Artificial intelligence and machine learning are innovative technologies that provide powerful use cases across all industries, including data management. But with these solutions come some headaches, too, as OT & IT professionals learn to process, analyze and create actionable insights around their data.
And that’s not to mention all the challenges related to retaining, storing and accessing old data for future use. For insights and some answers, host Daniel Litwin tapped Ray McCay, VP of Solution Sales, and Michael Lamb, Product Manager for Solution Infrastructure, from ViON, an IT storage and services solutions company.
“AI is giving the world new value propositions every day, and everyone is learning how to think about new AI actions they can take in the future to drive more value for the entire world,” McCay said. “So, now the concept of data having expiration dates starts to go away, because I’m using my data today to create value, and, tomorrow, data that I wouldn’t have used [in the past] will suddenly be useful to me again.”
McCay also said the data management model is changing as the data model itself is changing. “We have to effectively manage that data, manage the performance, manage the cost, manage the access patterns, and, when technology and use technology changes, the management of that technology changes too.”
It is essential when managing data to consider both active and inactive data storage needs. “You need to make sure management platform is going to be able to talk to your active performance NVMe storage all the way down to the cheapest inactive storage, whether that’s spinning hard drive or tape media, and see all the data that’s there,” Lamb said.