We do not believe that there is such thing as an average patient, an average machine failure, or an average anything. When evolutionary biologist Stephen Jay Gould was diagnosed with cancer with an average life expectancy of 8 months, he refused to succumb to the statistics. He continued to live for 20 years and wrote 17 books. Reflecting on his experience, he said, “Variation is the hard reality, not a set of imperfect measures for a central tendency. Means and medians are the abstractions.”
The more granular data we collect, such as sensor data captured every 10 milliseconds, the more we see that the averages obfuscate much richer reality. When we adapt new analytics approaches that derive actionable insights based on individual cases, we can solve more health problems for more patients and improve operations of more machines in more diverse environments. It is our quest to deliver new approaches that are also intuitive for millions of professionals and are scalable for billions of devices.
IoT analytics can improve every aspect of business operations and yet many companies continue to refrain from adopting it. The reasons are numerous and can range from sheer volume of data, uncertain results, high upfront costs of custom software development, to complex methods of analysis.
Seeing the tremendous benefits of loT analytics, we embarked on a journey to create simpler approaches and tools for professionals who monitor processes and equipment to manage outcomes. We created a motif discovery platform to help discern time motifs and search for similar ones in order to validate intuitive choices, find and understand underlying causes, and, quickly set up monitoring to manage outcomes. Our product is intended to empower many professionals who do not have data science degrees or time for lengthy statistical data modeling. We help users to find motifs that unlock the value of time patterns and thus drive them to succeed in their industry.
In less than two years, we have built a platform that scales to millions of devices powered by IoT sensors. During this development, we tested the premise of our platform with healthcare (EU and UK National Health System – NHS), environmental monitoring (Liverpool University and Science and Technology Facilities Council – STFC) and condition based maintenance of turbine engines (Lockheed Martin for the US Navy). We were selected by the EU, STFC, and University of Liverpool to participate in funded programs in order to test the platform and concepts with real world data and use cases.