White Papers Deriving Business Value from Big Data using Sentim | Page 2

INTRODUCTION
‘Big Data’ are two small words that are widely used to describe the massive growth in data of all forms and that
hold; the promise of delivering huge potential business impact. The question is, how?
Today, and increasingly in the future, businesses are surrounded by masses of data and raw information. Some
of this data is very relevant but much of it is not. Further, most of that data is unstructured in the form of email,
documents, images and different types of social media, blogs, and so on. Unstructured data is notoriously difficult
to access and query, it is scattered across many different locations and formats, and it requires some form of
preprocessing before it can be analyzed and used. Yet, it is this unstructured type data that is primarily exploding
in quantity, representing around 80 per cent of the annual growth of data and doubling in quantity every two years.
A few years ago, ‘Big Data’ was just another buzzword; a fad perhaps that would eventually fade. Today though, big
data is increasingly being used to provide deep insight and predictive analysis in to everything from stock market
movements to individual buying behaviors. Those that are able to make use and harness the power of this disruptive force in markets will benefit by being smarter, faster and more efficient, meaning they are more likely to seize
opportunities early and thereby profit. In the financial services industry, this possibility has not been lost on the
banks who along with associated firms, are investing heavily in applying a variety of technologies and approaches
to unlocking the value of ‘big data’.
How might big data be used practically in the commodity trading and risk management world? This white paper
attempts to answer this question and describes a practical application brought to market by DataGenic that uses
sentiment analysis to predict the price of crude oil.

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