Open Industrial
Digital Twin
Build dynamic models of physicalassets and processes, deliveringreal-time insights and intelligent
decision-making

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

Table of contents
Digital twin: A foundation to unlock today’s business opportunities across operations........... pg. 3
The industrial digital twin:
A practical definition focused on driving industrial innovation and value............................. pg. 3
Building the industrial digital twin............................................................................................. pg. 3
Evolving the digital twin............................................................................................................. pg. 4
Going beyond proof of concept................................................................................................ pg. 7
Industrial DataOps for digital twins........................................................................................... pg. 11
Unpacking the industrial digital twin............................................................................................... pg. 13
The makings of the open industrial digital twin....................................................................... pg. 13
Develop, operationalize and scale use cases 10 times faster................................................ pg. 15
A scalable approach to modeling data..................................................................................... pg. 17
Maintaining an industrial digital twin........................................................................................ pg.20
Delivering scalable solutions with the open industrial digital twin........................................ pg.22
The open industrial digital twin in action ....................................................................................... pg.25
How Elkem Carbon is enabling agile manufacturing............................................................... pg.25
The industrial digital twin is not a single use case......................................................................... pg. 27

2

Cognite is a global industrial SaaS company that
supports the full-scale digital transformation ofasset-heavy industries around the world. Our core
Industrial DataOps platform, Cognite Data Fusion®,enables data and domain users to collaborate toquickly and safely develop, operationalize, andscale industrial AI solutions and applications.

©COGNITE 2023 — COGNITE.COM

About Cognite

Cognite Data Fusion® codifies industrial domain
knowledge into software that fits into your existing
ecosystem and enables scale from proofs ofconcepts to truly data-driven operations to deliver
both profitability and sustainability.
WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Digital twin: A foundation to unlock today’s business opportunities across operations

We know the digital twin as a combination of one ormore data sets and a visual representation of thephysical object it mirrors. But while the digital twinconcept is no longer new, its meaning continues
to expand based on technological advancement,
particularly in the realm of the Industrial Internetof Things (IIoT).
Today, digital twins are morphing to meet the practical needs of users. In asset-heavy industries, optimizing production, improving product quality, andpredictive maintenance have all amplified the needfor a digital representation of both the past andpresent condition of a process or asset.
Gartner predicts that “by 2023, 33% of owner-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
WHY COGNITE DATA FUSION® →Gartner further indicates that digitalization will
motivate industrial companies to integrate andeven embed their digital twins with one anotherto increase their own competitiveness. 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 their operations, industrial companies will need an industrial
digital twin that contextualizes data across their
entire OT, IT and engineering data spectrum. Here’s
our definition ►Digital twins must serve data in a way that aligns
to how operational decisions are made. As a result,companies may need multiple twins, as the typeand nature of decisions are different. A digital twin
for supply chain, one for different operating conditions, one that reflects maintenance, one that’s forvisualization, one for simulation—and so on.
What this shows is that a digital twin isn’t a monolith, but an ecosystem. To support that ecosystem, industrial companies need an efficient way of

BENEFITS FOR YOUR TEAMS →CONTACT SALES →What is an industrial digital twin?
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.
The collected data must be trusted andcontextualized, linked in a way that mirrors thereal world, and made consumable for a variety
of use cases.

3

The idea of digital twins came from the manufacturing world nearly 20 years ago; more specifically,
from a 2002 article in an academic journal thattalked about “the idea that information about aphysical object can be separated from the object
itself and then mirror or twin that object.”supplier product sensor data into their own composite digital twins, up from less than 10% today.”©COGNITE 2023 — COGNITE.COM

The industrial digital twin: a practical definition focused on driving industrial innovation and value

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 in point-to-point integrations.

