This partnership brings together Uptake Fusion, which collects, moves, organizes, and curates data in Microsoft Azure to power advanced industrial analytics and asset performance management, with the industry consulting, systems integration, and application services of Cognizant.
“Uptake Fusion raises the value of the investments industrial operators have made in their existing operational technology (OT) data and automation systems. It cost-effectively allows companies to set up data lakes, in addition to enterprise-wide asset monitoring, automated reporting, and digital twins,” shared David Cox, Assistant Vice President and Consulting Lead, Energy and Utilities, Cognizant. “With Uptake Fusion, Cognizant strengthens the ability of our customers to realize the value of data intelligence sooner and at scale.”
In their enterprise cloud environment, industrial companies can use Uptake Fusion to provide internal and third-party data consumers with the data needed for industrial intelligence. Users can leverage its open APIs as plug-ins with existing non-proprietary tools such as Microsoft Power BI, PowerApps, and Azure Time Series Insights for dashboards, reporting, and monitoring.
“Process companies have their personnel and technical challenges with the availability of industrial intelligence for their maintenance, reliability, financial, and operational teams,” said Kayne Grau, CEO, Uptake. “Uptake’s partnership with Cognizant widens and accelerates access to Industrial AI while also curating and strengthening the integrity of OT data for broad enterprise use.”
Uptake Fusion connects to underlying OT systems that contain time-series data – including but not limited to OSIsoft PI System, Rockwell FactoryTalk, Inductive Automation Ignition, among other SCADA, historian, and IIoT sensors –– to cost-effectively enable advanced analytics applications.
Through secure data movement, storage orchestration, and curation with other enterprise data sets such as SAP, IBM, and Oracle, Uptake provides a rapid, scalable approach to cost-effectively unlock the pent-up demand of AI-enabled industrial intelligence.