Webinar On-Demand
The latest data concepts seem to arrive as quickly as the actual data! The modern data stack (MDS), data fabric, and data mesh are top of mind for leading professionals today. These trends share a common theme: decentralized architecture. But how does this impact the business?
Learn the answer from Karla Kirton, Data Architect at Blockdaemon and Nicola Askham, The Data Governance Coach, who discusses these decentralization megatrends with an eye to business impact. Joined by Matt Turner, Director of Industry Strategy and Partner Marketing at Alation, they answer questions and share advice on how to retain control, security, and governance — while helping people find useful, trustworthy data and information faster.
In this on-demand webinar you will learn:
More about data decentralization and how this can help drive business impact
How you, like Blockdaemon, can create a decentralized utopia
How you can weave data governance principles into your data mesh, helping people find, trust and share data faster
Hear Karla and Nicola, who share lessons learned and best practices to help you on your data journey.
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Webinar Registration
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. Join 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.
Whitepaper
Leaders are under mounting pressure to implement AI that drives business results. How can they ensure their data is ready to fuel AI models that succeed?
Webinar On-Demand
In today’s rapidly evolving digital landscape, effective data governance is critical for organizations striving to manage diverse and numerous data types. Ensuring the trustworthiness of AI/ML models requires robust governance of both inputs and outputs.