Mining big data has paid off handsomely for UPS, with the worldwide delivery service saving nearly 150 million litres of fuel and reducing travel distances by 586 unnecessary kilometres through analysing data generated by their services. 

The term 'big data' appears regularly in online articles, magazines and social media feeds - but what does it actually refer to? Everything each and every one of us does, especially our actions online leaves a digital trace which is being recorded somewhere.  Searches on search engines, activity on social media sites like Facebook, and even credit card transactions generate data.

Moreover, big data does not only refer to online activity. Even offline behaviour is constantly being recorded, with increased use of CCTV, sensors credit cards. Smartphones are a source of data as they send GPS locations and record behaviours.

All this data offers several opportunities with various applications, but this data is useless unless it is captured accurately, structured well, tested, mined and analysed by competent people. Only then does that data create value.

Transforming data into intelligence has to be done by professionals such as those at Marketing Advisory Services and Redorange, who are capable of looking at data in a novel and more meaningful way.

The strategic importance and best value which can be derived from big data is the ability for companies to gather insights and predictions which might ultimately lead to making better decisions and strategic business moves. Big data analysis is not only applicable to large corporations, as in today's world even medium and small businesses are generating large amounts of data which is many a time left untapped. 

Most businesses have their own website and some form of social media presence, and almost every company accepts payments via credit card. This means that even a micro company collects data on its customers, user experience, web traffic, and social media interaction. Analysing this data properly can lead to business operation optimisation, predictions about which products will sell, forecasts of customer turnover rate, as well as upcoming trends related to the particular industry.

Analysis of such data can give the company a competitive advantage, or at the very least, it will ensure that the company is not left behind to catch up with its competition.

Big data can also serve as a basis for several uses beyond commercial applications, such as optimising traffic flow in cities, predicting the spread of infectious disease, and finding relationships between lifestyle aspects and certain illnesses. In some cases, it can even predict the results of elections.

However, whilst big data has its merits and can provide us with a great deal of information about what people do, it does not tell us much about why people do what they do, or about the meanings that people attach to objects and actions. 

This is a limitation that one has to keep in mind.  In order to delve deeper into the more nuanced dimensions of human behavioural patterns, different methodological tools such as participant observation and similar ethnographic methods should be employed to substantiate big data analysis.