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.
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.
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.
