One part of the economy that's been particularly quick to embrace the potential of big data is the retail sector. Given the large amounts of customer information these firms collect as a matter of course, being able to feed this into an advanced analytics platform in order to gain insight is a natural fit.

Therefore, forward thinking retailers were some of the first adopters of big data analytics technology, and have developed their innovations into mature solutions that can give them a leg-up over competitors. But what does this look like in the real world?

Datanami recently highlighted several key use cases for big data that retailers are employing. While some are straightforward, there are also some more complex solutions in place that companies are using to understand the market and offer the best products and service.

For starters, product recommendation is a key area for big data. This is particularly popular among ecommerce retailers, as it is a relatively simple use of the technology, but one that can have a big impact. By using machine learning techniques and historical data, smart retailers can generate accurate recommendations before the customer leaves their site.

Eric Thorston, Hortonworks‘ general manager for consumer products, told Datanami: When you think about recommendations, everybody wants to beat Amazon. Love them or hate them – most retailers hate them – Amazon makes from 35 per cent to 60 per cent revenue uplift on recommendations, and everybody is saying, How can we get a piece of that?

But this is just the tip of the iceberg when it comes to what big data can offer retailers. For instance, another common use case for the technology is market basket analysis. Looking at which groups of products are commonly purchased together is an activity that has been carried out manually for decades, but with the advent of tools such as Hadoop, retailers can automate the process and delve much deeper into their data.

In the past, such activities may only use a small sample of customers, with receipts going back one or two years. But big data can greatly expand this, offering companies much more accurate results they can use to inform future strategy.

Big data is also a major benefit when it comes to analysing unstructured data, such as social media posts. In today's environment, any company that does not listen to its customers on platforms such as Twitter and Instagram will be missing out on a huge amount of potentially valuable information, and retailers are no exception.

Tools such as Hadoop use natural language processing to extract information from these channels and play a critical role in helping firms understand their audience. However, Mr Thorston warned this is an activity that must be conducted carefully.

"The minute you make a wrong move, you lose," he said. "The obligation is to use it judiciously. That prevents the misuse and that also preserves and supports and aligns to the ultimate goal, which is customer intimacy, customer loyalty, increased revenue, and increased margin."

These are just a few examples of how big data can help retailers. In addition to this, processes such as price optimisation, inventory management and fraud detection all stand to benefit from the technology.