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Kognitwin Unify - Data Contextualization

Industrial professionals are used to working with various data types, from time-series to piping and instrumentation diagrams (P&IDs)models, simulated data, and 3D/2D visualizations. And the list goes on. 

We understand how difficult it can be to handle all this information involved with large complex engineering projects.

In many cases, energy companies face common challenges, like:

  • Having a massive amount of data that is not connected
  • Data silos - Data is stored in different systems
  • Data quality issues with outdated and incorrect data
  • Lack of productivity - Personnel spend most of their time manually collecting data and combining it from different sources
  • Difficulty to get useful insights from data to improve the asset performance

Information needs to flow and be properly handled all the way from early-stage planning, through detailed engineering, procurement, construction, installation, commissioning, operations, and maintenance. If not done correctly, this can severely impact progress and performance on the project, asset, plant, or operation you are working on.

As part of digital transformation initiatives, we find that companies have invested in aggregating this data and making it available to their teams by using on-premise or cloud data warehouse solutions.

Kognitwin, our Dynamic Digital Twin, can help energy companies to handle this challenge providing a unified virtual representation of the physical elements of an asset and its dynamic behaviour over its lifecycle.

Due to its lean integration approach and data contextualization capabilities, Kognitwin Energy can rapidly integrate and contextualize this data in a way that considers the human users of the technology; creating a different way to discover, and interact with your data. The engine behind all of this is the Kognitwin Unify.

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 Kognitwin Unify  enables:

  • Connection to relevant data from different sources
  • Data accessibility and extraction of insightful reports for data-driven decisions
  • Identification of issues and flagging of missing data
  • Improved data accuracy and data quality
  • Simplification in how you interact with your data

Digital Twin Architecture Overview


Kognitwin Energy  provides tools and microservices that integrates with a range of data sources, exchanges data with them to perform value-adding Data Contextualization, Modelling, and Visualization services.

  • Data: Static, transactional and real-time data produced and managed by any contributing party through the asset’s lifecycle.
  • Modelling: The spectrum of process simulation and data-driven modelling, of which the results need to be an integrated part of the Data.
  • Visualization: Methods in which humans can see and interact with data to get the insights needed to understand your facilities around the globe.

Click here to read more about What is Kognitwin Energy

Data & Integration towards Contextualization

The Data & Integration pipeline provides data driven tools for configuration and orchestration of integration tasks. These are the mechanisms that connect with data sources and set up the necessary dataflow to and from the operator’s master data platform, and within the Digital Twin Service Platform.

Kognitwin Unify e-book

Kognitwin Unify is the advanced Data contextualization engine developed by Kongsberg Digital that enables customers to easily connect related data, break silos, and improve productivity. Download our e-book to learn more

Download our Kognitwin Unify e-book

Kognitwin Unify explained

To achieve the full potential from engineering data, and get an efficient overview, new ways of contextualization are needed.

Answering this demand, Kognitwin Unify is the advanced Data contextualization engine developed by Kongsberg Digital that enables customers to easily connect related data, break silos, and improve productivity.

So, what does Kognitwin Unify really do?

  • It represents the services that tie data, structures, and semantics together without relying on any master model. This means that it is possible to build virtual representations for assets that do not have a suitable Engineering Data Warehouse -in place.
  • It provides orchestration of contextualization as well as other data preparation and transformation capabilities.
  • It utilizes algorithms and artificial intelligence, such as machine learning, and acts as the glue that links the various objects together.
  • In short, it Collects, Contextualizes, Inspects & Validates data.
  • This means that through the Inspection process, Kognitwin Unify learns which data to expect and report back if it thinks that it is data you are missing or if there are any issues with it.


Kognitwin Unify features into action

Some practical examples are presented below, demonstrating some of the key features of the Data Contextualization Engine implemented in real operational routines.

Building the spiderweb

Into Kognitwin Energy, the users can navigate through the virtual replica of the asset and access the spider web – the graph view which visualizes the work done by  Kognitwin Unify.

From few to thousands of documents. The spider web shows linked data from a variety of different sources and their relationship together. The different data types connected include:

  • Related Tags
  • Connection to 3D model
  • Work Orders
  • Documents
  • P&ID’s
  • Children/Parent relationships
  • Images
  • Derived Properties

It means no more searching around in different data silos trying to find and link information together.

The graph views also allow users to drill down into any detail they like from this view and, based on the Inspection & Validation feature, it will report back on missing data that should have been there.

Thereby, if this Tag is expected to have a certain relationship, Kognitwin Unify enables you to identify and flag that it is missing.

All connections are made automatically with Machine learning and different mapping techniques to gather all the data we need and make it available in the digital twin.

Detect missing data based on rules

illustration6unify.pngDetected anomaly, not consistent numbers in the control loop, 1072 vs 1012.


The example above refers primary to an analytical functionality that is available for controlling the P&IDs.

How it works:

  • The P&IDs are scanned, and all the information is extracted automatically in the contextualization engine.
  • Different colors identify the data status, in this case:
    • Added circles: Showing all tags that are matched with the process data historian. The latest readings of real-time data are also available.
    • Green: Matched between P&ID and Engineering data warehouse.
    • Yellow: Matched with the control system controllers.
    • Red: Unmatched tags or detected anomalies.
  • Identified anomalies and errors are also stored in a dynamic report, which contains data for every P&ID on the asset.

On the user-experience side, users have the flexibility to:

  • Move freely between the P&ID file and shift between 2D and 3D functionality
  • Navigate to an object in 3D, find it in any of P&IDs through a search, or any other of the linked documents it is available
  • Isolate any components for an easier overview, since these facilities tend to be quite crowded with assets
  • Take measurements, make section views, or use many of the other features available for your specific need.

Kognitwin Unify at a glance

Data connection and a relationship query tool

  • Automatically configure your data for both green and brownfield projects.
  • From the mapping of all available data sources
    to dynamic simulation on the equipment’s performance.
  • Unify all the data sources you need.

Data validation and early mistakes detection

  • Mismatches and inconsistencies
  • Both in documents, design and operations.

One portal for everything

  • 3D, dynamic simulation, real-time data, transactional data etc.
  • Integrated and open data containers
  • Accessibility (The go-to tool for engineering data).



People often think about Digital twins in its simplest form as visualization of components, subsystems, or asset. However, the digital twin has evolved and enriched with more functionalities to attend the energy industry needs.

Complementary to the visualization function, Kognitwin Energy combines Data Contextualization by Kognitwin Unify and Hybrid ML which makes the Digital Twin alive and powerful.

Our Data Contextualization Engine breaks data out from its silos and unifies the different sources together, solving customers’ challenges and boosting collaboration into operations.

Kognitwin Unify also enables different visualization options for various needs. From 2D and 3D to AR / VR ++. Accessible on all devices, from heavy workstations and operation centers to lightweight and portable.


  • Håvard Paulshus Director of Solutions in Kongsberg Digital
  • Kjartan Haug Senior Solutions Engineer in Kongsberg Digital
  • Shane McArdle, Vice President of Production in Kongsberg Digital
  • Haavard Oestensen, Head of Growth of Production in Kongsberg Digital