Our app ties data from wearables to workplace performance (sales, sick days, attrition, etc.) and allows managers to deliver the right interventions to the right groups at the right time.
Although the app can be deployed across all functional areas of an organization, we’ve started with the sales department since sales metrics are the easiest performance measure to capture.
Details: Wearables are becoming ubiquitous---22% of people now own a wearable device that tracks health measures and 50% of people plan to buy one by the end of 2015.
Personal preference dictates what type of wearable is purchased, so effective solutions must support multiple types of wearables. Further, to eliminate privacy concerns as a barrier for adoption, individual data will be linked to a specific group, de-identified and then aggregated for analysis. At no time will an individual’s data be identified or collected. Our solution employs Validic—an API and platform that collects data for 150 types of wearable devices.
Force.com pulls data from Validic, and then is deployed on Heroku as data visualizations, analytics and a recommender system. The latter two are driven by a machine learning algorithm (K-means) coded in Python, and queried and modeled (affinity algorithm) by Neo4j, the leading graph database.
Health score index = sleep (duration, times woken) + activity (steps) + resting heart rate
Performance score index = sales + 30-day sick + 30-day attrition rate
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