AI is having a big impact on organizations of all sizes, across all industries. But if you don’t have the proper data architecture in place to support AI and machine learning, you’re likely to be disappointed in the results you’re seeing. Here are seven principles to consider for an AI-ready data architecture.
- Plan for scale and elasticity.
Artificial intelligence (AI) is all about data, all the time. Does your IT team’s architecture enable computations to be performed on demand? Does the environment allow users the freedom to, say, apply a formula to a large dataset without first asking IT to check server capacity? Scale and elasticity are at the heart of AI. A cloud-enabled data architecture offers elasticity, letting your organization scale up for the moments where additional computing horsepower is needed.