Executives at software companies have been explaining their roles in “IT/OT Convergence” for at least 20 years. I hope to lead a banning of that term. It’s actually a bit of nonsense.

Permit me an analogy. Sometimes in a soccer match a hard foul occurs. Suddenly opposing players are in each other’s faces. The referee pops in the middle and has a word (or more). Often the situation ends with the players grudgingly slapping hands in a bit of a handshake, and the game continues.

I think that some software supplier executives, both on the operations side and on the enterprise side, thought they would play the role of that referee, bringing the OT and IT teams together. It was never going to happen this way. Neither would it happen by various management efforts to merge the departments. Each team has a critical, but different, role in a manufacturing organization. What concerns us is the intersection of these roles.

This intersection lies in the realm of data. OT requires data from the processes, assets, materials to fulfil its role of efficiently and profitably producing products. IT requires much of the same data for its enterprise management requirements. This is where we should look.

I am beginning to interview people at companies tackling the issue from this point-of-view. Enter Element Analytics and an interview I recently had under the cover of the ARC Industry Forum with Steve Beamer, VP.

The essential focus of Element Analytics is data connection. It uses drag & drop coding, is source agnostic, and utilizes knowledge graph model. Beamer  says Element Analytics is tackling one of the most critical gaps in Industrial IoT — the fact that 95% of data across the Industrial Enterprise is unusable because it’s fragmented and disconnected. Element Unify breaks through the data silos by bringing IT and OT data together on a single solution. With Element Unify, IT and OT teams can collaboratively make data-driven operational and business decisions around rich, contextualized metadata, while ensuring scale, reliability and security. 

This allows IT and OT teams to work collaboratively to build rich data context at scale with no-code, automated data pipelines. The end result is a single federated, contextualized source of data from which users can establish their own single version of the truth, all with a powerful governance engine ensuring data integrity across the enterprise.

By aligning and scaling critical OT/IT data, the value of data is unlocked, enabling the delivery of high impact, enterprise-wide analytics that improve core business performance outcomes like asset flexibility, security, reliability, safety, and cost.

This lets them serve their OT partners better, helping satisfy OT business analytical application requirements, all while delivering the enterprise scale, reliability, security, and adaptability enterprise IT applications require.

Value to IT

Add meaningful value to the OT data domain by delivering a single IT/OT data management solution that satisfies the OT analytical application requirements. Additionally, no “rip and replace” — Element Unify easily snaps into IaaS Cloud systems and IoT services.

Value to OT

Enabling a layered process in which every team continues to add to the overall enterprise data model by providing their perspective. This drives an increasingly rich, more contextual view of the assets — and corresponding metadata — informing the manufacturing process.

For example, here is a glimpse at a use case we discussed.

Evonik is one of the world’s leading specialty chemical companies. When plant managers from two production sites in Mobile, AL, discovered that emergency maintenance for pumps and compressors represented nearly 75% of corrective maintenance costs, they decided to take action. 

To avoid equipment failure and increase uptime, the team wanted to leverage condition-based monitoring, predictive maintenance, and root cause analytics. While the plants had a variety of data available from systems like GoPlant and Emerson EMS, the production data was messy. Data scientists spent 50-70% of their time on data cleanup — leading to slower realization of analytics benefits. 

Arpan Seth, Senior Data Scientist and Process Engineer, turned to Element Unify
to produce clean, contextualized asset data models that integrate metadata from source systems and ultimately enable dashboards in Microsoft Power BI-providing a 360-degree view of the plant’s pumps and compressors. 

In just eight weeks, Evonik was able to deploy two asset data models for 464 pieces of rotating equipment at both plants. The results? The plant operations team is now able to predict equipment failure and make informed maintenance decisions using Power BI dashboards. Plant managers estimate a potential savings of $2M over five years, based on the ability to prevent four pump failures per year from just one analytic at one plant. 

Going forward, the team will be able to rapidly scale these analytics to other plants, as well as test and validate new AI and machine learning technologies at speed thanks to Element Unify. 

Challenges 

  • Connecting and integrating disparate sources of asset data 
  • Time and expertise needed for data cleanup and preparation 

Solution 

  • Connect to Aspen InfoPlus.21, SAP and Emerson EMS using Element Unify, to automate data extraction and cleanup 
  • Purpose-built IT/OT data management solution for data scientists; 360-degree view of equipment for plant operations teams 

Results 

  • 80% less time to build asset models 
  • 40% less analytics deployment work 
  • Potential savings of $550K over 5 years, from one analytic at one 

plant 

  • Improved data accessibility, enabling future analytics and AI/ML initiatives 
Share This