Evolving the digital twin
Building your digital twin requires turning siloeddata sources into trusted, contextualized data
for all. This includes integrating structured andunstructured data, making sure there is sufficienttrust and quality in the data, accelerating data

Equipping the process & capabilities that bring digital twins to life ▼In addition to the data layer, it’s just as important tohave technical capabilities that bring digital twins
into live operations. This includes functionality
around 3D visualization, building analytic models,
open APIs, using unstructured data, and evencapabilities for robotics.
With the foundational data elements in place,bringing a digital twin to life requires leveraging
technologies around visualization, data science,

Analytics

APIs & Apps

dashboarding, and more. This is the functional setof tooling that creates an intuitive representationfrom 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.
Suite of specialized digital twin tools for ongoingenrichment ►Digital twins can give a more comprehensive viewof the current state of assets or processes and also
be extended to represent entire systems of interconnected systems in addition to assets. Thinking about a digital twin in this way enables newuse cases around energy optimization, advanced

Computer vision

Robotics

GovernanceTemplates

Industrial Data Requirements
Integrations

Quality

Modeling

Facilitating incremental development, maintenance, and continuous improvement

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →©COGNITE 2023 — COGNITE.COM

4

3D visualization

modeling, and providing data governance—all while
templatizing repeatable tasks across the activities
above.

predictive maintenance, streamlined planning,site-to-site performance benchmarking, andautonomous site operations among many others.
To solve this expanded scope of use cases, a digital twin must turn data into information that canbe accessed and understood by both data andoperational experts. The traditional scope ofdata (time series and simulators) must expand toinclude other nontraditional data sets such as workorders, images, and CAD models for more complex
use cases like real-time process monitoring withcomputer vision.

High-fidelity 3D
representations

Operationalized
use cases fueledby data science

Collaborationin real time

Performance KPIs
& dashboarding

Drone and robotics
enablement

Open Industrial Digital Twin
GUI | APIs | SDKs

Charting, visualizations, and analytics
Data discovery and exploration

Historian

PLC, IoT

MES

Operational (OT) data

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →

QMS

CMMS

ERP

Conventional (IT) data

Simulation

Docs

Engineering (ET) data

©COGNITE 2023 — COGNITE.COM

5

Data onboarding and contextualization

Examples of data types that Cognite Data Fusions liberates and fuses

Documentation

Sequences

EventsVisual data

E.g., readings oftemperature, pressure,
speed, torque,direction and number ofoccurrences.

E.g., component
descriptions, service
manual and 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 partsor image to document
defects.

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →©COGNITE 2023 — COGNITE.COM

6Time series

CONTACT SALES →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 Twinsin 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)Industrial data
operations makes
industry 4.0 work. ►DevOps layer

Low code application development

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

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

ModelOps layer

Data science/machine learning/physics simulation

Industrial DataOps layer
Industrial data management

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Industrial Digital
Twin Solutions©COGNITE 2023 — COGNITE.COM

While many companies have undertaken digitaltwin 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.

Degree of uniquenessof each customer/
asset making
standard applications
insufficient for value
outtakeIndustrial customer requirements for high-impact
asset optimization solutions are highly heterogeneous or require significant customization. ►Extremely highsecurity anddata governancerequirements

Very high customer
support (SLA) andtraining requirements

Industrial customers’ applications needs are incompatible with attractive ecosystem commercialsthat encourage and favour independent application developer activity

The open industrial digital twin makes the contextualized data available to the user. To operationalize use cases—seen as the DevOps layer in theimage above, focusing on the application development itself—we need full confidence in the data
presented to the application users.
We’re talking about trust in data. This is a necessity, otherwise users can not use the digital twin
to improve workflows and capture business value.

Fragmentation/lackof winner-take-allindustrial platforms
offering low-costsales channel tocustomers

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*Gartner Magic Quadrant for Enterprise Low-Code Application Platforms(August 2019)

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →8

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

Application scalability challenges in industrial market segment©COGNITE 2023 — COGNITE.COM

For these layers to work together in a composable fashion, solutions will require openness andinteroperability. For example, as business needschange, new best-of-breed applications and tools
may be required for analysis.

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

1.

2.

3.

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 implementation of
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) andprovide status and actionable
next steps.

Turn data
into insights

Turn insights
into actionable advice

Build advice
into workflows

Automateyour workflows

Trust—or confidence—in data accuracy becomes
more critical as we advance into predictive andprescriptive solutions, and ultimately all the way
to closed-loop decision execution. While the latteris years into the future, trust in data is fundamental
to the journey toward autonomy.
WHY COGNITE DATA FUSION® →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 andmodel management. In other words, our digital twin
platform needs to have a strong DataOps backbone.

BENEFITS FOR YOUR TEAMS →CONTACT SALES →©COGNITE 2023 — COGNITE.COM

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

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End users need confidence in the recommendations

“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 to monitor
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.

