← Back to Resources

What is Element Unify™?

ARC’s Industry Forum is an annual pilgrimage for operations leaders in global industrial companies and their technology vendors who want to share best practices, stay on top of emerging trends, and catch up with old friends. 950 of us gathered last week in Orlando, FL to address “Digital Transformation,” and the need for asset-intensive industries seeking to adopt modern technologies like cloud computing and Artificial Intelligence (AI) that are increasingly embedded in other industries like finance and healthcare.

This was the perfect venue for us to announce the release of Element Unify because industrial digital transformation depends on data, a lot of data, and Element Unify is the world’s first data hub built to address the unique challenges of asset data – all IT/OT data associated with industrial equipment and operations. Unlike other data (customer, HR, finance), asset data is super hard to work with and the single biggest digital transformation barrier for industrial companies. It’s siloed across many production sites and systems, out of reach for most data consumers. Using a modern data lake to consolidate asset data doesn’t solve the problem because without context you can’t find the operational signals in the data that are required to feed modern analytics applications. And creating that context (fig 1) is time consuming and expensive because of the naming, structural and semantic complexities of the data, especially sensor data.

Time series data must be enriched with data from other systems for a 360° view

Example: A global top 10 industrial customer deploying a machine learning (ML)-based Asset Performance Management (APM) application at a single production site required five engineers working for six months (30-person months!) using a spreadsheet to integrate and model the data to feed the application. This resulted in a 40,000 row by 26,000 column spreadsheet of metadata from 100+ sources and systems, including 17,000 OSIsoft PI tags. Once deployed it was nearly impossible to keep the spreadsheet model up to date with an accurate, trusted view of ever-changing physical equipment and operating conditions, which routinely blew up the APM application.

Using Element Unify it took ONE person ONE month to build the same data model for the same asset, shrinking time to deployment by 30X. Element Unify also manages changes to the data resulting from changes in the physical environment or time-series tags. Without a way to easily maintain an accurate model, it’s impossible for ML to learn over time. Element Unify is now deployed for all this customer’s production sites globally and serves many other analytical use cases beyond APM, enabling the company’s intelligence systems to adapt and evolve. Element Unify achieves these outcomes for customers by combining the emerging data hub architectural pattern with software features that are purpose-built to address the complexities of asset data. Data hubs are “a more manageable and scalable approach to connecting points of data production with points of data consumption” (See Gartner data hub research note) replacing old approaches that tried to build an enterprise data model by mapping every data source across the enterprise to a new Franken-model in a data warehouse and using point-to-point interfaces with traditional ETL.

Today, data hubs are being deployed because companies must connect to more of their own data and to others’ data as well to enable their analytical algorithms. Data hubs support the need to connect to any data source, move the data to a common repository for harmonization and contextualization, then share the data, and data models, with all data consumers. Element Unify is the world’s first data hub addressing the asset data domain for industrial companies who desire to connect to data, manage their data models, what we call Asset Twins (digital representations of physical assets), then share models and data across the enterprise.

Here’s a summary of how Element Unify aligns with the key requirements of a data hub and addresses the unique requirements of industrial companies.

Data Model

  • Common models across an entire asset, or fleet of similar equipment types.
  • Sensor tags lack context like functional location that must be addressed with data from other systems.
  • Engineering design data (P&ID’s) must be incorporated into the models. As the physical environment changes, data models must adapt.
  • Object models for time series data like OSIsoft PI Asset Framework (AF).
  • Packaged templates for over 200 standard equipment types with the ability to easily create or import new templates.
  • Using purpose-built asset data pipelines to visually model an Asset Twin.
  • Data transformations purpose-built to contextualize time-series data with other enterprise data sets like SAP PM and IBM Maximo.
  • Graph data stores support multiple views of the same underlying data so operations, reliability, and maintenance teams can easily build the data hierarchy they need.
  • Export OSIsoft PI AFs and to other required models.


  • Flexible endpoint integration for IT and OT systems across multiple plant sites.
  • Range of integration capabilities including data integration (ETL) and application integration (APIs).
  • Flexible agent architecture to connect within complex and secure environments.
  • Connections to OSIsoft PI, Aspen IP.21 and others.
  • Supports data integration (ETL) and application integration (APIs).

Governance Controls

  • Range of governance policy types including data quality, privacy, security, retention, role and permissions.
  • Ability to identify sensor drift, null, static and other instrumentation problems.
  • Rich set of pre-defined and custom role-based access control (RBAC) roles for data sharing within and between organizational units.
  • Data quality tools keep Asset Twins evergreen and surfaces tag data quality issues.
  • ISO27001 Compliant.


  • All data consumers can easily access data they need.
  • Physical persistence in multiple cloud data stores tightly integrated with Hadoop ecosystem.
  • Data access via Element or 3rd party applications

We’re excited to launch Element Unify to help asset-intensive organizations avoid Franken-models, and instead, unlock their asset data to better align their data with business outcomes.

To learn more about Element Unify, see a demo, and hear how customers are using it to eliminate these challenges by building a 360-degree view of their data using Asset Twins, please watch the recorded webinar.