{"id":80630,"date":"2023-06-28T09:00:17","date_gmt":"2023-06-28T09:00:17","guid":{"rendered":"https:\/\/www.techopedia.com"},"modified":"2023-12-30T19:45:10","modified_gmt":"2023-12-30T19:45:10","slug":"how-the-combination-of-ai-data-fabrics-is-going-to-revolutionize-data-management","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/synergizing-ai-and-data-fabrics","title":{"rendered":"How the Combination of AI & Data Fabrics Is Going to Revolutionize Data Management"},"content":{"rendered":"

The one thing a properly functioning artificial intelligence<\/a> (AI) model needs is data<\/a>. Lots and lots of data. But rather than just blast data in its direction like a fire hose, the most effective approach is to optimize the data feed through a wide variety of vetting, curation, and analytical methods.<\/p>\n

This requires not just substantial infrastructure<\/a> but highly sophisticated management software<\/a>. These elements, in turn, are blended into a fabric architecture capable of drawing data from multiple sources and dispersing it in multiple directions at once so as to provide the AI model with the broadest perspective possible.<\/p>\n

This is a herculean job, of course. One that would typically demand a vast team of data scientists and IT resource managers. However, thanks to emerging technologies<\/a>, many of these processes can now be automated, and the driving force behind this automation is none other than AI.<\/p>\n

Mutual Support<\/span><\/h2>\n

This symbiotic relationship between AI and data fabrics<\/a> is no accident. In fact, it has been carefully designed to complement the abilities of both technologies to form a more properly functioning whole. As Analytics Insight noted recently, data fabrics provide the kind of timely, accurate, and non-biased data to drive positive results from intelligent algorithms<\/a>, while some of those algorithms enable fabrics to operate at the speed and scale to support AI training and operations.<\/p>\n

Today\u2019s data fabrics provide a wide range of advantages over more traditional network architectures when it comes to supporting AI models. For one, they enable real-time data access from diverse sources, regardless of location. They also break down the silo-based architectures that exist in most organizations and only serve to prevent full versions of the truth from emerging. And fabrics are meant to be not just scalable but highly agile, allowing them to pivot in the face of changing needs and environments.<\/p>\n

In other words, a data fabric provides a holistic view of all information assets available to a given organization, both internally and externally, public and private.<\/p>\n

Douglas Vargo, vice president of consulting services at CGI, says it does this by bringing three core functionalities<\/a> to the AI training process and the broader data environment that drives modern business models:<\/p>\n