Shlomo Mirvis is CEO and Co-founder at Intelligo Group.
Big data these days is a lot like the Wild West – untamed, a vast vista with untold resources ripe for the plucking. And more of it keeps coming. This year, more data will be created than has ever existed before – much of it unstructured. By 2020, 44 times more data will be created annually than a decade earlier.
Currently some 80 percent of data collected by companies is unstructured, meaning that firms cannot draw meaningful insights from that information. With the pool of unstructured data growing daily, one of the biggest challenges for companies in the coming years is going to be figuring out how to derive value out of that data.
To obtain value, data first has to be structured, and one of the best methods for giving data an understandable structure that can be searched, evaluated, counted, sliced and diced is by applying semantics technology to it. Semantics is the science of words – how they are used in different contexts, how they are understood, how people interpret words (ambiguity), and more. Semantics, essentially, is the science that tries to understand the structure of how people make themselves understood with words, and the principles of semantics can give structure to data as well, enabling organizations to make it work for them.
Semantics-based technologies have been successfully applied to data structuring since at least 2001, and companies are paying more attention to them after seeing just how good applications built around semantics technologies - like Siri and Alexa - really are.
Semantics have helped companies save time and money by enabling them to cut to the essence of the data they collect – helping them make sense of obtuse and risky endeavors, like hiring. Let's say a company needs to hire a CEO or other top management. The resume checks out, the references are good, and the candidate's experience at previous firms – and their reputation – makes them appear to be the right person for the job.
But as we know all too well today, even those with the most sterling reputations may be hiding something. In an age when secrets are hard to keep – thanks to social media – the details about an individual's less than wholesome behavior could torpedo the company that hires them, along with the stock prices of the firm. Instead of waiting for rumors and allegations to dog their selected CEO, firms should proactively search a candidate's background to determine if there is anything in their past that could come back to haunt the candidate and the company.
Semantic technology can help with that. Newspaper articles about the candidate, public posts in forums, quotes in professional publications are all part of a candidate's profile; and all need to be analyzed to determine what they really mean. It's not just about scandal; companies need to know the real stance of an individual they hire on a range of industry issues. How does a candidate for a top bank position feel about the Fed's position on interest rates? Does their past behavior jibe with what they are telling the people who will be hiring them?
The answer may be in the articles, posts, and quotes involving this candidate. We expect a candidate to put their “best foot forward,” but there may be more to an individual’s “back story” than they are letting on. That back story may be revealed by probing news, comments, posts, and other content generated by and about that candidate. What does all that information really mean? Semantic technology, analyzing what that information means in context, could provide the answers that firms need about the people they are planning to hire – the answers not on the resumes they proffer, but the answers that lie in their backgrounds.
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