AI and machine learning: How much data is enough? paid DellTech with intel
For most organizations, volume isn't the problem. Large enterprises typically have more data than they really need, says Ray O'Farrell, executive vice president and CTO of VMware.believe AI is a key enabler of their organization's future success. Four out of five of those organizations have AI programs in place or are currently piloting them; 74 percent have 10 or more separate initiatives underway.
"Questions I hear quite often from our customers are, 'Do we have the right types of data to fuel our AI programs' and, 'How do we value it?'" says O'Farrell."Organizations know there's value in the data they've collected, but they don't always know how to quantify that value or how to extract it." To be safe, many organizations seem to default to keeping all data in case they might one day need it, O'Farrell says. But the more data organizations keep, the more resources they must expend to store and secure it. The larger that data trove becomes, the more tempting a target it is for external attackers. And when that data is controlled by multiple siloed units within a company, the potential for compliance issues and data breaches grows significantly.
Not only do disparate groups end up managing data using different policies, they may be storing it in different places – on premise, in the cloud, or at the edge. Enterprise data is also likely to be mirrored, replicated, or backed up in multiple places, notes O'Farrell. Under the EU's General Data Protection Regulation , an organization's customers have some rights to ask that their information be “forgotten”. The sheer number of places where that data might reside makes fulfilling such a request enormously complicated.
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