Embedded Energy Technology (EET) wanted to build a dashboard for their customers to visualize the data analytics for the stream systems. They wanted the readings collected from the sensors to provide a complete picture of what is happening inside the component, and are displayed in a Web Portal that analyzes and presents the data in a concise format. The dashboard interface will also let customers set up email and text message alerts to be triggered when temperatures of the sensors customer select fall outside of the range they set. Summary reports are emailed monthly that highlight total energy savings, component health and month-over-month / year-over-year comparisons.

Before developing an IoT solution, ICURO hit a major roadblock with its healthcare platform. Limited data sharing between healthcare providers and insurance companies impeded effective communication, resulting in frequent duplication of efforts and making it difficult for each entity to deliver better care while maintaining reasonable costs. ICURO wanted to resolve these data sharing issues, they started developing tools and systems that connect products in meaningful ways, enabling both entities to collaborate more effectively.

In 2013, LumenData found itself in need of adding new real-time predictive analytics capabilities to its suite of services. To meet this need, LumenData acquired a state-of-the-art streaming data, capture and real-time predictive analytics company. This solved the pure predictive analytics end, but left LumenData with a need to be able to build IoT-targeted services.From an IoT perspective, LumenData was still missing the means to create suitable applications and dashboards that would make it easy for its customers to effortlessly make sense of whatever predictive analysis they might require.

The client had limited or inefficient integration of its data sources, which made it difficult to see patients through a longitudinal lens. The client was, however, uniquely positioned to leverage the expansive patient data contained within its network of care, and set out to do so in 2012. Specifically, they wanted to improve the outcomes of patients with Ischemic Heart Disease (IHD) through improved care management with goals of reducing readmission rates, better managing patient cholesterol levels, and better managing patient blood pressure. Specifically, the regional healthcare provider was interested in implementing a machine learning platform, that quickly automates complex analytical processes and integrates powerful information into existing applications and portals.