From predicting market trends to gaining insight into customer needs, predictive analytics can help businesses tap into their data to exploit new opportunities and gain the upper hand over competitors.
However, research by German applications giant SAP recently found that businesses are not using big data and predictive analytics to their full potential.
It's not that they fail to see the benefits: 85% of organisations surveyed said that predictive analytics had a positive impact on their business, and 77% believe that it helps them gain the upper hand over competitors.
The main stumbling block was skills, with three quarters of respondents finding that new data science skills are needed within the organisation to take advantage of the technology. TechRadar Pro talks to SAP's VP of Marketing and Analytics James Fisher on what's driving predictive analytics and big data in 2014.
TechRadar Pro: Why is predictive analytics becoming so high on the agenda?
James Fisher: Over the past couple of years we've heard a lot about big data. Businesses are collecting information on their customers' mobile habits, buying habits, web-browsing habits… The list really does go on. However, it is what businesses do with that data that counts. Analytics technology allows organisations to analyse their customer data and turn it into actionable insights, in a way that benefits business.
Predictive analytics technology is the core enabler of big data, allowing businesses to use historical data, combined with customer insight, to predict future events. This could be anything from anticipating customer needs, forecasting wider market trends or managing risk, which in turn offer a competitive advantage, the ability to drive new opportunities and ultimately increase revenue.
TRP: How is the cloud shaping predictive analytics?
JF: Predictive analytics and cloud both continue to be hot topics in the industry. More businesses are looking to make the most of the data that's at their fingertips, whilst taking advantage of cloud-based services to move from capital expense to operational expense. The natural next step is to combine the two.
Predictive analytics in the cloud is gaining momentum. This pairing allows predictive analytics to be more scalable, flexible and easier to deploy. It exploits the well-known advantages of the cloud to improve the return on investment and time to market of the most advanced analytics.
TRP: Are businesses currently getting maximum value out of predictive analytics?
JF: At the moment, we're seeing the mass potential of predictive analytics (and consequently big data) going untapped. For organisations to realise the full ROI of predictive technology, they must integrate forward looking insight into day-to-day tasks by embedding predictive models into applications. This means that employees at all levels of the business will need to be able to interpret data and feed this insight back to the business.
However, getting access to – and making sense of – data has, until recently, been seen as a complex and highly-skilled task, delivered by people with advanced degrees in statistics and prior analytical experience. This dynamic simply can't scale at the pace of the business, and as a result businesses aren't getting the maximum value.
TRP: What are the barriers to adoption for those who haven't yet taken the leap?
JF: As is often the case, it comes down to two things –skills shortage and time. There is a common perception about the skills required to effectively draw insight from data and feed this back to the business. According to research commissioned by SAP, 75% of UK businesses believe that new data science skills are needed within their organisation, and 81% would like specific training to integrate analytics into their day-to-day work.
However, with the increasing availability of newer predictive analytics technologies which are more intuitive and user-friendly, for the first time people at all levels of a business can 'self-service' their need for insight.
TRP: What skills do you think will be needed to make the most of these technologies?
JF: I've already touched on the historical view of predictive analytics as being a skilled and complex task. Where once, making sense of data was the field of a few, dedicated data scientists, sophisticated predictive analysis is now moving to a broad spectrum of users.
There's been a real shift in the skills businesses are looking for. The most important qualifications might not necessarily be academic degrees, certifications or job experience, but so-called 'softer skills' - curiosity, creative flair, the ability to visualise and to communicate clearly with non-technical people throughout the business with storytelling.
TRP: How can businesses go about up-skilling their existing workforce, and what should they be looking for from new talent?
JF: We could be in a situation in a few years where up to half of employees are using predictive analytics in some capacity as part of their daily routines. Up-skilling the existing workforce to meet this demand will play a part in this, as will hiring new talent which have the softer skills I've already mentioned.
But we do not all have to become data scientists. Whilst analytical skills are becoming increasingly important, and employers start to look for evidence of this on the CVs of people hoping to join their organisations, the fact is that advanced predictive analytics technology is making analytics much more accessible for the average worker.
More intuitive technology with easy-to-use interfaces that reflect the trends in consumer technology mean there is not always a requirement for specialist data science skills for individual lines of business to be able to interpret data and feed that insight back into the wider business.
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