The potential for a new breed of artificial intelligence applications to unleash new value is enormous, but IT organizations face substantial challenges to effectively implement this technology. Multiple factors—such as performance, scaling, and cost—are frequently cited as pain points, but there is one additional aspect that will ultimately determine the success or failure of any AI initiative: data.

There’s No AI Strategy Without a Data Storage Strategy

Much of the AI conversation to date has been focused on the compute environment, but attention is now turning to the data and storage layer. Data is core to the DNA of media and entertainment companies, government agencies, and life sciences research firms, among many others. Effectively harnessing this data will be critical in driving AI success for each of them.

To Leverage AI Effectively, Organizations Need an End-to-End Data Infrastructure   

The math is simple: The more of the right data a model can be trained on, the better that model will be, and the better the ultimate outcome. To build truly differentiated capabilities, organizations will need to build massive data lakes on their own proprietary data sets. Those data sets will, of course, comprise their most recent data, but many organizations are also beginning to realize there’s an immense amount of potential value hidden within the vast amounts of historic, archived data that also exist within their environment. The clear opportunity, then, is for organizations to build AI models that can leverage the entirety of their data assets, both new and old.

With unstructured data volumes undergoing explosive growth, and with IT organizations under pressure to retain their data longer for AI training purposes, the emerging requirement is to build a data storage environment that can satisfy performance requirements at the training/inference stage as well as cost-effectively retain enormous data volumes of historical data.

Learn More in a New Analyst White Paper by ESG

In a new white paper, Enterprise Strategy Group (ESG) outlines:

• The shifting data management requirements in the AI era.
• What organizations need to consider when building an infrastructure to leverage AI now and into the future in the most cost-effective way.
• Real-world survey data and insights from IT leaders across the globe.

Get the White Paper here.