Companies are losing business opportunities because they don't have the data they need. And when they do have the data, it isn’t provided in a timely fashion. Much of it ends up unused, resulting in millions of pounds of losses annually.

The biggest issue is a lack of people with the required skills to make data valuable. According to research by Pure Storage, 57% of European businesses need better data skills for the right people to have access to the right information.

Rising demand for data expertise in industries like retail, banking and finance is creating a severe skills shortage. A report by the Tech Partnership employers' network and SAS said the UK is expected to create an average of 56,000 big data jobs a year until 2020, with the demand for such professionals pushing their average salary to £55,000, 31% higher than the average IT position.

>See also: How to address the talent shortage in big data

Commentators have already warned of a skills gap now and in the future because not enough people are being trained to meet a rise in business demand for big data and analytics.

Some refer to the 'fourth industrial revolution', in which companies are using machines, devices and data to collect information and infuse intelligence in their operations. This trend means companies will require a different skill set from their workforce – one that revolves around data.

Difficulty in finding people with the right data skills

At the moment, there just aren’t enough people with the necessary skills to interpret all the big data needed. It's difficult to find the people with the right range of qualifications and skills. Organisations might need competencies in areas such as data science, coding, maths, communication and problem solving even for the most junior data roles.

Even trickier is the fact a business might not know exactly what it’s looking for in terms of skills. For example, a data scientist needs to take projects from end-to-end and tell stories about the findings, which requires skills in mathematics, programming and communication.

But a data engineer might be more focused on processing data sets and coding, taking in requests from data scientists. These roles are different to a data analyst position, which needs skills in statistics and business knowledge to understand specific queries from people.

At the moment it isn't uncommon for hiring managers to post a job description for a data analyst role while calling it a data science or data engineer role, which requires a very different set of skills and specialties. Professionals skilled in data analysis and presentation may be very different to people skilled on the coding and technical side of things.

In the long term, there has been a concerted effort to improve digital skills among young people over the last few years. But learning code isn't enough – to be qualified to handle big data, they might need to understand data integration and analysis, as well as be able to apply this in a business setting.

It's a more complex issue than graduates not having the right technical skills – it's more about them having the right combination of skills that allows them to transform data outputs into something valuable to businesses.

What do businesses have to look for?

Universities are certainly making efforts to close this data skills gap, with many universities offering data science and analytics degrees, mainly at a Masters level.

Many of these offer valuable industry experience that can help bridge the gap between academia and the work setting. For example, the MSc Data Science course at City University in London offers students the opportunity to undertake their MSc project in an industrial or research placement over an extended period.

The individual project could be carried out as a six-month internship in one of the companies City has a long-term relationship with, such as Facebook, Google or Microsoft.

However, companies might not necessarily have the time to wait for a new outcrop of graduates. There has been a trend for some companies to internally train employees with the right background and skillsets on the data skills needed, often with online training software.

Another trend is the rise of the citizen data scientist – where tools and technology have advanced to a place in which non-experts can perform analytical tasks that would previously have needed the expertise of a highly skilled data scientist or engineer.

>See also: The journey of a chief data officer – and the culture the role embodies

Gartner has predicted that the number of citizen data scientists will grow five times faster than their highly trained counterparts.

Finally, at a boardroom level, businesses are increasingly employing individuals in the position of a chief data officer (CDO). According to Gartner, the number of CDOs appointed by major organisations rose from 400 in 2014 to 1,000 in 2015, and by 2019, 90% could have a CDO.

The rise of the CDO is a testament to the growing importance that businesses are placing on data, meaning a permanent space at the executive table.

Of course this role has a lot of responsibility – across the organisation they'll be looking at using data as a competitive advantage and to identity new opportunities, execute a data strategy that will drive growth, and inspire change through insight into how innovation can change the business.

Sourced from James Petter, VP EMEA, Pure Storage