ABC faced two major issues when utilizing the data collected. One is the size, complexity, and disparity of the data collected would take lot of man-hours to process and evaluate. Two is real-time data cannot be put into use due to the nature of data science analysis.

Previously to working with ThingWorx, DeviceLynk built an IIoT platform but found it lacked scalability. They needed something to capture and handle data from an unlimited amount of devices and customers.

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.

IOTATOI is a division of Power Agent Systems that has developed an edge server called the Universal Monitoring System (UMS). IOTATOI is using UMS as an edge tier data collector to monitor backup battery arrays in manufacturing, mines, and chemical plants. This experience with battery backups led IOTATOI to expand the system to any condition that can be monitored by sensors such as vibration, heat, temperature, and flow. IOTATOI found they needed to move from their original middleware to something that would allow greater scalability and to also present information in a much broader way allowing them to dynamically look at the information and perform benchmarking analysis, feeding the data into analytics engines and creating a much more actionable view.

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.