For most industrial site operators and security team members, it’s easy to see the value of having a live “eye in the sky” on-site to assist in rapidly gaining situational awareness in times of crisis. Many drone-in-the-box systems are deployed primarily with a focus on scenarios like replacing risky, costly inspections in complex environments. Since teams see enormous value from these autonomous drone systems just by reacting to real-time findings, sometimes historical data isn’t given proper consideration. Since there are times when historical data’s value might not be seen until months, or even years, after deployment, it has become the long tail for drone-in-a-box systems.
Despite the time it takes to see its value, this historical data is often the highest ROI-providing component of a drone-in-a-box system because the GPS-based, time-stamped, secure data showcases an exact history of what is and what has been happening on an industrial site. This precise compilation of data becomes evidence that is often critical in resolving complex auditing, insurance, and vendor disputes that can cost companies hundreds of thousands to millions of dollars. Moreover, when applying machine learning, historical data can be used to find anomalies that serve as early markers for failures, and prevent costly maintenance and even business disruption.
What is historical data and how does Percepto’s solution provide it to users?
In the context of Percepto’s solution, historical data is any aerial photo, video or sensor data captured regularly at set points in time by our autonomous drone solution, which is then saved to Percepto’s secure cloud management system.
This data can be easily scrolled through from date to date for comparison and viewed by administrators and system users on-demand. Our platform allows users to check an exact day to look at site progress or anomalies, dating all the way back to the first flight ever conducted by the system. Over time, this data provides a historical reference of a site’s history – what has changed, if personnel are following protocol, if vendors made deliveries on time, and even the state of assets for insurance documentation.
The true power of historical data is unlocked by application of Machine Learning to the data acquired by the drone system over time. Anomalies can be detected autonomously through the power of Machine Learning algorithms, which doesn’t just detect risks rapidly and effectively, but also saves teams enormous amounts of time–there is no need to go back and review hours upon hours of captured video and data feeds.
For companies focused on digitization, or those whose auditing and office teams are often faced with complex claims from insurance providers, vendors or internal reviews, this data is incredibly useful. Exact, dated, location-specific visual context is a fantastic record that is useful for everything from an internal review or audit to helping resolve a court dispute and beyond. With Percepto’s autonomous drone solution, this data capture happens automatically – regardless of site conditions – thanks to our weatherproof hardware that allows flight in rain or snow and our automatic flight planning software that schedules flights on specific or randomized rhythms.
The value of historical data is evident in our clients’ results. Whether looked at by a team upon autonomous detection of an anomaly followed by an automatic dissemination of a related report to inform decision-making, or used a year later in an audit review to understand site conditions, this data has helped our clients save millions of dollars in claims processes and added efficiencies.