Open Industrial
Digital Twin
Build dynamic modelsof physical assets and processes,delivering real-time insights
and intelligent decision-making

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©2022 COGNITE

Digital twin: A foundation to unlock today’s
business opportunities across operations............................................................ pg.
The industrial digital twin: A practical definition focusedon driving industrial innovation and value............................................................... pg.
Building the industrial digital twin............................................................................ pg.
Evolving the digital twin............................................................................................. pg.
Going beyond proof of concept............................................................................... pg.
Industrial DataOps for digital twins.......................................................................... pg.
Unpacking the industrial digital twin.................................................................... pg.
The makings of the open industrial digital twin...................................................... pg.
Develop, operationalize and scale use cases 10 times faster................................ pg.
A scalable approach to modeling data.................................................................... pg.
Maintaining an industrial digital twin........................................................................ pg.
Delivering scalable solutions with the open industrial digital twin....................... pg.
The open industrial digital twin in action ............................................................ pg.
How Elkem Carbon is enabling agile manufacturing............................................. pg.
The industrial digital twin is not a single use case............................................... pg.
About Cognite...................................................................................................... pg.

Digital twin: A foundation to unlock today’s
business opportunities across operations
The industrial digital twin: A practicaldefinition focused on driving industrial innovation and value
The idea of digital twins came from the
manufacturing world nearly 20 years ago;
more specifically, from a 2002 article in an
academic journal that talked about “the ideathat information about a physical object canbe separated from the object itself and thenmirror or twin that object.”

Gartner further indicates that digitalization
will motivate industrial companies to integrate and even embed their digital twins with
one another to increase their own compet-

itiveness. To do that, industrial companies
need to make sound strategic decisions now
to lay a trusted and flexible data foundation
for success.

Building the industrial digital twin
To enhance the overall understanding of theiroperations, industrial companies will needan industrial digital twin that contextualizes
data across their entire OT, IT and engineering data spectrum. Here’s our definition:

3

Today, digital twins are morphing to meetthe practical needs of users. In asset-heavy
industries, optimizing production, improving product quality, and predictive maintenance have all amplified the need for adigital representation of both the past andpresent condition of a process or asset.

Gartner predicts that “by 2023, 33% ofowner-operators of homogeneous composite assets will create their own digital twins,
up from less than 5% in 2018” while “at least
50% of OEMs’ mass-produced industrial
and commercial assets will directly integrate
supplier product sensor data into their own
composite digital twins, up from less than
10% today.”©2022 COGNITE

We know the digital twin as a combination of
one or more data sets and a visual representation of the physical object it mirrors. But while
the digital twin concept is no longer new, its
meaning continues to expand based on technological advancement, particularly in therealm of the Industrial Internet of Things (IIoT).

The collected data must be trusted andcontextualized, linked in a way that mirrors
the real world, and made consumable for avariety of use cases.

Digital twins must serve data in a way thataligns to how operational decisions are made.
As a result, companies may need multiple

3D visualization

Analytics

What this shows is that a digital twin isn’t amonolith, but an ecosystem. To support that
ecosystem, industrial companies need an efficient way of populating all the different digital
twins with data in a scalable way.
This next frontier in the digital twin decouples individual models (for example, applications, simulation models, and analytics)
from separate source systems, eliminating
the unnecessary complexity established inpoint-to-point integrations.
APIs & Apps

Evolving the digital twin
Building your digital twin requires turning
siloed data sources into trusted, contextualized data for all. This includes integrating
structured and unstructured data, making
sure there is sufficient trust and quality inthe data, accelerating data modeling, andproviding data governance—all while
templatizing repeatable tasks across theactivities above.

Equipping the process & capabilities that bring
digital twins to life (▼)

Computer vision

Robotics

4

An industrial digital twin is the aggregationof all possible data types and data sets, both
historical and real-time, directly or indirectly
related to a given physical asset or set ofassets in an easily accessible, unified location.

twins, as the type and nature of decisions aredifferent. A digital twin for supply chain, one
for different operating conditions, one thatreflects maintenance, one that’s for visualization, one for simulation—and so on.

Industrial Data Requirements
Integrations

Quality

Modeling

Governance

Facilitating incremental development, maintenance, and continuous improvement

Templates©2022 COGNITE

What is an industrial digital twin?