Trustworthy data delivery in a real-time environment to many different data consumers—this iswhat the Industrial DataOps concept is all about.
To operationalize digital twins in the industry, weneed trusted data delivery, focused on continuous data integration across all OT, IT, ET, and visual
data sources.
WHY COGNITE DATA FUSION® →With an efficient way to create digital twins, companies can enable more people across the enterprise
to make data-driven decisions, putting the powerof digital innovation into the hands of experts at
plant floor operations, remote, across plants, inaftermarket services, and in R&D.

BENEFITS FOR YOUR TEAMS →CONTACT SALES →Without a robust foundation of Industrial DataOps,
trust in data, data models, and data-driven recommendations remains low, resulting in the inability
to progress into scalable operations improvement.

©COGNITE 2023 — COGNITE.COM

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Subject matter
expert datainspection

Industrial DataOps for digital twins

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 ingeneral need to be run across engineering, operations, and product.

The family of siblings accurately reflects the various aspects of the physical reality that provide
value, and provide more autonomy and local governance than the building and maintenance of asingle 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 set of“digital siblings.” Each sibling shares a lot of thesame DNA (data, tools, and practices) but is built
for a specific purpose, can evolve on its own, andThis puts the spotlight on what’s blocking enterprise business value today: How can we efficiently populate and manage the data needed forthe digital twins across the inherent entropy andever-changing nature of physical equipment andproduction 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 governedmanner 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 intodeveloping and scaling mission-critical use cases.
Data must be made available, useful, and valuable
in the industrial context.

©COGNITE 2023 — COGNITE.COM

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Make data:

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Industrial DataOps enables more effective provisioning, deployment, and management of digital twins at various levels of granularity. This stepchange in efficiency is a result of tooling and automation in the following areas:
Make data available: Data from any source system
can be accessed and provided with certain quality
assurance for data modeling.

Make data useful: Tools and microservices thatmake data useful to a range of data 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 analyticalproblems.

©COGNITE 2023 — COGNITE.COM

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

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Unpacking the operational digital twin
The makings of the open industrial
digital twintion solutions, and digital twins and to scale theseacross industrial assets”.

“Cognite Data Fusion® creates contextualized
data models that abstract away source systemcomplexity, enabling customers to quickly and reliably build new analytics dashboards, new produc-

To efficiently accelerate the initial rollout and theincremental improvements for digital twins, teamengagement and enablement is a requirement. It’s

necessary to engage and build on the competencyof the operators and subject-matter experts who
know their equipment and production lines thebest, and to drive efficient data-grounded collaboration for these roles.

©COGNITE 2023 — COGNITE.COM

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Industrial Digital Twin enabled by underlying graph database and analytical toolkit

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →The data backbone to power digital twins needs tobe governed in efficient ways to avoid the masterdata management challenges of the past—including tracking data lineage, managing access rights,
and monitoring data quality, to mention a few examples. The governance structure must focus on
creating data products that can be used, reused,and collaborated on in efficient and cross-disciplinary ways. The data products must be easily
composable and be constructed similar to howhumans think about data - where physical equipment are interconnected both physically and logically.
Cognite Data Fusion®, the leading Industrial DataOps platform, unlocks the potential of industrial
digital twins powered by data transformation andautomated 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 andapplication development.

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →©COGNITE 2023 — COGNITE.COM

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Using Cognite Data Fusion®, industrial organizations are improving brownfield asset performanceusing digital twins of equipment, assets, andprocesses.

CONTACT SALES →By integrating live operational data from workorders, engineering drawings, images, asset hierarchies and more sensor networks historical data,
the industrial digital twin delivers the complete
industrial data spectrum in one accessible userinterface and API, enabling:■ Data that can be shared and acted on

What industrial data contribute to our understanding of our operation ▼Images/videos
Work orders
Live & historical
live series■ Advanced, easy-to-access insights for all data
consumers

Document drawings
Asset hierarchy■ Predictive maintenance
Alarm & events■ Production optimization

3D models/design■ Improved safety■ Faster responses to adverse events

WHY COGNITE DATA FUSION® →Automated building and maintaining of data relationship: AI-powered contextualization services
Interoperable system: Open APIs,
Connectors to adopted industry solutions

BENEFITS FOR YOUR TEAMS →CONTACT SALES →Data governance: “Data has no value unless business trusts it and uses it.”

