As companies capture more data through mobile devices, remote sensors and telematics, the opportunity to increase the efficiency of field service operations has never been better argues coresystems CEO Phillip Emmenegger... 

This year, at tennis’ US Open, we got a glimpse of just how pervasive the idea has become of big data’s transformative power.

Thanks to cameras and remote sensors trained on the every move of the tennis players, we learned from the estimated 41 million data points collected how important it is for tennis players to return the ball to their opponent in the least amount of moves. Or, as the new “must-know” tennis stat that tracks the average distance a player travels on the court for each point is called, “Feet (meter) per point”.

It’s obvious that it’s better to expend less energy chasing down a ball, but now there’s tangible proof. The two men’s players to make the finals, Marin Cilic and Kei Nishikori, both despite their different styles of play, racked up the least amount of distance per point. Cilic averaged 42 feet per point; Nishikori, 52. In contrast, Roger Federer came in at 57 feet per point, and Andy Murray at 70 feet.

But what does the data crunching at the US Open have to do with field service? Just as in tennis, so in field service: those with the most efficient delivery win. Moreover, just as collecting the seemingly mundane data of the average distance a player ran per point is now being used by tennis coaches to re-examine their player’s games, so a company can now collect the most specific data to be transformed into meaningful analytics to eventually give companies an edge.

Moreover, providing superior customer service has been show to impact a company’s bottom line. According to business consultancy the Aberdeen Group in its recent study, “Secrets to Optimize Field Service for Better Customer Experience,” those companies that hit a 90% + customer satisfaction were also able to achieve an annual 6.1% growth in service revenue, a 3.7% growth in overall revenue, and even more importantly, an 89% current level of customer retention.

But how can field service companies harness big data? And, what exactly is “big data”? Big data – as opposed to just data – usually refers to the entire process of capturing, storing, managing and analyzing massive amounts of various types of data, according to the Aberdeen Group. Typically, the amounts are in the terabytes or petabytes, stored in multiple formats, from both internal and external sources, and with strict demands for speed and complexity of analysis.

Here are three ways your company can get started:

Collect and Capture Data: Within field service, there are a number of ways big data can be used. The first place to start is to assess the data that your organisation already collects. Using a field service software solution will allow you to collect data quicker and more easily. A field engineer using a tablet-based solution could seamlessly collect the time the technician was dispatched, the time to fix the job, whether or not the job was fixed the first time, if a technician had to return, what reason they had to go back, and what part was needed. What other data does your company collect? Is there fleet management of telematics data available? Would placing remote sensors at certain locations to collect data make sense?

The next place to look is across departments in your company. Could shared data from different departments lead to more efficiencies or opportunities? For example, could data shared between the field service department and the sales department end in more sales opportunities? Managers could also see if one particular area has a higher need for service technicians, and hire accordingly.

Predictive Analysis: More exciting examples could happen when companies get more sophisticated with remote sensors and machine-to-machine communications. The term “predictive analytics” is usually spoken of in marketing as the ability to track the behaviour of prospective buyers to help sellers understand the right time to engage them. But now we’re beginning to hear the term “predictive maintenance”, referring to the ability to foresee when a part or piece of equipment is near the end of its life span. With remote sensors, it would be possible to begin gathering data on whether a particular part is more prone to breaking down by monitoring a machine’s changes over time; or within a certain use case, and what its average life span is. The data could help predict the best time to schedule a pre-emptive service call.

It’s no secret that customer demand for better service has increased and that its success now contributes or takes away from a company’s bottom line. Ironically, as technology helps organizations deliver better customer service, the expectation for even better service just keeps rising. It’s up to companies to use every single tool they have, including the most minute details, to turn them into their own competitive advantage.