Big data these days is a lot like the Wild West – an untamed, vast vista with untold resources ripe for the plucking. More of it keeps coming; this year, more data will be created than has ever existed before – much of it unstructured. By 2020, forty-four times more data will be created annually than a decade earlier.

Currently some 80% 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.

Structuring data with semantics

To obtain value, data first needs to be structured. 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. The principles of semantics can give structure to data, as well, enabling organizations to make that data work for them.

Reaping the benefits of semantics for hiring

Semantics-based technologies have been successfully applied to data structuring since as early as 2001. Companies are paying more attention to these technologies now more than ever after seeing just how good applications built around semantics technologies really are. Just look at the success of Siri and Alexa as examples.

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 and reputation from previous firms makes them appear to be the right person for the job.

However, 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 – mainly thanks to social media – the details about an individual’s less-than-wholesome behavior could end up torpedoing the company that hires them. 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 anyone involved.

Semantic technology can help with that. Newspaper articles about the candidate, public posts in forums, quotes in professional publications – all these are part of a candidate’s profile, and all need to be analyzed to determine what they really mean.

Corralling true meaning

Using semantics for hiring is not all 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 backstory 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, through analysis of what this information means in context, could provide the answers that firms need about the people they are planning to hire. The answers, that is, not present on a resume – rather those that lie underneath.

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