The Total Economic Impact™
Of Cognite Data Fusion
Cost Savings And Business Benefits
Enabled By Cognite Data Fusion
NOVEMBER 2022

A FORRESTER TOTAL ECONOMIC IMPACT™ STUDY COMMISSIONED BY COGNITE

Table Of Contents
Executive Summary ................................................. 1

Consulting Team: Russell Pask, Consultant
Jan Sythoff, Principal
Consultant

The Cognite Data Fusion Customer Journey ........ 5
Key Challenges ...................................................... 5
Solution Requirements ........................................... 6
Composite Organization ......................................... 6
Analysis Of Benefits ................................................ 7
Incremental Productivity Savings Due To
Improved SME Efficiency ....................................... 7
Reduced Shutdown Time ....................................... 9Real-Time Data Efficiencies ................................. 10
Optimized Planned Maintenance Program .......... 12
Energy Efficiency Savings .................................... 13
Optimization Of Heavy Machinery In
Continuous Production ......................................... 14
Unquantified Benefits ........................................... 16
Flexibility ............................................................... 16
Analysis Of Costs .................................................. 17
Implementation Costs ........................................... 17
Subscription Fees ................................................. 19
Operating Costs ................................................... 20
Financial Summary ................................................ 21
Appendix A: Total Economic Impact ................... 22
Appendix B: Endnotes .......................................... 23

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THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Executive Summary
Data operations adoption in industrial sectors lags behind retail, fintech, and others dueto the complexity of industrial data and unique requirements such as continuous realtime delivery, agile principles, and self-service. Industries like oil and gas, chemicals,
and manufacturing can lack access to data and thus the means to optimize workflows.
Cognite Data Fusion enables organizations to bring all data into a single repository,
contextualize it, and build use cases to improve the performance of their heavy assets.

Cognite Data Fusion is a platform that begins byingesting operational, engineering, and information

KEY STATISTICS

data from industrial assets into a single repository soorganizations are better able to build solutions andapplications to improve business outcomes.
Cognite Data Fusion then creates contextualized
data models that abstract away source systemcomplexity, enabling customers to quickly andreliably build new analytics dashboards, new

Return on investment (ROI)

Net present value (NPV)400%$21.56M

production solutions, and digital twins and to scalethese across industrial assets.
and were therefore unable to leverage data to
Cognite commissioned Forrester Consulting toconduct a Total Economic Impact™ (TEI) study andoptimize production, reduce costs, or improve
efficiency.

examine the potential return on investment (ROI)
enterprises may realize by deploying Cognite Data

However, prior attempts by these organizations to

Fusion.1 The purpose of this study is to provide

unify their complex industrial data had yielded limited

readers with a framework to evaluate the potential

success. Equally, interviewees were conscious of thefinancial impact of Cognite Data Fusion on theirpotential power of machine learning (ML) in

organizations.

combination with new workflows to transform andoptimize operations.

To better understand the benefits, costs, and risks
associated with this investment, Forrester interviewed

After the investment in Cognite Data Fusion, thesix representative customers with experience using

interviewees observed how siloed data became

Cognite Data Fusion. For the purposes of this study,

accessible in a business context and that their

Forrester aggregated the interviewees’ experiences

subject-matter experts (SMEs) were able to buildand combined the results into a single composite

better dashboards and develop better plans based onorganization with 30 large industrial facilities, 25,000

what the contextualized data revealed.

employees, and revenue of $2 billion per year.

Key results from the investment include major

Prior to using Cognite Data Fusion, interviewees

revenue gains and cost savings arising from

noted how their organizations were digitallysignificant improvements to production workflows,

immature, experiencing challenges with siloed data,

optimization of equipment use, reduction in shutdown
time, and improved maintenance planning.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

1EXECUTIVE SUMMARYKEY FINDINGS

Unquantified benefits. Benefits that provide value

Quantified benefits. Three-year, risk-adjusted

for the composite organization but are not quantified

present value (PV) quantified benefits for thein this study include:

composite organization include:••

Incremental productivity savings ofcustomer interviews that organizations mapping$1.5 million due to improved SME efficiency.

