So how much data is out there? Hard to know exactly, but consider this: as of 2012, there were an estimated 2.8 zettabytes (or approximately 2.8 trillion gigabytes) of global data! And that number is expected to increase by 2,000% by 2020! Now granted, most of this data may be meaningless to your company. Some estimates indicate that only 3% of all available data has actually ever been used, so being able to decipher what is meaningless from what is important can make the difference between success and stagnation for many companies.
Marketers understand how important big data can be to their efforts. From social media to web visits to phone calls to transactional events to GPS coordinates, and more, information is constantly being generated. For many years, smart marketers, especially those in the direct marketing industry, have used practices such as data mining, modeling, and statistics to “predict” future problems or anticipated customer behavior. Of course, in the pre-cloud era, data was flowing in at a controllable pace, from a finite group of sources. So how has big data both complicated and helped decision-makers?
Gartner’s definition of data may put this entire issue into perspective, “Big data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.”
So, new forms of processing means new analytical tools are needed; these tools will allow analysts and executives to have the information they need to make informed decisions based on reality rather than gut instinct. But, prior to that, it’s necessary to ascertain precisely what you need these analytics for. If you’re a retailer, that could be identifying customer demand. For transportations companies, it could be detecting shipping or supply chain operational issues to avoid delays and minimize costs. Once the parameters you’re looking for are determined, it’s much easier to streamline the data you need to make these analyses.
An article that appeared in the July 7 issue of fleetowner.com specifically deals with these two issues: big data and predictive analytics. In the commercial truck industry, where new truck technology is generating data constantly, it is vital to deploy this data to get ahead of maintenance e-related issues. So, predictive analytics is really about predictive maintenance. With numerous vehicles on the road, the ability to access the data coming from these vehicles can enable a company to detect maintenance issues before they result in a breakdown or worse. And by dealing with these issues earlier, a company can reduce costs and increase efficiencies. But the article also states that the data is meaningless unless it’s acted upon; so fleet managers and owners need to keep their eyes on the incoming data.
The article quotes Brad Stinson, VP of engineering for Stemco, as he talks about the fact that managing data is much more of an issue than merely gathering that data when it comes to predictive analytics. “The hurdles are not with the hardware [as] the technology is available. The real issue is with the processing and delivery of the information in a clear and actionable format where fleet s will be able to readily act on the data.”
The reality is this: big data is here to stay. You either adapt by learning how to access the information you need to make strategic business decisions, or you go the way of the dodo, the giant sloth, and the wooly mammoth. That is not a path most companies would choose to make.
If your company is currently a big data user, what are the benefits you’ve seen? Has it precipitated moves that might not have occurred otherwise? Let us know.