In addition to the data layer, it’s just as
important to have technical capabilities thatbring digital twins into live operations. This
includes functionality around 3D visualization, building analytic models, open APIs,
using unstructured data, and even capabilities for robotics.
With the foundational data elements in place,bringing a digital twin to life requires leveraging technologies around visualization,
data science, dashboarding, and more. Thisis the functional set of tooling that creates anintuitive representation from the underlying
data foundation. Cognite’s offer is unique
because our technology incorporates both
the data layer and these functional toolkits
to provide a robust launching pad for open
industrial digital twins.

maintenance, streamlined planning, site-tosite performance benchmarking, and autonomous site operations among many others.
To solve this expanded scope of use cases,a digital twin must turn data into information that can be accessed and understood

High-fidelity 3D
representations

Operationalized
use cases fueledby data scienceby both data and operational experts. The
traditional scope of data (time series andsimulators) must expand to include othernontraditional data sets such as work orders,images, and CAD models for more complex
use cases like real-time process monitoringwith computer vision.

Collaborationin real time

Performance KPIs
& dashboarding

Drone and robotics
enablement

Open Industrial Digital Twin
GUI | APIs | SDKs

Charting, visualizations, and analytics
Suite of specialized digital twin tools for ongoingenrichment (►)

5

Data onboarding and contextualization

Historian

PLC, IoT

MES

Operational (OT) data

QMS

CMMS

ERP

Conventional (IT) data

Simulation

Docs

Engineering (ET) data

©2022 COGNITE

Digital twins can give a more comprehensive view of the current state of assets orprocesses and also be extended to represent
entire systems of interconnected systems in
addition to assets. Thinking about a digitaltwin in this way enables new use cases aroundenergy optimization, advanced predictive

Data discovery and exploration

Documentation

Sequences

EventsVisual data

E.g., readings oftemperature, pressure,
speed, torque, direction andnumber of occurrences.

E.g., component
descriptions, service manualand engineering diagrams.

E.g., operating curves,
tabular data, multivariate
data.

E.g., system failures,
identified abnormalities,
planned maintenance andconducted inspections.

E.g., 3D model of entire
vessels or components,
images of individual parts orimage to document defects.

©2022 COGNITETime series

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Examples of data types that Cognite Data Fusions liberates and fuses

Going beyond proof of concept
Abundant digitalization ambition and necessity“Our challenge lies not in the PoC phaseof potential Digital Twin use cases,
but how to execute the Digital Twins in a scalable,
IT like environment.”
Digital Transformation Lead at large
industrial software & services multinational

Most industrial companies have ran at least a handful of AI proof-of-concepts (PoCs)

Many PoCs are considered successful

Few PoCs ever make it into production

Very few PoCs ends up providing substantive ROI (by becoming proofs-of-scale)

DevOps layer
Low code application development

Industrial data
operations makes
industry 4.0 work. (►)
ModelOps layer
Data science/machine learning/physics simulation

Industrial Digital
Twin Solutions
7

An interoperable approach can be simplified to a data layer, an analytics layer, and a
low-code layer for visual app development.
More formally, this translates into an Industrial
DataOps layer, a ModelOps layer—including
simulation hybrids—and a DevOps layer.

“Proofs of concept
are common in industry.
Scaling them isn’t”

Industrial DataOps layer
Industrial data management©2022 COGNITE

While many companies have undertaken
digital twin initiatives, few have been able
to provide value at scale. One of the biggest
pitfalls is a monolithic approach to digital
twins that struggle to scale beyond an initial
proof of concept. To achieve scalability, an
open, interoperable approach is needed.

Industrial customer requirements for high-impact
asset optimization solutions are highly heterogeneous or require significant customization. (►)

The open industrial digital twin makes thecontextualized data available to the user.
To operationalize use cases—seen as the
DevOps layer in the image above, focusing
on the application development itself—weneed full confidence in the data presented
to the application users.
We’re talking about trust in data. This is anecessity, otherwise users can not use thedigital twin to improve workflows and capturebusiness value.

