Striving for a Connected Plant? Unify your people, processes, and data

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Members of the Element team were excited to participate in the Connected Plant Conference in Austin, Texas. Presented by Power Magazine and Chemical Engineering Magazine, the event is focused on helping organizations “harness digital tools to drive success” and billed as the “only event covering digitalization for the power and chemical process industries”. Scott Jenkins and Sonal Patel helped pull the event together and laid out process safety, operational performance, sustainability, and ESG objectives as the key challenges faced.

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The following graphic from a World Economic Forum whitepaper shared at the event touches on several themes that were discussed.

I was thrilled to be among so many leaders looking to drive innovation in their companies; they had braved uncertain COVID conditions to travel to the event to network, learn from others, and explore offerings from the thirty or so vendor sponsors. As I digest the Texas barbeque I ate with my teammates and reflect on the IT/OT convergence that industrials are experiencing, I want to share three key takeaways that stood out for me from the sessions.

1. Bring your people with you on your transformation journey

There is a clear recognition that the workforce is going through some significant changes. New entrants have different expectations not only regarding work/life balance but about the ways in which technology should support them – they expect data to be readily available and be able to execute many tasks remotely rather than to have to attend to them at the plant. At the same time, rising automation and improved reliability has resulted in operators gaining less experience hands-on with managing equipment – e.g., fewer shutdowns result in less experience in working through a shutdown. Transformation is always challenging and paying particular attention to the people dimension can really help:

• Lay out a clear vision but focus on specific near-term outcomes that improve operations and people’s jobs
• Improve data literacy to help operators understand how the technology will fit in their processes and support them
• Onboard a small group initially, groom champions from within the group, and then extend to larger groups working in consort with the champions
• Conduct regular knowledge sharing sessions where users can showcase their work and transfer best practices
  
  

2. Build your processes with the necessary scale in mind

The adoption of new digital tools and approaches necessary for your transformation initiatives will require updates to operational, management and analytic & decision-making processes. The need to address the people aspect of change is a given (albeit it was a key theme at the event suggesting the importance of keeping it top-of-mind) and was mentioned above. To deliver against the typical transformation objectives, the processes also need to scale across the enterprise. Effectively scaling out to multiple plants and to address the myriad of use cases (spanning anywhere from descriptive through diagnostic and predictive to prescriptive use cases) depends on establishing a governed approach and minimal rework for fast time to insight and ultimately, timely achievement of transformation objectives. As a data professional, I feel data is central to unifying processes and powering the move to scale – more on that below.

3. Base your data models and analytics on a governance underpinning

Whether you aim to have a single source of truth, or multiple “sources of truth”, is a matter of the technical approach you adopt. Companies vary in their level of maturity and sophistication when it comes to technology deployment in general and data architecture in particular. Yet, they all need to be able to rely on the data used for decision-making and understand that data (and therefore, model and analytics) quality and governance are key – if your data is poor quality or ungoverned then it is difficult to invest confidence in the decisions it drives. Target approaches that build in quality checks and improve observability. Ensure the data environment is properly governed so that models are reliable and require little maintenance (unlike libraries of Python code, often used to manage data).

The following slide from a presentation by Lloyd Colegrove (Independent Consultant, retired from 29-year career at Dow as Director of Data Services and the Director of Fundamental Problem Solving within Manufacturing and Engineering) brings several of these aspects together.

Here at Element we see our customers as being on a united data journey as they seek to achieve digital transformation, starting with individual use cases and working their way to more extensive, enterprise-wide, data-driven capabilities.

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Element Unify: Uniting IT/OT metadata – bridging the IT/OT data divide

At Element we are on a mission to unify IT and OT metadata for analytics. To be clear, technology is not the whole answer for digital transformation, it is part of the answer. Our team has deep industrial and data expertise which informs our business and product development. We believe, given the complex industrial environment and multitude of IT and OT data sources, it is important to have a mechanism for integrating the source data, contextualizing it appropriately, provisioning it for consumption while ensuring governance throughout the lifecycle.

Purpose-built for industrials with a knowledge graph foundation and enterprise-grade security, Element Unify does all this. Unify snaps onto your data architecture as a foundational element and does not require “rip and replace” changes, leaving your users with the freedom of choice to use the tools they prefer but knowing that they can rely on the underlying data because their models are governed.

If you believe data quality and governance can help you in your journey, I suggest checking out Element Unify and considering a free trial.

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