Analysing big data has been on the tip of many a technologist's tongue for the past couple of years.
This analysis is described as the future for enterprises looking to gain insights into business operations and find patterns between sales and marketing activity against revenue.
Many organisations have used it to good effect. Camden Council uses IBM big data analytics to create a database that consolidates residents' data to reduce fraud and costs, while Expedia consumes big data to better understand what its customers are buying.
Open source frameworks like Hadoop make the storage of data more cost effective and, with numerous analytics tools on offer, the promised big data future is here.
But it is set to change. Much of the analysis of large data sets is currently a process of looking at what is happening or has happened across an organisation.
This data is analysed into insightful information that highlights sales opportunities or problems in a supply or manufacturing chain.
This is often used to make an organisation more effective, but cloud computing, machine learning and in-memory technologies are creating the foundations for a big data future where looking forward is the objective.
Crystal ball analytics
Predictive analytics is set to be the next trend in big data. Rather than react to insights gained through data analysis, enterprises will use a combination of real-time, historical and third-party data to build forecasts of what will happen in their business months, weeks or even just hours in advance.
Such an approach allows action to be taken to avoid predicted problems, such as equipment failure or depleted stock, or to capitalise on opportunities to market products to customers, such as targeting people in euphoric or deflated moods after a sporting event.
Forrester analysts Rowan Curran and Mike Gualtieri believe that predictive analytics have never been more relevant and easier to use, and offer ways for forward-thinking enterprises to succeed in competitive sectors.
"Big data, gobs of compute power, and modern tools are making predictive models more efficient, accurate and accessible to enterprises," they wrote in a Forrester Wave research paper entitled Big Data Predictive Analytics Solutions, Q2 2015 (PDF).
"Why do it? Because enterprises that predict will win, retain and serve customers better than those that don't."
However, the analysts added the caveat that predictive analytics needs to be used in a way that prompts actions rather than just forecasts.
"Predictive analytics has limited value unless the exposed insights can be deployed directly into software applications and business processes," they wrote.
Capturing customer attention
Customer relationship management (CRM) software is one major area where predictive analytics can be very valuable.
Sales people and marketers can use predictive analytics to forecast the impact of their activity and provide more personalised pitches or content to individual customers.
This would make them more effective than relying on historical data on previous interactions.
Larry Augustin, chief executive at SugarCRM, told V3 that his company is adding predictive analytics into CRM software.
"We see [CRM] bringing a lot of intelligence into the system driven by the access to data and information that's available today," he said, noting that sales representatives can gain insights about when to approach potential customers.
"I think there's a lot of opportunity to add action and intelligence into systems that in the legacy world have been primarily databases," he said.
"That technology already exists, but the trick is how you use it and tune it for the kind of questions you come up with that result in an action."
Augustin explained that CRM is moving away from "systems of record" to "systems of engagement" that use predictive analytics to cut through the noise in big data and uncover insights that can be acted on.
"It's all about activity, not the recording of data," he added.
Machine-learning and analytics technology specialist Inside Sales is one company pursuing the concept of not simply analysing big data but using it to make predictions, with a cloud-based predictive analytics service operating at a 'hyper scale'.
The system takes CRM data from platforms such as Salesforce and Microsoft Dynamics, and analyses it against over 100 billion anonymised sales interactions taken from across its global customer base.
This allows greatly expanded and enhanced predictive analytics, as a company's internal data is compared against an aggregated mass of data.
In practice, this allows a company to see how it is performing against larger regional and global sales trends in its sector and understand how influences such as current events, economic factors and even the weather have affected its sales pipeline.
Dave Elkington, founder and chief executive of Inside Sales, told V3 that this kind of analytics is the next big evolution in big data and cloud computing.
"[Software-as-a-service] begged the need for the big data revolution. We're in the next phase of that evolution which is the predictive revolution," he said. "It's not about big data, it's what you do with that big data."
Inside Sales is effectively turning its service into a platform for companies to deploy its Neuralytics self-learning predictive engine beyond just predicting how best to carry out tailored sales and marketing activities.
The firm's Predictive Cloud allows companies to build their own apps on top of it in similar way to the Salesforce1 CRM development platform.
This allows Neuralytics to be used in a wider way. Companies like Coca-Cola can use it to forecast required levels of stock against predicted sales volumes and ensure they do not have vast amounts of product sitting around in storage.
Curran approves of the early adopters of predictive analytics, and warned those that are sitting on the side-lines to pay attention to the more forward-thinking companies.
"We're happy to see some vendors already moving in this direction, but others will need to act soon to avoid leaving a capability that is fast becoming table-stakes to third parties," he said.
Enterprises could follow the example set by the Met Office, which uses predictive analytics for weather forecasting, and Gatwick Airport, which uses it to manage airport activity.
With a few compelling uses cases bubbling up to the surface, it is clear that taking a crystal ball rather than reflective approach to big data is the future for technology-savvy enterprises not content to keep looking behind them.
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