Artificial intelligence (AI) is making its impact on organizations of all sizes, across all industries. But what if you don’t have the proper data architecture in place to support AI and machine learning? CIO brought this subject to us in their article, “The Seven Design Principles of an AI-Ready Data Architecture.”
AI is all about data, all the time. Scale and flexibility are at the heart of AI. A cloud-enabled data architecture offers elasticity, letting your organization adjust for the moments where additional computing horsepower is needed.
In some instances the infrastructure adapts. For instance, data and analytics ecosystems on Amazon Web Services (AWS) that are built around Amazon S3 naturally inherit the virtually infinite scalability of S3 storage. However, the compute horsepower required to harness the value of these huge datasets will also need to adjust in tandem.
Metadata is your friend from the beginning. Is your organization identifying and classifying data at the point of ingestion? Metadata is much easier to manage early in the process rather than later, and it has value to organizations far beyond compliance.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