incidents to facilities could reduce the amount of

Cognite Data Fusion empowers industrial data

human movement through potentially dangerous

scientists by operationalizing and contextualizing“hot” areas, reducing risks to employee healthdata as well as by providing much greater dataand safety.

accessibility and visibility, transforming the way••

reporting. Some organizations were starting to

Benefits of $4.8 million arising from reduced

develop use cases to examine how to

shutdown time. The opportunity cost of large,

contextualize data to facilitate ESG reporting.

industrial assets being out of production is

Costs. Three-year, risk-adjusted PV costs for thesignificant. Using a digital twin and bettercomposite organization include:safely minimize shutdown periods when data•

both internal and external resources.
Real-time data access enables a 1%
improvement in productivity. Live data access

Subscription fees of $3.2 million. Cognite Data
Fusion has a subscription-based licensing model

making by increasing site safety, improving

centered on a range of factors including thepredictive maintenance, and raising machine

number of industrial sites, the types of data to be

performance.

fused, and the number of data fusion services
being deployed. Over the three-year study

Optimizing planned maintenance results increates contextualized data to optimize planned••

enhances operational flexibility and decision-savings of $4.3 million. Cognite Data Fusion•

Implementation costs of $241,000. Deployment
and integration of the Cognite platform requires

anomalies arise.

•

Environmental, social, and governance (ESG)

data scientists and SMEs are collaborating.

component data visibility, SMEs are able to•

Health and safety. There was evidence fromperiod, subscription fees total $3.2 million.
•

Operating costs of $2 million. These costs

maintenance by analyzing and interpreting

relate to the development of new use cases andavailable resources, workflows, and component

scaling proven solutions to new facilities.

lifecycles.

The representative interviews and financial analysis

Energy efficiency savings of $5.1 million.

found that the composite organization experiences

Intelligent data can be used to reduce energy use

benefits of $26.95 million over three years versus

and therefore operational costs.

costs of $5.4 million, adding up to a net present value(NPV) of $21.56 million and an ROI of 400%.

Benefit gains of nearly $9 million from
optimizing heavy machinery. The largest
benefit arising from the deployment of Cognite
Data Fusion is through the optimization of heavymachinery and industrial processes.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

2EXECUTIVE SUMMARYROI

BENEFITS PV

NPVPAYBACK400%$26.95M$21.56M

14 months

Benefits (Three-Year)
Incremental productivity savings due toimproved SME efficiency$1.5M

Revenue gains arising from shorter shutdown
timeReal-time data efficiencies

Optimized planned maintenance programme

Energy efficiency savings

$4.8M

$2.3M

$4.3M

$5.1M

Optimization of heavy machinery in continuous
production$9.0M“A very professional customer
service organization — working withme to make sure that we get themost out of Cognite Data Fusion. I
look at them much more like apartner than a supplier.”— VP, digitization, manufacturing

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

3EXECUTIVE SUMMARYTEI FRAMEWORK AND METHODOLOGY
From the information provided in the interviews,
Forrester constructed a Total Economic Impact™
framework for those organizations considering an
investment in Cognite Data Fusion.
The objective of the framework is to identify the cost,benefit, flexibility, and risk factors that affect theinvestment decision. Forrester took a multistep
approach to evaluate the impact that Cognite Data
Fusion can have on an organization.

DUE DILIGENCE
Interviewed Cognite stakeholders and Forrester
analysts to gather data relative to Cognite Data
Fusion.

INTERVIEWSInterviewed six representatives at organizations
using Cognite Data Fusion to obtain data withrespect to costs, benefits, and risks.

COMPOSITE ORGANIZATION
Designed a composite organization based on
characteristics of the interviewees’organizations.

FINANCIAL MODEL FRAMEWORK
Constructed a financial model representative of
the interviews using the TEI methodology and
DISCLOSURES
Readers should be aware of the following:This study is commissioned by Cognite and delivered byForrester Consulting. It is not meant to be used as acompetitive analysis.

risk-adjusted the financial model based on
issues and concerns of the interviewees.

