Case Study: Element Analytics achieves scalable predictive industrial analytics with Microsoft Azure

Startup offers predictive analytics to industries using scalable cloud platform

Element Analytics, a San Francisco startup, provides the Element Platform to rapidly ready industrial OT and IT data for predictive analytics. Industrial customers face a major challenge: massive, heterogeneous time‑series data from sensors, control systems, lab equipment and enterprise systems comes in many formats (discrete vs continuous) and is often messy or inconsistent, making cleaning, modeling and scalable analysis difficult — and raising concerns about moving sensitive operational data to the cloud.

Element’s solution, built on Microsoft Azure and integrated with the OSIsoft PI System, performs asset data modeling, event labeling and sensor cleansing, blends OT and IT sources, then generates machine‑learning models and visualizes actionable insights via tools like Power BI. In a deployment at a large oil and gas company (17 production units, 1.5 million sensors streaming to Azure), the platform predicted electric submersible pump failures—reducing lengthy (~30‑day) downtime, avoiding revenue loss and cutting operations and maintenance costs — while using Azure tenant isolation and open‑source toolsets to address customer data concerns.

Element Analytics

Sameer Kalwani

Founder and Vice President of Product