Big data has become one of the go-to buzzwords for ambitious startups and breathless tech reporters, a stand-in for just about any type of intelligence-derived insight delivered by a computer. In reality, big data is an increasingly specialized and targeted part of the IT toolbox—not the cure-all it’s often pitched as but a vaccine to be deployed with specific goals in mind.
When used properly, big data analysis provides so much insight it earns another name entirely: predictive analytics. This is an even more specialized area, with potentially even greater returns—if you know how and where it works best.
Predict the unpredictable
Predictive analytics is the discipline of quantifying past behaviour to gain insight into the most likely dynamics of that same behaviour in the future. It begins with robust data collection, either by setting up your processes to harvest the customer, employee, product, or other information you need, or by simply purchasing it from a third-party specialist who’s collected the information on their own. Then, analysts use sophisticated mathematical models to find hidden trends within this data—and the effects of these analyses can prove enormous.
Take, for instance, the case of Orbitz.com, which found that users browsing their site on MacOS were 40 percent more likely to book a four- or five-star hotel than Windows users. This allowed Orbitz to tailor its search results based on the prediction that Mac users would be more interested in luxury hotels than their PC-using peers. This prediction lined up well with reality, and a rather simple change in a hotel suggestion algorithm drummed up significant new revenue and satisfied customers with shorter search times.
Big data + automation = IT overpowered
Driving the potential of this phenomenon is the capacity to feed data into automated systems. When data mining is reliable, you can trust it to produce insights that guide algorithmic management processes. IT security, in particular, has never been the same since the advent of semi-automated tech that bases its security decisions on real-time analytics of network activity. However, the abilities of big data are now so widely useful they’re becoming indispensable even beyond highly technical disciplines, like cybersecurity.
A sufficiently empowered system of predictive analytics can notice complex trends in customer behaviour and quickly adjust suggested store content to match their changing tastes. Another system could look at the current level of scheduled overtime and automatically order a new batch of coffee beans for the office to fuel workers through a rough season. Some of the most innovative work in IT today has to do with the integration of predictive analytics and the office’s most old-school devices. Though it might seem incongruous to match such a bleeding-edge tool to legacy systems, the confluence of new and old can widen bottlenecks you never even knew where there.
Big data and printing are becoming inseparable
Modern analytics can start to alleviate some of the most intractable problems in IT—and when IT professionals think about intractable problems, there are few office devices that spring to mind more readily than the printer. Not only does it create new scheduling requirements for paper, toner, and more, but lower-end printers and print services often come with shoddy software or buggy network code that can lead to serial complaints and, thus, serial headaches.
That changes with devices that work with advanced, predictive systems integrating big data and printing. Any full-featured print service worth investing in now includes advanced predictive technology that can rapidly assess and repair issues as they arise, before they cause backups. This type of advanced tech can also notice conflicts between print tasks and scheduled (or easily predicted) downtime and patterns in connectivity issues that could result in lost data.
Even something as simple as a depleted toner cartridge can be a drain on the schedule. A system that can see such an event coming and automatically schedule the appropriate maintenance, however, eliminates that drain. With analytics, that type of service is taken out of your hands, allowing you to spend less time worrying about printers and more time worrying about the business.
Get back to the big picture
The term big data may get abused in the culture at large, but it has specific applications in the IT world—and many of these applications come in the form of predictive analytics. The predictions made possible by big data analytics can save time for existing IT workers, thus freeing them to focus on the innovative projects that had previously been too time-consuming to consider.
By integrating big data and predictive analytics with IT management, you can get back to the kind of big-picture, process-level improvements that were always supposed to define the impact of IT in the first place.