Endless streams of Latin letters and numerals characterize the legendary motion picture Matrix. Operator Tank can process tonnes of it in seconds. Trinity takes only a few moments to learn how to ride a military M-109 helicopter when she is rescuing Morpheus along with Neo. Of course she is plugged in and Tank only has to pass on the codes to her brain. Neo is the only soul who can see the data in matrix as is, in its rawest original form. Hence he can dodge bullets and fly whenever he wishes to like no one else.
Back to real life.
Imagine a world where all data could be processed, deciphered and converted to intelligence, i.e., knowledge for improving the quality of life, however mostly limited to the business world. This has been achieved to an extent by mankind, of up to 10% roughly by the most commonly used BI or Business Intelligence systems.
Business Intelligence systems (comprising of software, tools, applications) is what enterprises use to make sense out of their data, to make decisions that influence their overall performance. A basic example: A bunch of data such as isolated numbers means nothing unless, for example, put in the form of a graph or a report to be of use to a business enterprise. BIs convert data into useful information.
But what of data that BIs don’t understand? It is called unstructured data. Textual data such as reports, books, manuals, resumes, feedbacks, posts, tweets, … et all. How does one decipher and convert alphabetical letters into information? Data mining, Natural Language Processing (NLP), text analytics, noisy-text analytics, semantics and linguistics are various methods that help interpret unstructured data in more meaningful ways than just performing basic keyword searches.
One of the emerging offshoot of data interpretation – Contextual Analytics – is bound to become the most sought method for deciphering unstructured data to gain intelligence. This new form of knowledge is so accurate and precise that it has direct impact on costs and the overall performance of especially large organizations. The reason for this is that Contextual Analytics is the closest to how humans process textual data. It mimics the same. In other words, it is based on what the world knows as Machine Learning.
A thousand tweets received in response to a business campaign – could Contextual Analytics deduce their overall emotion?
The real value and worth of Big Data can be realized only when advanced forms of Contextual Intelligence is applied to it.