One of the essential problems involved in managing large datasets is ensuring they’re complete. Many use cases, including materials databases, can use machine learning (ML) and artificial intelligence (AI) algorithms to accurately identify and fill gaps with data extrapolated from other data in the set. The datasets might contain time series data which, for example, may track the movement of components through a supply chain and/or static data like a parts inventory or test results. Altair’s data science tools are well suited to this task.

Serba Dinamik is an engineering company specializing in operations and maintenance (O&M), engineering, procurement, construction and commissioning (EPCC), and IT solutions for energy exploration and production firms. Their team worked with Altair to develop a Smart Predictive Maintenance Data System (SPMDS) utilizing Knowledge Studio and Panopticon. Maintenance crews use Panopticon-powered dashboards built into SPMDS to monitor every sensor mounted on operating turbines in real time. AI models built with Knowledge Studio identify potential failures or issues that require engineering attention, and, based on that understanding, take turbines offline only when necessary.

In a world where everything is becoming more and more connected, Mabe, a leader in home appliances, is leveraging the convergence of big data, analytics and simulation to accelerate innovation. Martin Ortega, Senior Design Engineer at Mabe, explains how they are using Altair’s AI, data analytics and simulation solutions to uncover insights, create new business opportunities, and advance product development.

The Electric Storage Company is a Northern Ireland-based firm that manages electric power in households from renewable sources using battery storage and Internet of Things (IoT) technologies. The company installs smart batteries in homes and communities, along with sophisticated management software that lets homeowners sell excess energy back to grid operators when prices are high and helps them maintain the lowest possible energy input costs.