Problems Data Can Solve For Utilities: Inaccurate Inventory

The following is an excerpt from our 5 Problems Data Can Solve for Utilities (And 1 Problem It Can’t Solve) ebook. You can access the full report here.

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THE Problem

Do you know how many poles you have...what’s on them...and exactly where they’re located?

Chances are the answer is “no.” In its work with one electric utility client, PrecisionHawk discovered that 7% of the assets the utility thought were in the field were not in the field, 40% of poles were located more than five feet away from where the database had them located, and 10% percent of the assets the utility thought were removed had not been removed.

In an analysis of a different utility client, PrecisionHawk found more than 17,000 pieces of inaccurate data in its database, including 500 poles that were not in the database at all.

Why is this happening? “Traditional methods of data collection are very antiquated,” says Patrick Mills, Director of Data Services at PrecisionHawk. “The first 20 poles may be accurate, but by pole 50 the line workers get tired.” For utilities with thousands of assets, data quality that deteriorates down the line can be a big problem.

One problem with not having a handle on your assets? Unhappy customers. “The information going from the team in the office to the team in the field isn't always accurate, and this impacts how quickly the utility can fix the lines,” says Mills. “The utility workers have to spend time trying to figure out where the switch going into this neighborhood is or where that circuit ends.” The longer it takes to get the right information, the longer it takes to fix the problem—increasing the risk of customer complaints.

Inaccurate inventory is also a safety issue. For example, if the GIS data doesn't reflect the proper phase of a line, a line worker wiring a piece of equipment might connect it to the wrong phase, causing a failure in the circuit.

THE Data Fix

If the data you have is incomplete, inaccurate, or difficult to view, there are solutions available that can save the day. 

The first step is to become “collection agnostic,” meaning that instead of relying on just one type of collection—such as ground teams or drones—you build a suite of collection methodologies that fit your utility. For example, you might use the traditional ground-based method for some situations and turn to drone-, satellite-, or helicopter-based collection methods for those areas that are unsafe for ground teams.

Another thing that’s agnostic is the actual cameras and sensors used to capture images, which helps increase the quality of your data by getting clear images down to the tiniest component. For example, you might use a combination of RGB, stereo, and LiDAR cameras depending on the situation.

The next step is to integrate the different types of data from the various collection methods and image capture equipment into a holistic view of the entire system and present it in robust but easy-to-read reports. Thankfully, geospatial data acquisition and analysis solutions that employ machine learning (ML) and artificial intelligence (AI) can process, analyze, and report on data from any source. This type of solution lets you collect more data, and do it more quickly and accurately—and better, faster data leads to better, faster action.

Download our ebook, 5 Problems Data Can Solve for Utilities (And 1 Problem It Can’t Solve), to learn how you can improve your utility with data.

Download the Ebook