How Field Service Management is Using Big Data


If you’re not sure what field service management does or what is at stake when a field service team isn’t performing at optimum levels, here’s a real-world example for you.

In 2009, Mike Severson, an IT specialist, was returning to his building after a late afternoon smoke break. He was planning on working later that Friday to implement some system changes after staff had left for home. If all went well, he should be done by around 6:30.

Hopping on the elevator, it abruptly jarred to a stop somewhere around the 34th floor. He pushed the alarm button several times but received no response. His cell phone was sitting on his desk far away in his office, and there seemed to be no camera in the older skyscraper’s elevator car either. No one seemed to be around to hear his cries for help through the antique brass elevator doors.

The following Monday morning, – some 44 hours later, – a severely dehydrated and understandably shaken Severson was finally discovered and thankfully rescued. He also received a significant settlement from the building owners.

How could this situation have been prevented by better field service management practices? The elevator’s broken alarm could’ve been caught by using field data to ensure service upgrades were being maintained, or stuck elevator cars could’ve been avoided by security guards ensuring they were in proper working order between shift changes.

While this tale might be an extreme example, it demonstrates what can happen when there’s a breakdown in field service management operations. Field service management (FSM) is about much more than ensuring your technicians don’t have overlaps in their service calls or scheduling a fleet of vehicles. It’s become a complex enterprise that relies on accurate, actionable data to lower liability risks and ensure customer needs are satisfied. But while collecting that data is vital, it ultimately means nothing if it isn’t being converted into useable analytics that can serve as insights into past issues and predictors of future outcomes.

Data is Nothing Without Analytics

Every day, technicians are driving to project site locations, bargaining with vendors, finishing work orders, and engaging with onsite challenges. All this activity yields data that gets collected by field techs that is then either entered into a mobile app or collected into some application.

While collecting that data is vital, it ultimately means nothing if it isn’t being converted into useable analytics that can serve as insights into past issues and predictors of future outcomes.

Data that is plugged into the right module and then built into analytic models can take all the past information gleaned from projects, contracts, suppliers, inventory, accounting, timelines, and more and churn out key performance indicators that can result in major improvements for every channel of your service model.

Consider the following: A recent study indicates that building supply companies who’ve implemented big data analytics into their field service management have established:

  • An increase in service profitability of 18%
  • A customer retention rate of 42%
  • Growth in their service level contracts of 44%
  • Bid award increases of 23%

Numbers like these tell a clear story that utilizing the predictive and preventative tools that big data analytics offer is the key to obtaining next-level success and the path forward to managing a best-in-class field service operation.

Solving Issues Before They Happen is a Game-Changer

What if you had a tool that could help you predict disruptive supply delays or discover workforce shortages before they happen? When you utilize data to create predictive analytic guideposts, you can foresee costly setbacks and prevent customer complaints before they occur.

One of the most utilized applications of big data analytics is optimizing workforce/resource management. Field service management typically needs to deploy many different techs and field resources at one time and attempting to manage that large of a workforce through traditional applications can prove to be an exercise in futility.

By deploying a plan guided by big data analytics, your field service management group can manage and track their personnel, fleet, and equipment in real time, ensuring there are no gaps in coverage or holes in productivity. Big data analytics significantly improves both the logistics and overall efficiency of a large-scale project deployment and demonstrates to the customer that you are equipped to handle any job with ease.

Another historically difficult issue big data analytics has resolved is delivery coordination conflicts. In the construction industry in particular, unplanned late or missing deliveries can mean a loss in production days, costing a fortune in operational losses. More than just providing scheduling and contemporaneous tracking, analytic models derived from big data can provide insight into possible delays and holes in the supply chain before they occur, allowing them to pivot quickly and fill the gap before a late supply delivery results in lost days of work and angry customers.

And these two examples are just the proverbial tip of the iceberg. Using big data analytics empowers FSM operations to employ the “ounce of prevention is worth a pound of cure” mentality to their overall operations. It means moving your business from reactive to proactive and gives you the power to dramatically increase your productivity, boost profitability, and build a strong operation that your customers can count on. Book a demo, today!