Degree of uniquenessof each customer/
asset making
standard applications
insufficient for value
outtakeFragmentation/lackof winner-take-allindustrial platforms
offering low-cost sales
channel to customers

Extremely high security
and data governancerequirements

Very high customer
support (SLA) andtraining requirements

Industrial customers’ applications needs are incompatible
with attractive ecosystem commercials that encourageand favour independent application developer activity
As a result, industrial enterprises are turning
to end-user development enablement instead
According to Gartner* by 2024:■ three-quarters of large enterprises will beusing at least four low-code development
tools for both IT application development
and citizen development initiatives■

low-code application development will
be responsible for more than 65%
of application development activity

8Additionally, different low-code application frameworks—and SaaS applications
outright—may be chosen for different business functions and their users. These applications must be independent of the underlying
data layer.

Application scalability challenges in industrial market segment*Gartner Magic Quadrant
for Enterprise Low-CodeApplication Platforms(August 2019)©2022 COGNITE

For these layers to work together in a composable fashion, solutions will require openness and interoperability. For example, asbusiness needs change, new best-of-breed
applications and tools may be required foranalysis.

Achieving autonomous operations is a journey with value capture along the way. (▼)

1.

2.

Turn insights
into actionable advice

3.

Build advice
into workflows

4.

Visualize existing data and put it
into context

Enrich the existing data andcreate recommendation models
with actionable advice

Advice is delivered to the right
persona with a defined process

Integrate models directly withselected systems to trigger
automatic actions

Users interpret information via
graphs, dashboards, etc., andmake qualified decisions basedon available data.

From simple implementationof known equations to anomaly
detection and machine learning.

Users are prompted with advice
about impending issues and how
to proceed.

Closed loop integrations with nohuman interaction.

Humans evaluate output andmake qualified decisions basedon the recommendations.

Persona-based content canbe delivered through virtualassistants (Siri/Alexa) and provide
status and actionable next steps.

Turn data
into insights

Automateyour workflows

End users need confidence in the recommendations

future, trust in data is fundamental to the journey toward autonomy.
If we want to execute at scale, we need industrial data management that provides a robust,

well-governed, high-confidence “IT-like”
environment. We need high-quality, verifiable, observable data and model management. In other words, our digital twin platformneeds to have a strong DataOps backbone.

©2022 COGNITE

Trust—or confidence—in data accuracy
becomes more critical as we advance into
predictive and prescriptive solutions, and ultimately all the way to closed-loop decision
execution. While the latter is years into the9

Validation of models, uncertainty estimates, data quality assessments, etc. all need to be resolved before we can move to Level 3

“Our challenge lies not in the PoC phase of potential Digital Twin use cases,
but how to execute the Digital Twins in a scalable, IT like environment.”

Data and model
quality
Monitoring of data ingestion

Time series quality monitoring

Automated identification & reporting

Monitoring the ingestion done by extractors.
Metrics recorded in Prometheus to indicate if
data is not ingested correctly (by frequency,
freshness, quantity).

Set rules for time series and notifies the userwhen the rules are broken or exceeded.

Automated Identification & Reporting(AIR) empowers subject matter expert tomonitor their equipment with thresholds,
calculations, statistics, and machine learning.

Access all data by scanning
tag/equipment

Validate against robotics captured
photo data

Annotate manually to enrichdata backbone

In field scanning of equipment brings up
matching isometric drawings, P&IDs, and 3D
asset models.

Access previously stored 360-image icon
to get a better view, and also see facility
modifications that are not mapped in theisometric.

Activate camera and annotate into theisometric, including a geospatially mapped
photo with timestamp.

With an efficient way to create digital twins,
companies can enable more people acrossthe enterprise to make data-driven decisions, putting the power of digital innovation into the hands of experts at plant floor
operations, remote, across plants, in aftermarket services, and in R&D.

Without a robust foundation of Industrial
DataOps, trust in data, data models, anddata-driven recommendations remains low,
resulting in the inability to progress into scalable operations improvement.

©2022 COGNITE

Trustworthy data delivery in a real-time environment to many different data consumers—
this is what the Industrial DataOps concept
is all about. To operationalize digital twinsin the industry, we need trusted data delivery, focused on continuous data integration
across all OT, IT, ET, and visual data sources.

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Subject matter expert
data inspection

Industrial DataOps for digital twins

on its own, and provides value in isolation.

A digital twin isn’t a monolith, but an ecosystem. This composable way of thinking about
the ecosystem of digital twins supports how
businesses in general need to be run acrossengineering, operations, and product.