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

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For the high-value purposes of data science model
development, the value of interconnected industrial data made possible by the data integration
layer of the digital twin doesn’t depend on any 3Dvisual data representation. It’s all about having aninterconnected data model that can be queried
programmatically.

Cognite Data Fusion®:©COGNITE 2023 — COGNITE.COM

Develop, operationalize and scaleuse cases 10 times faster

Populated industrial
digital twin

Software developers—and data scientists byextension alike—all depend on data models to makeanalytical software applications work and scale.
For example, take someone developing a production optimization application across a fleet ofassets. What they need is a strong domain API
that gives them instant access to the data model,
which contains all the relevant data for any given
asset or process—regardless of where that data
comes from or where it’s stored. Also, they need it
in domain language, not in the language of databases.

CIM:PowerTransformerEnd

CIM:PowerTransformerEnd

Cognite Data Fusion delivers an industrial knowledge graph to develop, operationalize, and scaleuse cases ▼CIM:PowerTransformer

isParentOf

connectsTobelongsTo

CIM:Terminal

belongsTo

CIM:Analog

belongsTo

TimeSeries:ActivePower...

BENEFITS FOR YOUR TEAMS →Contextualised shop floor data model that:

CONTACT SALES →connectsTo

CIM:ConnectivityNode■ provides a digital twin for CNC Machines withalarms, events, live process status, and engineering documentation■ reusable data model to support asset healthand quality use cases and expand to future,
more comprehensive data sources■ queried in domain-specific language

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isParentOf

ectsToWHY COGNITE DATA FUSION® →Example:Machinery and Equipment Manufacturer

belongsTo©COGNITE 2023 — COGNITE.COM

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

A scalable approach to modeling data

©COGNITE 2023 — COGNITE.COM

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

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →by setting up contextualization pipelines to populate the Cognite Data Fusion® industrial knowledge
graph. Make data available, meaningful and valuable independent of its origins. Empower all data
consumers with instant access to past, real-timeand synthetic (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. ▼Cognite Data Fusion®

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →©COGNITE 2023 — COGNITE.COM

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Data contextualization involves connecting all thedata for a clearer understanding of an asset or facility. Cognite Data Fusion® delivers contextualized
data as a service through a combination of AI-powered contextualization services and subject-matter expert enablement. Convert data to knowledgerepresent the beginning of a reusable data asset.
Once a reference data model has been defined, we
can move to execution and scaling. Rapid development and scaling are made possible by autocompleting solution-specific use case data models
by applying AI-powered pattern recognition andreinforcement learning. 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.

©COGNITE 2023 — COGNITE.COM

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Create a model for one asset, then scale it acrossan entire installation. Spend less time, effort, andresources on finding, cleaning, and mapping data,
and more time on generating value. The step ofbuilding a reference data model should be a standard part of moving data from sources to applications within your data architecture, as it will

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Simply, a data product is a packaged data set, a
sub-set of the entire industrial knowledge graph,used to solve a specific set of use cases. With adata product, provide transparency toward data
quality, uptime, Service Level Objectives (SLO),
and compliance with decentralized (owned by thedomain users) governance rules, lineage, and revision auditing.

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

Data (product) owners & managers
Requirement:
Discoverable

Open Industrial Digital Twin

Requirement:
Addressable
Requirement:
Trustworthy■ Digital twin solution-specific data onboarding
and governance, where operators and plant IT
workers create data products that power operational decisions.
■ Discipline-specific data onboarding and governance (for example operations, engineering,or product), where subject-matter experts anddomain specialists create shareable data products.
■ Enterprise-wide data onboarding and governance, where specialized data management andIT teams create enterprise-wide data products.

Data (product) consumers
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
WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →Use case solving
CONTACT SALES →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 to be trusted.
For data to be trusted, it needs to be 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 most valuable
operational data domains first, not on the enterprise-wide master data landscape all at once. The
goal is to shift from a centralized 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 thedata in their primary business tools.

©COGNITE 2023 — COGNITE.COM

Maintaining an industrial digital twin

The data product owners need to ensure theirdata is discoverable, trustworthy, self-describing, interoperable, secure, and governed by
global access control. In other words, they needto manage their data products as a service, not asdata.

