Whitepaper
How can healthcare and life science organizations leverage AI? Leaders are realizing the path to impactful, ethical AI begins with trusted, quality data. This playbook reveals how data governance delivers trusted data for critical AI initiatives in the sector.
Learn how data governance drives key healthcare use cases, including:
Thoughtful migration to the Data Cloud with clear data prioritization
AI and ML projects – with applications ranging from patient analysis to privacy and compliance
Critical healthcare and life science goals, like improving patient outcomes, streamlining operations, and reducing costs.
This playbook is designed for healthcare professionals exploring how to implement data governance to fuel AI and ML initiatives with a wide range of applications. Get your copy today!
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In the rapidly evolving regulatory landscape, ensuring compliance with data governance standards is crucial for organizations operating in Singapore. The Monetary Authority of Singapore (MAS) has introduced new data governance guidelines to enhance data protection, transparency, and risk management practices.
Datasheet
Alation Data Governance provides the ability to establish and sustain governance programs to centralize data assets’ metadata to remove silos, reduce compliance risk through policies, standards, and controls for those assets, and unlock an organization's data safely for everyone by guiding people to trusted, high-quality, and governed data to make compliant data-driven decisions.
Webinar On-Demand
As organizations invest in AI, effective governance is crucial to maintain safety and compliance. But did you know it's also critical for creating a consistent framework for innovation? Interac, a leader in digital payments, has partnered with Alation to build a robust AI governance framework. Hear Mohit Sirpal and Shubneet Bharwani from Interac as they share expert insights on creating a trusted data environment for AI. Learn how Interac leverages data intelligence to enable self-service, transparency, and traceability in their AI processes, ensuring they are prepared for future AI initiatives.