Twin Central - Industrial Knowledge Graph

Embracing AI-enhanced active metadata curation and utilizing semantic graph modeling to facilitate OT/IT/ET data contextualization and discovery in Energy and Manufacturing Industries Industrial metadata, as defined by Gartner*, encompasses “any data that is used to enhance the usability, comprehension, utility, or functionality of any other data point,” and serves as a critical resource for gaining insights from operational data, ultimately driving better business decisions.

The Challenge

Relationships between data in different systems are often documented solely in the minds of domain experts, making it difficult for other stakeholders to discover and use the same data for their own purposes. As operational data and metadata from multiple business sources become increasingly available in cloud services, it is now possible to establish a contextualized, cloud-based single source of truth, facilitating improved business decisions by uncovering otherwise inaccessible insights.

Data access speed and data quality are often in opposition within most ad hoc software data engineering systems designed to manage complex operational data dependencies in real-time environments. Creating secure, dependable data pipelines that cater to both batch and real-time data sources while maintaining data quality suitable for specific use cases has also proven challenging.

What is Twin Central?

EOT Twin Central™, a new class of industrial data operation software, when combined with EOT Twin Talk and Twin Sight, empowers organizations to achieve both quality and speed by enabling development teams who best understand the solution context to act directly on the data, its quality, and governance, without resorting to costly centralized master data management. EOT Twin Central™ facilitates the creation of an asset-centric, single source of truth semantic data model. With Twin Central, business technologists can map, link, store, and synchronize relationships between assets and their operational, engineering, and financial metadata using a unified relationship graph. Twin Central’s low-code platform enables operations such as query, search, and navigation across current and historical contextual data models, complete with graph versioning and asset model history. 

A partial list of use cases where applying a modern data operation platform enables better data-driven business decisions includes:

Anomaly Detection:

  • Production Forecasting
  • Maintenance Job Prioritization
  • Remaining Life Prediction
  • Equipment Failure Detection

Twin Central is Enabling Business Users to Model Industrial Assets

Watch how embracing AI-enhanced active metadata curation and utilizing semantic graph modeling to facilitate OT/IT/ET data contextualization and discovery.