By Nathan Jones, Technology Consultant
When I was a young man attending college, I would often find myself driving my old ‘89 Honda Accord down I-25 late at night going to and from Fort Collins, Colorado. One particular evening a youthful lack of discretion came over me, and I decided to briefly drive a stretch of the highway without any headlights on. Traveling at 85 MPH through the pitch-black night illustrated to me just how vulnerable you can be if you aren’t able (or willing) to monitor the surroundings. One bump in the road, one wandering deer, or one piece of roadside equipment is all it would have taken for disaster to strike. Luckily for everyone, my foolish stunt passed without incident. Flash-forward to present day, things have only gotten more complicated in the business of dangerous driving. The blessing/curse of the smartphone has taught us that we don’t need to be driving down the highway at night to be an incredible threat to those around us, just a person casually glancing down while driving. We rely on constant feedback from the world around us in order to function safely and effectively, ignoring it is at best foolish and at worst deadly. Managing a UVM program can be a bit like driving that old Honda Accord down the highway at night – if you keep your eyes on the road and your lights on, you can navigate the twists and turns reliably; look down at your phone or drive with the lights turned off altogether and you could end up in the ditch or worse.
The UVM environment is extremely data-rich, with information constantly flowing in from utility personnel, contractors and customers. Having possession of this massive amount of data is only half of the battle; the other half is interpreting it and putting it to good use. Similar to the driver who ignores the warning signs as they drive down the highway, a UVM program ignores data trends and analytics at their peril. What the numbers are is the easy part – it is fairly simple to acquire statistics regarding the number of trees pruned, amount of herbicide used, cost per tree, etc. The why of the numbers can be the more difficult (and more important) piece of the puzzle. Why does this particular feeder experience 35% more outages on average than the others? Why is a particular species of tree all of a sudden failing below eye level? Why has the inventory of 24-36- inch elms increased by at least 50% each cycle for the last 3 cycles? Why does the speed limit drop from 75 to 45 in this particular location? The speed limit drops because there is a sharp curve ahead that requires a slower speed to navigate safely. Warning signs are all around us, and a savvy vegetation manager will learn to read the signs and steer the program appropriately.
Every utility is different, and every UVM program is similarly different. There certainly is no template for running a successful UVM program – that’s where data analytics comes in. The beauty of harvesting the bounty of data available in the field is that you can see precisely what is happening on your system and make real-time decisions based on the data that best suits you. A deep dive into your data can allow you to fine-tune your cycle lengths, track and adjust removal rates to focus on problem trees, monitor crew production based on a variety of metrics, accurately track herbicide usage, see if the budget is on track based on projections, and so much more. The raw data is all out there, all you have to do is reach out and grab it. Once you have it, a quality software system will make short work of crunching the numbers and present the acquired data in a meaningful and easy to interpret manner, so you can act quickly and intelligently move the program in the right direction.
Making the most informed decisions possible in any business can be the difference between success and failure, and the UVM sector is no different. With modern data collection methods and specialized analytical tools, collecting and analyzing field data has never been easier than it is today. However, acquiring and analyzing the data is just one piece of the puzzle. Once the data has been acquired, it’s up to the vegetation management team to take that analysis and apply the findings for the betterment of the system. Just like driving a car down the highway, a savvy vegetation manager must take the constant stream of incoming data, interpret in a way that is most appropriate for their specific program, and steer the program in the safest possible direction. Of course, hitting a few potholes is inevitable, but a good driver will take lessons learned in the past and apply them to future obstacles when they present themselves. And contrary to what the younger version of me would say, the “check engine” light is not there just for decoration.