Culture Maturity Model
"The key to using AI and ML to make decisions is having context for the data. Alation can ensure that the datasets are labeled with accurate metadata, which tells the machine how to understand the context around the data it’s looking at. Only then can it answer our questions in a way that’s meaningful."
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FAQs about AI governance
How can businesses integrate AI governance frameworks to support data use and regulatory compliance?
AI governance frameworks set policies for how data is collected, labeled, and used in models. By enforcing these policies in a governance platform, businesses ensure compliance with privacy laws and ethical AI practices while maintaining trust.
How does AI data governance impact the accuracy and reliability of business analytics?
Governed data provides consistency, transparency, and quality—key inputs for accurate AI models and analytics. Without governance, bias, poor data quality, and compliance risks undermine results.
How do I establish an AI governance framework or program?
Begin by defining policies for data usage, bias monitoring, model documentation, and accountability. Then, implement these policies with supporting technology that integrates governance into the AI lifecycle.
What tools and technologies support AI governance?
Key technologies include data catalogs, lineage and quality solutions, model monitoring tools, and AI governance platforms. Together, they provide visibility and control across data and AI pipelines.
How does AI affect healthcare data governance?
AI can accelerate discovery, classification, and anomaly detection—but must be implemented responsibly. Governance ensures model inputs are documented, sensitive data is protected, and lineage is clear. Integrated with a catalog (e.g., Alation), AI-driven classification and quality monitoring scale oversight without sacrificing ethics or privacy.
How do I establish an AI governance framework or program?
Begin by defining policies for data usage, bias monitoring, model documentation, and accountability. Then, implement these policies with supporting technology that integrates governance into the AI lifecycle.
How does AI data governance impact the accuracy and reliability of business analytics?
Governed data provides consistency, transparency, and quality—key inputs for accurate AI models and analytics. Without governance, bias, poor data quality, and compliance risks undermine results.