CASE STUDY
Employed four fundamental elements of TEI in

Forrester makes no assumptions as to the potential ROI
that other organizations will receive. Forrester strongly
advises that readers use their own estimates within theframework provided in the study to determine theappropriateness of an investment in Cognite Data Fusion.

modeling the investment impact: benefits, costs,

Cognite reviewed and provided feedback to Forrester, but
Forrester maintains editorial control over the study and its
findings and does not accept changes to the study that
contradict Forrester’s findings or obscure the meaning of
the study.

provides a complete picture of the total

flexibility, and risks. Given the increasing
sophistication of ROI analyses related to IT
investments, Forrester’s TEI methodology

economic impact of purchase decisions. Please
see Appendix A for additional information on the
TEI methodology.

Cognite provided the customer names for the interviews
but did not participate in the interviews.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

4The Cognite Data Fusion Customer Journey
Drivers leading to the Data Fusion investment

Interviews
Role

Industry

Headquarters

Revenue

Product manager

Oil and gas

Europe$250 billion

VP, digitization

Manufacturing

Europe$275 million

Senior analyst

UtilityEurope$3 billion

Data analytics manager

Oil and gas

US$7 billion

Senior VP and VP, digitalization

Marine geophysics

Europe$600 million

VP, data governance

Oil and gas

Europe$6 billion

KEY CHALLENGES“With any type of planned
downtime event, with Cognite,
we have been able to reduce thetime by 20% by identifying
opportunities for simultaneous
operations during shutdowns.”

Prior to investment in Cognite Data Fusion, almost allof the interviewees’ said that their organizations had
multiple repositories for operational data. Typically,
they had a large array of siloed data systems, andonly some of this data could be analyzed in individual
reporting systems.
In general, the interviewees’ organizations had

Data analytics manager, oil and gas

attempted to develop their own industrial data
analytics capability internally and realized that thetask was much more difficult than they had firstsuspected. Even digitally mature organizations
struggled with how to automate and scale theseapproaches using machine learning.
The interviewees experienced some common
challenges, including:••Lack of data contextualization. Interviewees
reported that their information sits in lots of datasilos. This often means time-consuming manual
processes are required to perform any kind ofanalysis. A key challenge for these organizations

Multiple data repositories. Industrial data is

was not only to bring this data together but todifficult to access and, therefore, underutilized.

combine different data sources to provide new

The older, slower machinery often found in heavy

insights driven by algorithms and automation.

industry runs on legacy or idiosyncratic systems

None of the interviewees’ organizations had beenthat do not provide highly accessible, normalized

able to do this; they particularly struggled indata that can be used in an integrated fashion.

bringing together structured and unstructured
data.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

5THE COGNITE DATA FUSION CUSTOMER JOURNEY•

Incomplete dashboards and analytics. Modern
industrial processes combine physical systems incomplex workflows: data needs to operate
frictionlessly so workflows can be optimized.
Opportunities for improving processes andimproving safety are invisible to organizations
with incomplete, unintegrated data.

SOLUTION REQUIREMENTS
The interviewees’ organizations searched for asolution that could:•

Provide a single operational, data-focused
repository.

•

Contextualize all their industrial data.

•

Improve data visibility and accessibility.

•

Automate and scale across industrial facilities.

COMPOSITE ORGANIZATION
Based on the interviews, Forrester constructed a TEI
framework, a composite company, and an ROI
analysis that illustrates the areas financially affected.
The composite organization is representative of thesix interviews, and it is used to present the aggregate
financial analysis in the next section. The composite
organization has the following characteristics:
Description of composite. This global, B2B
manufacturing organization with revenues of$2 billion provides chemicals and industrial materials
to wholesalers. The composite organization has astrong brand, global operations including 30 coreassets, and a large business base of 10 million
customers.