The family of siblings accurately reflects thevarious aspects of the physical reality thatprovide value, and provide more autonomy
and local governance than the building andmaintenance of a single canonical digital twin.

What is needed is not a single digital twin that
perfectly encapsulates all aspects of the physical reality it mirrors, but rather an evolving setof “digital siblings.” Each sibling shares a lotof the same DNA (data, tools, and practices)
but is built for a specific purpose, can evolve

This puts the spotlight on what’s blocking
enterprise business value today: How can
we efficiently populate and manage the dataneeded for the digital twins across the inherent entropy and ever-changing nature ofphysical equipment and production lines?

The answer lies in how to think about the data
backbone supporting the digital twins. The
data backbone needs to be run in a distributed but governed manner known as Industrial DataOps.
To adequately extract the value of industrial
data insights, operationalizing data must
become a core part of your business strategy. This translates into developing and scaling mission-critical use cases. Data must be
made available, useful, and valuable in theindustrial context.

©2022 COGNITE

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Make data:Industrial DataOps enables more effective
provisioning, deployment, and managementof digital twins at various levels of granularity. This step change in efficiency is a resultof tooling and automation in the following
areas:
Make data available: Data from any source
system can be accessed and provided withcertain quality assurance for data modeling.

Make data useful: Tools and microservices that make data useful to a range ofdata consumers, including data scientists,
analysts, and engineers.
Make data valuable: Where common data
products and templates can be leveraged
repeatedly to scale across similar assets,sites, and analytical problems.

©2022 COGNITE

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Make data meaningful: Relationships
between different data sets are formed incode and maintained in an industrial knowledge graph.

Unpacking the operational digital twin

To efficiently accelerate the initial rollout andthe incremental improvements for digital

twins, team engagement and enablement
is a requirement. It’s necessary to engage
and build on the competency of the operators and subject-matter experts who know
their equipment and production lines thebest, and to drive efficient data-groundedcollaboration for these roles.

Industrial Digital Twinenabled by underlying graph database and analytical toolkit

13“Cognite Data Fusion® creates contextualized data models that abstract away source
system complexity, enabling customers toquickly and reliably build new analytics dash-boards, new production solutions, and digital twins and to scale these across industrial
assets”.

©2022 COGNITE

The makings of the open industrial
digital twin

The data backbone to power digital twinsneeds to be governed in efficient ways to
avoid the master data management challenges of the past—including tracking
data lineage, managing access rights, andmonitoring data quality, to mention a few
examples. The governance structure must
focus on creating data products that can beused, reused, and collaborated on in efficient and cross-disciplinary ways. The data
products must be easily composable andbe constructed similar to how humans think
about data - where physical equipment areinterconnected both physically and logically.

©2022 COGNITE

Using Cognite Data Fusion®, industrialorganizations are improving brownfield asset
performance using digital twins of equipment, assets, and processes.

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Cognite Data Fusion®, the leading Industrial DataOps platform, unlocks the potential
of industrial digital twins powered by datatransformation and automated contextualization services for developing and maintaining a comprehensive, dynamic industrial
knowledge graph. The industrial knowledge
graph acts as the foundation for the twin andprovides the point of access for data discovery and application development.

By integrating live operational data fromwork orders, engineering drawings, images,
asset hierarchies and more sensor networks
historical data, the industrial digital twin
delivers the complete industrial data spectrum in one accessible user interface andAPI, enabling:

What industrial data contribute to our understandingof our operation (▼)

Images/videos
Work orders
Live & historical
live series
Document drawings
Asset hierarchy
Alarm & events
3D models/designDevelop, operationalize and scale use
cases 10 times fasterFor the high-value purposes of data sciencemodel development, the value of interconnected industrial data made possibleby the data integration layer of the digitaltwin doesn’t depend on any 3D visual data
representation. It’s all about having an interconnected data model that can be queried
programmatically.

Populated industrial
digital twin

Cognite Data Fusion®:
Automated building and maintaining of data
relationship: AI-powered contextualization
services

Data governance: “Data has no value unlessbusiness trusts it and uses it.”Interoperable system: Open APIs,
Connectors to adopted industry solutions

Real-time data access: Highly optimized
time series database for read and write.

