Bit Stew Systems, From GE Digital, provides the premier platform for handling complex data integration, data analysis, and predictive automation for connected devices on the Industrial Internet of Things (IIoT). Purpose-built for the IIoT, Bit Stew’s MIx Core™ data intelligence platform solves the data integration challenge at scale for complex industrial data environments. Bit Stew has earned global recognition by being named to Gartner’s Cool Vendors List and as Frost & Sullivan’s Entrepreneurial Company of the Year – North American Service Solutions. In 2015, Bit Stew was ranked as one of the Top 100 Analytics Companies and Top 100 IoT Startups by Forbes.
Incorporated in 2009, Bit Stew is a private company that is headquartered in Canada with offices in the USA, Australia and Europe.
Data Integration at Industrial Scale: Data integration is the Achilles’ heel for industrial organizations. 80% of data analytics project cost is associated with data integration and of those projects 50% fail. These projects often rely on traditional ETL or BI tools administered by large integration teams.
Machine Intelligence: Automate complex IIoT data management with machine intelligence by creating relationships across your industrial data regardless of format, frequency, and type. MIx Core applies schema to data as its ingested in order to speed integration. An optimized NoSQL architecture and ability to dynamically adapt to sources as they come online allows you to ingest and integrate all your data 6x faster than traditional ETL.
Edge: MIx Core enables edge processing through automated data ingestion, modeling, and mapping capabilities. The platform ingests data directly from equipment sensors without the need for extensive API libraries. A federated architecture supports analytics from control systems to the cloud, allowing the application of analytics to data while in motion. This enables real-time access to actionable insights, in a local context, to vastly improve operational agility.
Capabilities: Analytic ensembles utilize machine learning algorithms to create context, learn patterns, detect anomalies, and store new intelligence into MIx Core’s knowledge repository