The following is an excerpt from our Enriching Data, Empowering Action whitepaper. You can access the full report here.
Speeding up the workflow of collecting, processing, and analyzing data—without sacrificing the quality of the collected data—helps utilities make fast, accurate decisions on time-sensitive issues.
The Safe, Cost-Effective Way to Speed Up Collection
PrecisionHawk is a collection-neutral solution—meaning it can process and analyze data no matter where it originates from, from manual methods to satellites. For this reason, PrecisionHawk recommends the data collection modality that best fits the mission based on the timeline, required resolution, safety risks, and cost per unit (for example, per structure, per mile, or per acre).
Unlocking Insights Through Data Processing
Many utilities have vast amounts of existing asset data, and few effective means to process it—meaning all the key insights are locked away inside the database.
If the data wasn’t collected well or is corrupted, the only solution may be to recollect. As you’ll see in the storm hardening case study, PrecisionHawk has led recollections for utilities whose data was low quality. However, if the data is clean and accurate, PrecisionHawk can develop software scripts and workflows that will automate the processing.
This creates efficiencies that speed up analysis and lower costs. Software scripts handle repetitive tasks that don't require subject matter experts, allowing utilities to reallocate their human resources to more valuable tasks. Now, instead of having a team of analysts scrolling through, say, 10,000 images to pinpoint potential issues, the utility can train a few analysts to analyze the 500 images that are flagged for review.
Faster Analysis and Higher Accuracy Through Machine Learning Models
Speeding up analysis without compromising accuracy may seem like an impossibility—but because PrecisionHawk’s machine learning models quickly narrow down the number of images to be reviewed, analysts are able to focus on only the most relevant images.
Not only that, machine learning algorithms are self-learning, meaning they become more accurate the more data they’re exposed to. And their standards are not biased by human error or perspective, making them even more precise.
The speed and accuracy of machine learning and the expertise of trained analysts combine to create the fastest, most precise data processing workflow.
CUTTING LOSSES, SAVING TIME
While machine learning and trained analysts can speed up the processing of vast amounts of data, utilities need to be cognizant of the point of diminishing returns. PrecisionHawk conducts continual process reviews to be sure utilities don’t hit that point: the team reviews past projects, creates a process workflow, monitors successes and challenges during projects, collects feedback from clients and analysts, and makes improvements based on all these inputs and results.
In some cases, PrecisionHawk discovers that it will take more time to optimize or upgrade a segment of a utility’s workflow than it will save the analyst in the long run. When this happens, in the interest of efficiency, the team instead focuses on an area of the workflow where they’re more likely to attain measurable improvements.
Data that Drives Results
Accelerating, automating, and amplifying data helps utilities collect the vast amounts of data needed to ensure accuracy and usefulness, because AI and machine learning can filter that data down to the most relevant information. The balance of automation and human intervention makes the process cost effective, as well.
With the right data, utility personnel can now make the best decisions to increase safety, customer satisfaction, and cost savings—decisions that will benefit the utility today and tomorrow.
The bottom line: by deploying PrecisionHawk’s data solution, enterprises can improve every step of their inspection process—from collecting data to deploying repair crews.