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■
■
■
■

Data that can be shared and acted on
Advanced, easy-to-access insights for alldata consumers
Predictive maintenance
Production optimization
Improved safety
Faster responses to adverse events©2022 COGNITE■■

Cognite Data Fusion delivers an industrial knowledge graph to develop, operationalize, and scale use
cases (▼)

Data set
EventsFor example, take someone developing a
production optimization application acrossa fleet of assets. What they need is a strong
domain API that gives them instant access tothe data model, which contains all the relevant data for any given asset or process—regardless of where that data comes from orwhere it’s stored. Also, they need it in domain
language, not in the language of databases.

ectsToMay have

Implements

Files

May have

Implements
OrganizeTime
series

Implements
Implements
Implements

Sequences
3D

belongsTo

CIM:PowerTransformerEnd

CIM:PowerTransformerEnd

isParentOf

CIM:PowerTransformer

isParentOf

connectsTobelongsTo

CIM:Terminal

connectsTo

May have
Implements

May have

May have

Properties

LabelsExample: Machinery and Equipment Manufacturer
Contextualised shop floor data model that:■ provides a digital twin for CNC Machines with alarms,
events, live process status, and engineering
documentation

CIM:ConnectivityNodebelongsTo

CIM:Analog

belongsTo■ reusable data model to support asset healthand quality use cases and expand to future,
more comprehensive data sources

TimeSeries:ActivePower...

■ queried in domain-specific language

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To provide this level of convenience to thedeveloper and data scientist, the output of
the work taking place at the data management and data integration layer of a digital twin architecture is a data model (or aset of data models) that removes the data
complexity. This enables developers anddata scientists to focus on application logic
and algorithm development itself—not
on finding, transforming, integrating, andcleaning data before they can use it. In short,the backbone of all digital twins is a unified,developer-friendly data model.

Implements©2022 COGNITE

Software developers—and data scientists byextension alike—all depend on data models
to make analytical software applications workand scale.

A scalable approach to modeling data

©2022 COGNITE

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Cognite Data Fusion® an Industrial DataOps platform, supports hybrid data integration for both streaming and batch-oriented
sources. Connect to all OT, IT, and ET data
sources with a high-performance historianand OPC-UA/MQTT connectors, and use
Spark-powered on-platform transformation
services for data pipeline streamlining.

populate the Cognite Data Fusion® industrial knowledge graph. Make data available, meaningful and valuable independentof its origins. Empower all data consumers
with instant access to past, real-time andsynthetic (simulated and predicted) data andscenarios. Simple access to complex industrial data.

Fully contextualized operational data can only beprovided by an industrial knowledge graph with automated population. (▼)

Data set
Events

Implements
May have

Implements

Files

May have

Implements
OrganizeTime
series

Implements
Implements
Implements

Sequences

Implements

May have

May have

Properties

Labels18

3D

May have

Cognite Data Fusion®

©2022 COGNITE

Data contextualization involves connecting
all the data for a clearer understanding of anasset or facility. Cognite Data Fusion® delivers contextualized data as a service througha combination of AI-powered contextualization services and subject-matter expert
enablement. Convert data to knowledge
by setting up contextualization pipelines toOnce a reference data model has been
defined, we can move to execution and scaling. Rapid development and scaling are madepossible by autocompleting solution-specificuse case data models by applying AI-powered
pattern recognition and reinforcement learn-

ing. Use case templatization encapsulates
much of the application complexity directly at
the data layer, making data visualization scaling from one to thousands of dashboards oranalytic models possible in a matter of hours.

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beginning of a reusable data asset.

©2022 COGNITE

Create a model for one asset, then scale it
across an entire installation. Spend less time,effort, and resources on finding, cleaning, andmapping data, and more time on generating
value. The step of building a reference data
model should be a standard part of moving
data from sources to applications withinyour data architecture, as it will represent theSimply, a data product is a packaged dataset, a sub-set of the entire industrial knowledge graph, used to solve a specific set ofuse cases. With a data product, provide transparency toward data quality, uptime, Service
Level Objectives (SLO), and compliancewith decentralized (owned by the domainusers) governance rules, lineage, and revision auditing.

