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4 Pillars of Value for Data-Focused Industrial Enterprises

November 17, 2021

Steve Beamer and I recently presented 4 key areas where industrial companies are realizing value from their digital transformation projects. It was an insightful session, which you can view any time here.

To kick off the discussion, Steve shared his view on the status quo for many industrial companies today, who want to leverage their investments in existing infrastructure. These organizations may want to use cloud-based solutions since the Industry 3.0 systems they are using are not designed to work together. Small, incremental investments in software can unlock data trapped in 3.0 systems.

Companies with 3.0 systems often tailor software for processes and SMEs, who become de facto wranglers of data for analytics. Attempts to tackle digital transformation initiatives can fall short due to limited resources, siloed data, lack of trust in data over time and lack of scalability.

Industrial Companies Can Benefit From 4 Pillars That Bring Real-World Value.

  1. Speed – Speed requires a change in mindset. Adopting new cloud-based software can enable faster time to insight, bringing value to the organization quickly. Software removes the need for manual wrangling and custom code, providing a central system from which multiple experts, including from both IT and OT teams, can collaborate.

    Additionally, once data is organized it can be reused limitlessly, unlocking additional use cases that might not have been possible or easily attainable. By partnering with software vendors, companies can leverage SME time more effectively, doing more by taking advantage of software capabilities and avoiding one-off manual efforts.

    Real-world example: It took a large oil and gas customer 6 months to model their first asset with 3 people. With Element Unify, the same asset was modeled in 4 weeks by 1 person, resulting in an 18x improvement in speed. Scale enabled by software is much more achievable at this pace.
  1. Quality – The manual method of wrangling data to build analytics comes with a built-in risk around data quality. As OT requests IT-managed datasets, technical debt accumulates, requiring a refresh for each new analytic or any change in process or equipment.

    Eventually, analyses built on out of sync datasets are abandoned and SMEs default to  decision-making based on experience, which lacks traceability and transparency. Software offers automated data quality checks. Additionally, companies can benefit from full transparency on data sources and equipment and processes changes, as well as the ability to persist the data from the source. Teams spend more time improving operations and less time on manual update cycles.

    Real-world example: A large chemicals company opted to standardize local tag names at the corporate level, to enable analytics. In Element Unify they were then able to deploy sensor diagnostics, ensuring the team is informed when a sensor does not produce a value or send quality data to the analytic.
  1. Governance – When datasets are managed in a central system of record, teams can easily see what changes occur, who collaborated on the changes and have a history of all changes over time for easy reference. This results in greater trust in the quality of the data and ultimately reduced effort in maintaining it.

    Real-world example: An Element Unify customer in the power sector had already built their data models and was maintaining them manually. As their reliance on the models increased, so did the team needed to maintain the models. By embracing software, they were able to build a library of corporate Asset Framework (AF) models and keep them up to date. Furthermore, the company was able to redirect the team’s efforts toward building new data models to solve different challenges and improve the business, uncovering net new value for the organization.
  1. Scale – Of the 4 pillars of value, scale is the most challenging to achieve. As companies adopt new software and successfully roll it out at one facility, it becomes apparent that what worked in one location may not work in another. This is because efforts made toward organizing and managing data to stand up analytics at one location are often invisible outside that location, leading to repetitive work at additional facilities.

    This siloed approach does not scale and increases the running tab of technical debt. Organizing data once and having it readily available for future use reduces team effort exponentially. Adding metadata from more data sources for different use cases is effortless, since you are building from existing datasets and enhancing existing models. With software, scaling analytics to multiple sites is much easier with existing personnel, a scenario that would require additional resources if the manual approach was used.

    Real-world example: An Element Unify customer in the oil and gas sector was able to implement major software across 40 facilities in 2 years by organizing their data once and reusing it across facilities. In addition to the major software roll out, they unlocked an additional $364 million a year in revenue by implementing a new project made possible with ready data.

    Another large customer in the food and agriculture sector is adopting this approach and scaling analytics across the organization with a very small team.


It’s no surprise that the 4 pillars; speed, quality, governance and scale are linked.

In a final real-world example, an Element customer discovered that an analytic that took 90 days to build and deploy at one plant only took 60 days with Unify. Scaling to several plants would take 60 days without Unify but only 20 days with the software, allowing them to roll out analytics to 16 plants in 15 months, rather than only 2 in the same period without Unify. Ultimately, the company will realize average savings of $110,000 per year from each analytic at each plant.

Question Highlights

We’ve highlighted a few of the questions that came up, and once again encourage you to watch the recording to hear the full session.

Next Steps

  1. Try Element Unify for 30 days. The software comes with starter templates and sample data to get you up and running quickly. You will also have access to help documentation and training, and experts are available to discuss your use case if needed. If you are already using AWS or Azure products, the trial is also available from those marketplaces. Alternatively, request your trial from our website.
  2. Join us for a live demo to see how establishing a solid, graph-based metadata foundation can help you.