We loved the idea of using the new location feature of Appian 19.3. Location contextualization has been such a big part of the rise of mobile devices, and we felt that pulling location data into Appian processes gave us the opportunity to do really novel things with the platform. Princeton Blue's work within the Pharma and Compliance spaces has taught us a lot about how large companies manage their people and facilities, so workplace safety felt like a natural target for a novel application. With around 6,000 workplace deaths and 6 million workplace injuries per year in the US alone, we felt that bringing location contextualization to the problem with Appian could make a big difference.
What it does
Once set up with facilities, devices, and policies by the organization, all the power is in the hands of the user. Users report to a facility by simply clicking a button on their mobile device, which sends their location to Appian and to myriad location-based IoT systems. Each systems sends back any relevant information about that location, and Appian combines it all with information from decision objects about the facility to dynamically create a list of policies and procedures to display to the user. The new Google Translate connected system of Appian 19.4 is leveraged to render the list in the user's native language, and a signature is digitally captured and saved to the audit log. Finally, with the formalities out of the way, the user is free to browse all of the data themselves so they know everything about the conditions they're about to walk into. The focus throughout is on improving the safety of both the user and the organization, by providing better information to the user and the ability to prove compliance at a far higher granularity to the organization.
Challenges we ran into
rIoT is a real-time, location-based platform, so during testing we had a very challenging time testing various scenarios with different data inputs. Certain types of information, weather alerts for example, are only available at certain times, so final debugging had to be timed to coincide with real-world weather events. Furthermore, some systems also change the information they return based on where in the country the user is, and so rules and interfaces had to be written to take into account the dynamic nature of the API data.
Accomplishments that we're proud of
Our team is proud of making a difference in people's lives, by bringing technology and the human experience together to make a better world. The inclusion of real-world data in this application makes it unusually clear to us as developers what impact it could possibly have. It's very easy to envision a plant-worker checking their phone before entering a building, seeing a warning about high VOC levels, and putting on a mask he or she otherwise might not have. Working on rIoT made us feel like we might actually be making a difference in someone's life. And we're really proud of that.
What's next for rIoT
The easiest way to expand rIoT is to add additional sources of data. We plan to add sources in the future, but any organization that picks up the app can add data by simply adding a connected system and integration. Background radiation data and solar irradiance are two data points on our radar, and both could be very meaningful depending on the industry. We'd also like to take the data framework we've built and go in a slightly different direction. Instead of letting the user query one facility at a time, we'd like to create a live view of the aggregate data of all facilities together for a 'facility supervisor' role. The amount of real-time data to which we're connected could allow us to provide unique insights on the status of the organization as a whole.