After all, it’s the business operations team that
understands the data in context best and istherefore best placed to communicate anduse the data to solve operational challenges.
One way of looking at governance is by separating the layers of governance structures andassociated data management:

Data (product) owners & managers
Requirement:
Discoverable■ Digital twin solution-specific data
onboarding and governance, whereoperators and plant IT workers create
data products that power operational
decisions.
■ Discipline-specific data onboarding andgovernance (for example operations,engineering, or product), where
subject-matter experts and domain
specialists create shareable data products.
■ Enterprise-wide data onboarding andgovernance, where specialized data
management and IT teams create
enterprise-wide data products.

Data (product) consumers

Open Industrial Digital Twin

Requirement:
Addressable
Requirement:
Trustworthy

Data product A

Solution X

Data product B

Solution Y

Data product C

Solution Z

Data product D

Solution G

Requirement:
Self describing and interoperable

Data-product-as-a-service

Use case solving

Requirement:
Ease of development using favorite tools
Requirement:
Ease of operationalizing
Requirement:
Ease of scaling20

For data to be operationally used at scale,especially for critical operations, it needs tobe trusted. For data to be trusted, it needs tobe productized. Build trust and collaborate
with data consumers through data products,that may be used, reused, and collaborated
on in efficient and cross-disciplinary ways.

To productize your data, focus on the mostvaluable operational data domains first, noton the enterprise-wide master data landscape all at once. The goal is to shift from acentralized data team, such as digital or data
center of excellence, into a collaborative
setup, where each data domain is co-ownedby the respective business function producing the data in their primary business tools.

©2022 COGNITE

Maintaining an industrial digital twin

The data product owners need to ensure
their data is discoverable, trustworthy,
self-describing, interoperable, secure, andgoverned by global access control. In otherwords, they need to manage their data products as a service, not as data.

Legacy system integrationarchitecture archetype

Modern platformarchitecture archetype

Businessneeds
Federation

Decouplinglayer

IT features
Democratization

Legacy silos age■ Self sufficient: all data and services
needed within one silos■ Data replication from other silos:
constant alignment and continuousinconsistency

Data productcatalogDatadomains

Modern open solution development
acceleration modelStandardization■ Separation between data and business
functionalities (decoupling)

The open industrial digital twin allows fordata consumption based on the use case.
Any model the user creates can live off thestreaming live data that exists in the twin,enriching the twin with users developing
insights (for example synthetic temperatureor flow information created by a simulator
for equipment where no real sensor exists)back into the twin.
Combined with the complete data spectrum, these insights on equipment behavior
enhance the industrial digital twin, creating
a positive feedback loop that is even more
useful for future solutions.

■ Common marketplace for shared services(data & service catalog)■ Data enrichment within silos
not automatically shared to other silos■ Sharing by design (reuse and data
democratization)
Automation■ Global quality standards (data/service asa product)

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Product X...

Product C

Product B

Product A

Solutions

It’s essential to move from a conventional
centralized data architecture into a domain
data architecture (also referred to as a datamesh). This solves many of the challenges
associated with centralized, monolithic
data lakes and data warehouses. The goal
becomes domain-based data as a service,
not providing rows and columns of data.

©2022 COGNITE

For each of these data products there’s acustodian shepherding the process. An enterprise-wide network of custodians is importantto achieve the nonlinear scaling effect acrosssites, production lines, and equipment.

■ Define and monitor who should be able
to access the data and who could beinterested in it.

■ Define how the data product is related toother data products.
■ Keep communicating with the data
custodian network to make sure that
innovation and collaboration happensin efficient ways across the different data
domains.

■ Define and monitor what data quality canbe expected from the data in the data
product.

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Digitalizing how businesses are run isn’t
about what technology can do—Industrial
DataOps is about having making it easy foreveryone in an organization to create data
products and make data-driven decisions.
Convenience is the key concept to embrace.
To get this distributed model to work, anIndustral DataOps solution should:■ Define and update what data should be
added to data products and how it relates
to business needs from digital twinsand solutions.

©2022 COGNITE

Delivering scalable solutions with theopen industrial digital twinSource data model

Domain data model

Solution data model

Asset hierarchy from SAP ■ OPC from Kepware■ BOP/BOM from MES

Asset or process centric ■ Follow standards(e.g. OPC, ISA95) ■ Custom semantic types (CNC)

Data model targeting data consumption for use cases

Liberate data in source systems by having themmodeled and queryable in Cognite Data Fusion®
through the same API interfaces.

Domain data models are optimized for reuse between
application developers, data scientists and solution
developers. We offer a set of industry standard OOTB
data models that can be extended.

