Like many of their peers, as business leaders look to the future, they may well be concerned most about getting value out of all the data they possess. They look around at a world of unstructured data, rich in insights, but not suited to traditional legacy databases. They perceive that technologies such as Machine Learning and Artificial Intelligence are becoming mainstream for producing accurate predictions and insights from all this data. And they wonder how on earth are they going to be able to bring on-board the people with the skills they need to leverage these data sources? So they begin to look at hiring a Data Scientist who can use these technologies to help unearth the hidden intelligence of the company’s data assets.
“This stuff is hard,” says Nova Spivack, CEO and co-founder of the enterprise intelligence company Bottlenose. To realize the potential of all the data requires special individuals – Data Scientists, hard-core analysts, those with advanced computer science degrees from elite and cutting-edge university programs. Big companies in popular sectors like tech or finance – Google, Microsoft, IBM, Citigroup, American Express and the like – may not have a problem getting those folks to sign on as employees. But even large global enterprises in more traditional verticals often find themselves scraping by with small Data Science teams.
Spivack relates, for instance, that he recently visited with a giant automaker in such a position. Because the small Data Science group’s services are in such demand, business leaders can’t get their situations serviced as quickly as they need. “One part of the company is responsible for all the analytics, but [the capabilities these employees provide need] to be distributed to every person and every function,” he says.
And if even big guns like these have it rough, imagine the difficulties faced by small- and mid-sized businesses, especially those operating outside of hot markets like the start-up tech sector.
Spivack cites a figure by IDC stating that by 2018, there will be only about 1.5 million managers with proficiency in data-driven decisions in the U.S. and a shortage of nearly 300,000 Data Scientists and Analysts. “They are scarce and getting scarcer,” he says. Even any upticks in students graduating with these skills won’t help anytime soon, as they lack the real-world, practical Machine Learning and AI experiences that are so crucial to success.
“The world can’t meet the demand for these people, not even ten years from now,” he says. “That’s a huge obstacle to real, full adoption of these technologies at all layers of the economy.”
What can the vast numbers of businesses that want in on better Business Intelligence do now, if they lack the money or glamour credentials to attract the cream of the crop in terms of human labor? That’s what the latest version of Bottlenose Nerve Center 3.0 aims to address. The Cognitive Computing platform essentially creates a new AI-automated analyst, according to the company. Companies in the under-$100 million revenue mark are among the planned beneficiaries of its focus on enabling non-Data Scientists and non-Analysts to automate more of what human Data Scientists and analysts do to get as much insights out of data as possible.
According to Spivack, Bottlenose has been observing how Analysts and Data Scientists work; discovering that about 70% to 80% of what they do is repeatable and automatable. Typically, this is boring work where Artificial Intelligence technologies can add a lot of value, he says.
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