Key Assumptions• Global industrial
company• $2 billion annual
revenue• 30 large industrial
assets• Unable to use industrial
data at scale

Initially, the composite’s internal team and the
Cognite team work together to liberate operational
technology, engineering technology, and IT data fromsilos in the flagship facility and ingest it into the
Cognite Data Fusion platform. From there, the work
shifts to building, and testing use cases on theflagship site. Once use cases are successfully
deployed, teams look to scale them across thecomposite real estate.
In the first year, the focus is on building the use
cases and testing them on the flagship asset — aseach use case is fully tested before the team looks tobuild it out to 19 more facilities. In the second year,
the benefits begin to be more fully realized, and use
cases are now scaled across 25 facilities. In
particular, the work on shorter shutdowns, real-time
data, and improved productions workflows comes tofruition. These benefits along with energy efficiency

Deployment characteristics. The composite has 30

and improved maintenance are scaled out to 30 large

large industrial facilities distributed across the globe

assets in Year 3.

where it processes chemicals and industrial
materials. The approach taken for the deployment is
to start by looking at the high-potential use cases andworkflows and to build the test use cases for these on
one flagship facility.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Alongside this, the internal team builds its skills andunderstanding of the Cognite Data Fusion solutions
architecture and therefore its ability to build anddeploy new industrial use cases.

6

Analysis Of Benefits
Quantified benefit data as applied to the composite

Total Benefits
Ref.

BenefitYear 1

Year 2

Year 3

Total

Present
Value

Atr

Incremental productivity savings due toimproved SME efficiency$450,000$618,750$810,000$1,878,750$1,529,020

Btr

Reduced shutdown time$0$2,500,000$3,600,000$6,100,000$4,770,849

Ctr

Real-time data efficiencies$0$1,319,063$1,587,600$2,906,663$2,282,922

Dtr

Optimized planned maintenanceprogram$0$0$5,670,000$5,670,000$4,259,955

Etr

Energy efficiency savings$0$0$6,804,000$6,804,000$5,111,946

Ftr

Optimization of heavy machinery incontinuous production$0$5,025,000$6,451,200$11,476,200$8,999,775$450,000$9,462,813$24,922,800$34,835,613$26,954,467

Total benefits (risk-adjusted)

INCREMENTAL PRODUCTIVITY SAVINGS DUE

a substantial effect on the bottom line — especially in

TO IMPROVED SME EFFICIENCY

large-scale industrial environments.

Evidence and data. Industrial data operations often

Another interviewee described how information silos

take place in hazardous environments where data isresulted in the creation of manual workarounds

siloed with inadequate dashboards. Many firms stillconnecting time series data with site meter readings

extensively use paper, have poor connectivity, andor production data. Greater data visibility as well as

use legacy processes based on operations.

the opportunity to automate these processes had a

Cognite Data Fusion empowers industrial data
scientists by operationalizing and contextualizing
data and by simplifying complex data pipelines. The

significant impact on the effectiveness of industrial
data scientists to provide key insights and for
operations teams to work in more optimal fashion.

first benefit of Cognite Data Fusion is to makeOnce the SMEs and data scientists began to harness

opaque, siloed data visible in an integrated way forthe power of the data, they were able to apply it to

SMEs.

multiple challenges such as shutdowns, planned

Some interviewees said that a key reason forinvesting in Cognite Data Fusion was that it would
enable their digital transformation by making
disparate data visible and highly available.
Others began from a position of analyzing theirsignificant known levels of unproductive time andmaintenance, health and safety, heavy equipment
optimization, workflow optimization, and energy
efficiency.
Modeling and assumptions. Based on the interview
data, Forrester assumes the following:•

The model is based on a three-year deployment

understanding that if they could unlock this, it could

scaling from 20 large assets in Year 1 to 30 in

make their operational and development teams much

Year 3.

more effective. Incremental improvements can have

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

7

ANALYSIS OF BENEFITS••

The blended salary costs of the SME team per

Risks. The risks that can potentially impact thislarge asset per day is $5,000.

benefit are:

The composite experiences 10 days’ worth of•Issues with legacy industrial IT systems.

•

The degree of information compatibility amongproductivity savings per asset in Year 1, rising to
12 days in Year 3.

assets.
•“With Cognite, we can see a 3Dmodel of the facility and tie that
into a database that monitors thecondition of each piece ofequipment.”

Issues with the range of different operational datasystems to be integrated.

Results. To account for these risks, Forrester
adjusted this benefit downward by 10%, yielding athree-year, risk-adjusted total PV (discounted at 10%)of $1.5 million.