Optimized data models for applications, data
scientists and other solutions. We provide a GraphQL
interface with auto generated SDKs for minimal code.
This provides Backend as a Service (BaaS) to reduce
cost of development and maintenance of solutions.

In practice, every solution has specific expectations and requirements to how data is
supposed to be structured and modeled,
and to this end, Cognite Data Fusion®
provides a flexible data modeling frameworkthat allows for such different perspectives
into data to be clearly described.

tains query ability in Cognite Data Fusion®
through the same API interfaces. The domain
data model synthesizes information acrossdata sources and allows for a detailed
description of an asset or process that canbe continuously enriched as new informationand data sources become available.

While the source data model liberates datafrom a variety of source systems it also main-

A solution data model imposes a schemathat the application can validate and rely on

and the instances of which are populatedwith the help of Cognite’s contextualization
services in an automated way, leveraging adomain-guided machine-learning approach.
Compared to the domain data model, whichallows for a higher level of entropy and therepresentation of evolving ontologies, thesolution data model is much more rigid whileat the same time allowing for true scalability
across two dimensions:23Apps, solutions, usecases etc.

RAW©2022 COGNITE

Sourcesystems

24

2. Scalability across a portfolio of solutions
is enabled by immediate access to a wide
range of data sources and the fact that application requirements to data are decoupledfrom the representation in the domain data
model. This allows for use cases to be solvedthat require different levels of data granularity, e.g. plant-level maintenance optimization vs enterprise-level strategic planning.

Cognite Data Fusion® serves as the data
backbone for digital twins for dozens ofindustrial companies around the world. The
SaaS product serves data to a broad set of
digital twin types in a way that secures rapid
rollout to hundreds of sites, makes them efficient to deploy and operate, and secures
support for lean processes when they’re in
production.

©2022 COGNITE

1. Scalability of one Solution is ensured
through automated population of solution
data model instances, e.g. scaling a maintenance optimization solution across the entire
asset portfolio.

The open industrial digital twin in action

The goal of the project has been to equip
Elkem Carbon with machine learning tools in
operations and the necessary capabilities toutilize them; to build a first version of Elkem’s
Carbon data model and; to demonstrate how
this built data model could bring value to
Elkem Carbon by solving a specific machinelearning use cases in the milling step of theramming paste production process.

Problem statement
One key source for producing off-spec material is poor conditions of the mill. This can becaused by, e.g., wear, clogging or air leakage.
In the worst case, the bad state of the mill cango unnoticed for a long time if the processengineers do not monitor the system at all
times, or if the fault is hard to predict fromvisual inspection of the data (i.e., caused by‘sudden’ errors). Since the anomalous behav-

Value
The State Detection model improves transparency on actual conditions of millingsystem machine(s) and its subcomponents.
Looking at comprehensive data insights and
KPIs, as well as utilizing the machine learningmodel with actionable insights:■ Enables operators to proactively act
before a potential failure occurring rather
than after■ Reduces the likelihood of having
off-spec products■ Provides better understanding of systembehavior allowing better monitoringand more advanced analysis■

Reduces the likelihood that the Mill
becomes a bottleneck

Going forward
Elkem Carbon Fiskaa is operationalizing the
ML tools and use cases to a larger user base.
Liberating data from the rest of the production processes and related data sources willallow Elkem to capture value not only fromthe current Vertical Mill but also other criticalproduction processes.

Key project deliveries
Cognite equipped Elkem Carbon withmachine learning tools utilizing contextualized data from Cognite Data Fusion®.
Cognite then empowered Elkem to use the
ML tools and to build one State Detection
Model targeting the mill, visualized in one
dashboard. At the end, the project delivered, amongst other:■ A proven and trusted State Detection
Model
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Cognite has supported Elkem Carbon with
their first step towards becoming a moredata-driven company

ior is not detected in due time, it can lead
to off-spec production and loss of revenues.
Developing a state detection model was thefirst step in mitigating this problem.