Legacy system integrationarchitecture archetype

Modern platformarchitecture archetype

Businessneeds

Data productcatalog

Federation

Decouplinglayer

IT features
Data
domains

Democratization

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

Combined with the complete data spectrum, these
insights on equipment behavior enhance the industrial digital twin, creating a positive feedback loopthat is even more useful for future solutions.

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

WHY COGNITE DATA FUSION® →The open industrial digital twin allows for data
consumption based on the use case. Any model
the user creates can live off the streaming live
data that exists in the twin, enriching the twin withusers developing insights (for example synthetic
temperature or flow information created by a simulator for equipment where no real sensor exists)back into the twin.

BENEFITS FOR YOUR TEAMS →■ Global quality standards (data/service asa product)

CONTACT SALES →21

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 data mesh). 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.

©COGNITE 2023 — COGNITE.COM

For each of these data products there’s a custodian shepherding the process. An enterprise-wide
network of custodians is important to achieve thenonlinear scaling effect across sites, productionlines, and equipment.

Delivering scalable solutions with theopen industrial digital twin

To get this distributed model to work, an Industral
DataOps solution should:■ Define and monitor what data quality can be
expected from the data in the data product.

Digitalizing how businesses are run isn’t about what
technology can do—Industrial DataOps is about
having making it easy for everyone in an organization to create data products and make datadriven decisions. Convenience is the key concept
to embrace.

■ Define and update what data should be added
to data products and how it relates to businessneeds from digital twins and solutions.

■ Define how the data product is related to otherdata products.

©COGNITE 2023 — COGNITE.COM

22■ Define and monitor who should be able to accessthe data and who could be interested in it.

■ Keep communicating with the data custodian
network to make sure that innovation and collaboration happens in efficient ways across thedifferent data domains.

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Apps, solutions, use cases etc.

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 framework that allows for such different
perspectives into data to be clearly described.

API interfaces. The domain data model synthesizes
information across data sources and allows for a
detailed description of an asset or process that canbe continuously enriched as new information anddata sources become available.

automated way, leveraging a domain-guided
machine-learning approach. Compared to thedomain data model, which allows for a higher levelof entropy and the representation of evolving ontologies, the solution data model is much more rigid
while at the same time allowing for true scalability
across two dimensions:

While the source data model liberates data from avariety of source systems it also maintains query
ability in Cognite Data Fusion® through the same
WHY COGNITE DATA FUSION® →A solution data model imposes a schema thatthe application can validate and rely on and theinstances of which are populated with the helpof Cognite’s contextualization services in an

BENEFITS FOR YOUR TEAMS →CONTACT SALES →

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RAW

©COGNITE 2023 — COGNITE.COM

Source systems

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

Cognite Data Fusion® serves as the data backbone
for digital twins for dozens of industrial companies
around the world. The SaaS product serves data to abroad 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 forlean processes when they’re in production.

©COGNITE 2023 — COGNITE.COM24

2. Scalability across a portfolio of solutions isenabled by immediate access to a wide rangeof data sources and the fact that application
requirements to data are decoupled from therepresentation in the domain data model. This
allows for use cases to be solved that requiredifferent levels of data granularity, e.g. plant-level

maintenance optimization vs enterprise-levelstrategic planning.

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →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 to utilize them; tobuild 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 machine learning use cases in the milling
step of the ramming paste production process.

↘ Problem statement
One key source for producing off-spec material
is poor conditions of the mill. This can be caused
by, e.g., wear, clogging or air leakage. In the worst
case, the bad state of the mill can go unnoticed fora long time if the process engineers do not monitor the system at all times, or if the fault is hardto predict from visual inspection of the data (i.e.,
caused by ‘sudden’ errors). Since the anomalous
behavior is not detected in due time, it can lead tooff-spec production and loss of revenues. Developing a state detection model was the first step in
mitigating this problem.
WHY COGNITE DATA FUSION® →↘ Key project deliveries

The State Detection model improves transparency
on actual conditions of milling system machine(s)and its subcomponents. Looking at comprehensive data insights and KPIs, as well as utilizing themachine learning model with actionable insights:

Cognite equipped Elkem Carbon with machinelearning 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:■ Enables operators to proactively act before apotential failure occurring rather than after■ Reduces the likelihood of having off-spec products■ Provides better understanding of system behavior allowing better monitoring and more advanced
analysis■ Reduces the likelihood that the Mill becomes abottleneck↘ 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 will allow Elkem to capture
value not only from the current Vertical Mill but also
other critical production processes.

