Big Data is a term that is heard a lot in technology circles, but understanding what it actually means is not always the easiest. Broadly, it refers to data sets that are so large that traditional attempts to process and analyse them are no longer applicable.

The term has become more common because of the vast amounts of information now available as a result of digital technologies. GPS trackers on our smartphones, social media posts, website cookies and countless other digital footprints that we make each day, all contribute towards the data deluge.

As well as the huge increase in the amount of data now available, both the speed with which data is transferred and the different formats that it appears in has also risen. Big Data is, therefore, a huge enabler for businesses and individuals, but also a significant challenge.

For example, an enterprise firm may wish to know more about its customers in order to improve its level of service and gain a competitive edge. Big Data offers them this information, but obtaining the insight that they need is often easier said than done.

Data can arrive from multiple sources, in a variety of formats and is often completely unstructured. To put the size of this data into some form of context, companies may have to process information in the order of petabytes, which translates as 1,024 terabytes or 1,048,576 gigabytes.

However, it is impossible to define a minimum boundary for what separates “Big Data” from ordinary manageable data. In fact, to set such a definition would not only be arbitrary, but also short-sighted, as the amount of data that we define as “big” is only likely to grow in the future.

Instead, businesses usually adopt the term Big Data when the information that they are trying to process with traditional database and query approaches takes in the region of days and weeks, rather than minutes.

However, if companies are able to capture and analyse their huge data stores, the information gleaned has the potential to transform their business. Where are customers using our services most? Why are they choosing not to buy our product? How long are they using our website and what pages are they visiting? These questions and many more have their answers in Big Data – if firms know how to look for them.

As a result, while “Big Data” as a term can refer to the data itself, it is also used to describe software that helps to analyse or store it. Third party suppliers of these tools may use a variety of methods in order to process this data, ranging from machine learning, NoSQL databases and other forms of advanced analytics.

Increasingly, Big Data companies are focusing on predictive analytics, that is, how large data sets can be used to predict future behaviour. The more data companies have at their disposal, the more accurately they can model what is likely to happen, which has huge ramifications for industries ranging from retail to meteorology.

With the adoption of wearables and the Internet of Things predicted to take off in the coming years, the likelihood is that, whether it’s past, present or future trends you’re looking at, Big Data is only going to get bigger.

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