■ Three different user-specific Grafana
Dashboards for operations, maintenanceand process engineers©2022 COGNITE

Enable agile manufacturing with
an open industrial digital twin■ Live, enriched contextualized data
available in Cognite Data Fusion®
■ Data Science toolbox for future model
development■ End-user ownership of end-solution tocontinue improving and iterating the end
solution over time

■ Increased potential for future cost saving
at the mill

■ Foundation for future scaling across
Elkem organization

Elkem Carbon is equipped with an extensive toolbox
to continue exploring data and adopt existing state
detection models based on new findings (▼)

Cognite Charts

Cognite Air

Cognite Functions

Cognite Data Fusion® (CDF) contextualize several data sources into one enriched
data model for end-users to explore andbuild front-end solutions on top.

Charts is a powerful tool for domain
experts explore, trend, and analyze industrial data stored in Cognite Data Fusion®.

Automated Identification & Reporting(AIR) empowers engineers to monitor theirequipment with thresholds, calculations,
statistics, and ML.

Cognite Functions enables data scientists to easily move from PoC models to
production, by hosting. running and scheduling Python code in Fusion.

Cognite SDK

GrafanaPython CodeWith Cognite’s Python SDK Data Scientists
has easy access to the data and functionality in Cognite Data Fusion® in preferreddevelopment environment enablingadvanced analytics.

Cognite’s Grafana connectors enables
domain experts to easy visualize andanalyze the data in Cognite Data Fusion®.

Code used in the project with detailed
README.md for using the scripts andcontinue contextualizing going forward.

©2022 COGNITE

Cognite Fusion

26

Extensive Cognite toolbox delivered to Elkem Carbon

The industrial digital twin is not a single use case
The industrial digital twin is not a use case. It
is not an app. It’s a platform on which to build,
deploy, and scale many different use cases
and applications, from those large, complex,
and visually impressive to more modest on
the surface, focusing on delivering actionable
information to drive process improvements.
An industrial digital twin is one tool thatindustrial companies can use to reach agreater level of understanding and control
regarding their data and their operations,discovering insights to optimize operations,increase uptime, and revolutionize businessmodels.
Historically, a digital twin has had a single
dimension of contextualization to solve
the use case it was specifically created toanswer. When creating an industrial digitaltwin, the richness of the data describing theindustrial reality is critical to create many morecorrelations between data sources.
An operational digital twin built with Cognite Data
Fusion® enables rapid scaling of tailored solutions
across domains(▼)

Infinitely scalable

Make Data Valuable

Industry standard tools

Low or no-code

Data science

Industry applications
More easily governed

Accelerated time-to-value

Make Data Available

DCS

Historian

CMMS

27

Automated preparation, cleaning, mapping, contextualization.
Lower incremental cost with data model that grows over time.

Cognite Data Fusion®

MES©2022 COGNITE

Make Data
Meaningful & UsefulOpen Industrial Digital Twin

The industrial digital twin must be dynamic,
flexible enough to meet the needs of a
growing variety of users and models. This
is why contextualization is crucial to thecreation of the industrial digital twin, making
it flexible enough and scalable enough tohandle the complexities of current operations
and to anticipate the demands of data-driven
operations in the digitalized industrial future.
The solutions that are built on top can support
personas across the organization, including
subject-matter expert chart tools, applications
for operations, quality, and maintenance, andthe ability for data science teams to build
predictive or prescriptive solutions.

data—the ability to understand where itcomes from, how reliable it is, and how toenrich it over time. They will also be the firstones to scale successful solutions on top ofthat data, which must be the priority of any
digitalization initiative.
Bring AI-software capabilities into the process
without replacing current applications andsource systems. Build, operationalize, andscale hundreds of use cases.

©2022 COGNITE

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Companies that deploy an industrial digitaltwin will finally have true control over their

About Cognite
Cognite is a global industrial SaaS
company that supports the full-scale digitaltransformation of asset-heavy industries
around the world. Our core Industrial DataOps
platform, Cognite Data Fusion®, enables data
and domain users to collaborate to quickly
and safely develop, operationalize, and scaleindustrial AI solutions and applications.

Cognite Data Fusion® codifies industrial
domain knowledge into software that fits into
your existing ecosystem and enables scalefrom proofs of concepts to truly data-driven
operations to deliver both profitability andsustainability.

A unique global team that combines softwarewith deep industry expertise
Softwareand data science

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Visit us at www.cognite.com
and follow us
on Twitter or LinkedIn.
Industry
expertise©2022 COGNITE

10+ International Olympiad
in Informatics medalists
15% Ph.D.s

cognite.com →