In an era where data drives innovation and competitiveness, many industries find themselves wrestling with a common challenge: legacy operational technology (OT) systems, siloed data, and scaling issues. These barriers hinder operational efficiency and stifle the ability to make data-driven decisions. So, what’s holding back your industrial data, and why is moving to the cloud the key to overcoming these challenges?

We have an important update regarding Microsoft Time-Series Insights (TSI) that directly impacts users relying on this service. Microsoft recently announced that TSI will be discontinued sooner than originally planned, with the shutdown date now set for July 7th. This acceleration in the shutdown timeline means that all TSI customers must swiftly migrate to alternative platforms to avoid disruptions to their operations.

We are excited about the latest Microsoft announcement on Fabric and how this aligns with our latest developments including data connectivity.

Uptake Highlights: Microsoft Digital Operations Signals IIoT Solution Spotlight

The technical integration between Fusion and Snowflake platforms offers our Microsoft Azure clients a comprehensive solution in DevOps, Data Management, Analytics, and Operational Data Management. Snowflake, a leader in Information Technology, Data Management, and Analytics, brings deep IT expertise and innovative IT products to enable organizations to speed up their digital transformation journey.

“How is your asset performance?” is probably a question that you typically get from upper management. It can be very challenging to answer for organizations that are often running in reactive mode.

For more than 20 years, companies have established strategies for collecting data from the field using traditional historians as a way to provide engineering, operations management, and even enterprise systems with the ability to access historical data from the field. Many of these systems have evolved to provide various types of data from time series to events, tag configurations to hierarchical views of the world to make things easier for clients to understand the context. The demand for this data has increased, and the acceleration that organizations have experienced in recent years is pushing the boundaries of these systems to support the various functions of industrial operations…

As a result, the pace of innovation has started to increase on many fronts to tackle the sources of GHG emissions across industries such as Iron Ore, Steel, Chemicals, Oil & Gas, and Transportation. They require massive capital projects as it relates to complex changes in the design of process equipment and overall industrial processes and manufacturing. While all of that gets resolved, the question is, what can we do now?

Self-serve analytics empowers users to access and analyze data independently without needing specialized personnel or software. In this blog post, I'll explore the benefits of self-serve analytics for the industrial process control industry, including improved decision-making, increased efficiency, reduced costs, and more.

The Knowns-Unknowns approach consists of mapping what is already known to build up the baseline knowledge about the specific operations and assets, e.g., critical assets, failure modes, tasks, and activities, including frequency of inspections and type of inspections or online condition-based monitoring applications.