Data analytics manager, oil and gas

Incremental Productivity Savings Due To Improved SME Efficiency
Ref.

MetricSourceYear 1

Year 2

Year 3

A1

Number of large assets using Cognite Data Fusion

Composite

20

25

30

A2

Average cost of SME team per large asset per day

Interviews$5,000$5,000$5,000A3

Productivity gain from an extra days SME output arisingfrom Cognite Data Fusion implementation

A1*A2$100,000$125,000$150,000

A4

Number of extra days productivity gained through greater
data availability

Interviews

10

11

12

A5

Percentage of productivity recaptured

TEI standard

50%

50%

50%

At

Incremental productivity savings due to improved SME
efficiency

A3*A4*A5$500,000$687,500$900,000

Risk adjustment↓10%$450,000$618,750$810,000

Atr

Incremental productivity savings due to improved SME
efficiency (risk-adjusted)
Three-year total: $1,878,750

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Three-year present value: $1,529,020

8ANALYSIS OF BENEFITSREDUCED SHUTDOWN TIME•

This use case is implemented from the end of the

Evidence and data. A key performance indicator for

first year, scaling from 25 large assets in Year 2

industrial data operations is how much time assets

to 30 in Year 3.

spend in production. The opportunity cost of large,•

industrial assets being out of production is significant

For the composite, the average revenue gain per
day from a large asset is $200,000.

and an essential part of the successful managementof industrial installations. Leadership, operations•

In Year 2, it is possible for five days to be gained

teams, and industrial data scientists are all looking for

per asset through optimizing shutdowns. In Yearways to safely reduce the necessary amount of

3, this rises to six days.

shutdown time to stay at full production for as long as

Risks. Revenue from reduced shutdown times will

possible.

vary from organization to organization with respect to:

One interviewee said that they used Cognite Data•

Productivity of the asset.

•

The level of the shutdown overhaul and its

Fusion to find opportunities to conduct simultaneous
operations during shutdowns to potentially deliver acomplexity.

20% time savings. Another described how they could
better monitor individual pieces of equipment and so

Results. To account for these risks, Forrester

reduce the risk of failure.

adjusted this benefit downward by 20%, yielding a

Modeling and assumptions. Based on the interview

three-year, risk-adjusted total PV of $4.8 million.

data, Forrester makes the following assumptions:

Reduced Shutdown Time
Ref.

MetricSourceB1

Number of large assets using Cognite Data Fusion

B2

Year 2

Year 3Composite

25

30

Average revenue gain per day of large asset operation

Composite$200,000$200,000

B3

Total productivity gain per day of large assets operation

B1*B2$5,000,000$6,000,000

B4

Number of extra days of operation from shutdown tasks

Interview data

5

6

B5

Operating margin

TEI standard

12.5%

12.5%

Bt

Reduced shutdown timeB3*B4*B5$0$3,125,000$4,500,000

Risk adjustment

↓20%
$0

$2,500,000$3,600,000

Btr

Reduced shutdown time (risk-adjusted)
Three-year total: $6,100,000

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Year 1Three-year present value: $4,770,849

9

ANALYSIS OF BENEFITSREAL-TIME DATA EFFICIENCIES“First and foremost, they
understood our problem. It
wasn't about selling a service —it was about solving yourproblems.”

Evidence and data. Real-time data visibility istransformative for organizations that have struggledwith data silos. Live data access enhances
operational decision-making, therefore increasing site
safety, predictive maintenance, and machine
performance. One interviewee described how theavailability of real-time data enabled by Cognite Data

Senior VP, digitalization, marine
geophysics

Fusion supported superior training of their machinelearning model in production, which led to increased
performance levels in production processes. Anotherexplained how the insurance risk associated with
their organization’s heavy equipment was reduced•

significantly by being able to provide their insurer withlive data.

In Year 2, each asset is open, on average, for
335 days and in Year 3 for 336 days.

•

Forrester assumes a 1% gain in productivity.

•

The operating margin is 12.5%.