BENEFITS FOR YOUR TEAMS →CONTACT SALES →■ A proven and trusted State Detection Model■ Three different user-specific Grafana Dashboards for operations, maintenance and processengineers■ Live, enriched contextualized data available in
Cognite Data Fusion®
■ Data Science toolbox for future model development■ End-user ownership of end-solution to continue
improving and iterating the end solution over time
■ Foundation for future scaling across Elkem organization■ Increased potential for future cost saving at the
mill

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Cognite has supported Elkem Carbon with theirfirst step towards becoming a more data-driven
company↘ Value©COGNITE 2023 — COGNITE.COM

Enable agile manufacturing with
an open industrial digital twin

Elkem Carbon is equipped with an extensive toolbox to continue exploring data
and adopt existing state detection models based on new findings ▼Extensive Cognite toolbox delivered to Elkem Carbon
Cognite Fusion

Cognite Charts

Cognite Air

Cognite Functions

Cognite Data Fusion® (CDF) contextualize several data sources into one
enriched data model for end-users to
explore and build 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 their equipment 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.

GrafanaPython CodeWith Cognite’s Python SDK Data
Scientists has easy access to thedata and functionality in Cognite
Data Fusion® in preferred development environment enablingadvanced analytics.

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

Code used in the project withdetailed README.md for using thescripts and continue contextualizing
going forward.

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →©COGNITE 2023 — COGNITE.COM

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Cognite SDK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, andscale many different use cases and applications,from those large, complex, and visually impressive
to more modest on the surface, focusing ondelivering actionable information to drive process
improvements.
An industrial digital twin is one tool that industrial

Industry standard tools

companies can use to reach a greater level ofunderstanding and control regarding their data andtheir operations, discovering insights to optimize
operations, increase uptime, and revolutionize
business models.
Historically, a digital twin has had a single dimensionof contextualization to solve the use case it was
specifically created to answer. When creating an

Low or no-code

Data science

industrial digital twin, the richness of the data
describing the industrial reality is critical to create
many more correlations between data sources.
An operational digital twin built with Cognite Data
Fusion® enables rapid scaling of tailored solutions
across domains ▼Industry applications
Infinitely scalable

Make data valuable
More easily governed

Cognite Data Fusion®

Make data available

WHY COGNITE DATA FUSION® →

DCS

27

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

Historian

BENEFITS FOR YOUR TEAMS →CONTACT SALES →

CMMS

MES

©COGNITE 2023 — COGNITE.COM

Make data
meaningful & useful

Accelerated time-to-value

The industrial digital twin must be dynamic, flexible
enough to meet the needs of a growing variety ofusers and models. This is why contextualization is
crucial to the creation of the industrial digital twin,
making it flexible enough and scalable enoughto handle the complexities of current operations
and to anticipate the demands of data-driven
operations in the digitalized industrial future.

Bring AI-software capabilities into the process
without replacing current applications and source
systems. Build, operationalize, and scale hundredsof use cases.

©COGNITE 2023 — COGNITE.COM

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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, and theability for data science teams to build predictive orprescriptive solutions.

Companies that deploy an industrial digital twin will
finally have true control over their data—the ability
to understand where it comes from, how reliable
it is, and how to enrich it over time. They will also
be the first ones to scale successful solutions on
top of that data, which must be the priority of any
digitalization initiative.

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Want to know more about our product?

Explore more insights from Cognite

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Discover how Cognite Data Fusion®
makes data more accessible andmeaningful, driving insights that
unlock opportunities in real-time,
reduce costs, and improve theintegrity and sustainability of youroperations.

Customer interviews and financial
analysis reveal an ROI of 400% andtotal benefits of $21.56M over three
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platform.

Discover our rich catalog of industry insights and technology deep
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WATCH NOW →GO TO STORIES →READ THE REPORT →READ OUR NEWEST BLOGS →©COGNITE 2023 — COGNITE.COM

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CUSTOMER STORIESPRODUCT TOURWHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →COGNITE.COM →