Many firms may not be realizing the full potential of their big data analytics operations because they are not able to study all of the information they possess using these solutions.

This is according to figures from Forrester, which suggest only 12 percent of the average company's information is being processed using data analytics tools. Research analyst at the organization Mike Gualtieri told the recent Hadoop Summit in Amsterdam that the majority of information is simply placed in 'cold storage' in a firm's data warehouse and then never used, Computing reports.

For many businesses, the problem lies in the fact they have developed large data silos that prevent their data being viewed and analyzed quickly by personnel from across the company, which can lead to biases creeping into any evaluations that are performed and obscure new profit-making opportunities.

However, this is where using technologies such as Hadoop can help businesses, Mr Gualtieri noted. He said: "Hadoop allows you to gather all of your data and break down silos, but it is also a framework for processing that data. I would call it the first data operating system."

Mr Gualtieri added that companies should look to ensure that Hadoop is also able to work alongside their existing technologies, so they can use both to analyze more data and meet the evolving demands of customers.

In today's environment, consumers expect to be treated as individuals, so when it comes to activities such as marketing and customer service, generalised communications that do not take into account a person's preferences will no longer be adequate.

This is something more firms have to deal with, as around two-thirds of organizations surveyed by Forrester stated their customers expected to be treated as individuals.

Mr Gualtieri described this as the "royalty" trend, and went on the suggest that businesses may be able to take advantage of new technology such as mobile devices and wearables to improve the service they offer to customers by sharing data between companies.

As an example, he suggested that if someone wears a fitness monitoring wristband and then visits a restaurant, the eatery could obtain the data from the wearable-maker to alter its menu for that particular person, to suggest items they could eat with the same or fewer calories than the person had burned during their workout.