“We are saving $1 million
because we now have real-timedata on equipment deployment.”•

Seventy percent of the real-time data gains are

Senior VP, digitalization, marine
geophysics

organization to organization with respect to:Modeling and assumptions. Based on the interview
data, Forrester makes the following assumptions:•This use case is implemented from Year 2 on 25

attributable to Cognite Data Fusion.
Risks. Real-time data efficiencies will vary from•

Average level of asset productivity.

•

Revenue per large asset.

•

Operating margin.

Results. To account for these risks, Forrester
adjusted this benefit downward by 10%, yielding athree-year, risk-adjusted total PV of $2.3 million.

large assets and scaled to all 30 in Year 3.
•

For the composite, the average revenue per day
from a large asset is $200,000.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

10ANALYSIS OF BENEFITS

Real-time Data Efficiencies
Ref.

MetricSourceC1

Number of large assets using Cognite Data Fusion

C2

Year 2

Year 3Composite

25

30

Average productivity per day per large asset

Composite$200,000$200,000

C3

Average number of days large asset remains in production

Interviews

335

336

C4

Total annual revenue of large assets using Cognite Data
FusionC1*C2*C3$1,675,000,000$2,016,000,000

C5

Productivity gain from real-time data efficiencies

Interviews

1%

1%

C6

Total productivity gains from real-time data efficiencies
across large assets

C4*C5$16,750,000$20,160,000

C7Operating profit margin

TEI standard

12.5%

12.5%

C8

Productivity gains attributable to Cognite Data Fusion

Interviews

70%

70%

Ct

Real-time data efficiencies

C6*C7*C8$0$1,465,625$1,764,000

Risk adjustment

↓10%
$0

$1,319,063$1,587,600

Ctr

Real-time data efficiencies (risk-adjusted)
Three-year total: $2,906,663

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Year 1Three-year present value: $2,282,922

11

ANALYSIS OF BENEFITSOPTIMIZED PLANNED MAINTENANCE PROGRAM•

Optimizing planned maintenance comes
onstream in Year 3 across all 30 large assets.

Evidence and data. Cognite Data Fusion creates
contextualized data that enables SMEs to optimize•

For the composite, the average revenue per day

planned maintenance through reviewing available

from a large asset is $200,000 and in Year 3, on

resources, workflows, and component lifecycles.

average, the assets are open for 336 days.

Industrial data scientists can model what will happen•

using a digital twin of the facility setup to see how

The composite achieves a gain of 5%.

they can bring down a particular subset of the overall

Risks. The likely risks in reaching optimized

workflow and continue production.

maintenance planning are:

One interviewee noted that since using Cognite Data•

Variation in the productivity of large assets.

•

The avoided loss available in output is lower

Fusion to contextualize data, their large-asset
downtime had dropped from 34% to 4% by utilizingthese kinds of processes. Others agreed that a 5%

because production is more effective than

avoided loss for this use case was typical. Anotherpredicted.

described efficiencies from leveraging predictive

Results. To account for these risks, Forrester

maintenance on key assets.

adjusted this benefit downward by 10%, yielding athree-year, risk-adjusted total PV of $4.3 million.

Modeling and assumptions. Based on the interview
data, Forrester makes the following assumptions:

Optimized Planned Maintenance Program
Ref.

MetricSourceD1

Number of large assets using Cognite Data Fusion

Composite

30

D2

Average revenue per day per large asset

Composite$200,000

D3

Average number of days each large asset remains in
production

Interviews

336

D4

Avoided loss in output

Interviews

5%

D5

Total avoided lost revenue

D1*D2*D3*D4$100,800,000

D6Operating profit margin

TEI standard

12.5%

D7

Avoided loss attributable to Cognite Data Fusion

Assumption

50%

Dt

Optimized planned maintenance programD5*D6*D7

Risk adjustment

↓10%

Dtr

Optimized planned maintenance program (riskadjusted)
Three-year total: $5,670,000

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Year 1

Year 2

Year 3$0$0$6,300,000$0$0$5,670,000

Three-year present value: $4,259,955

12

ANALYSIS OF BENEFITS

ENERGY EFFICIENCY SAVINGS•

A large asset, based in the US, will consume

Evidence and data. Large industrial units use$10,000 of energy per day and remains in

considerable amounts of power and heat. Modern

production for 336 days.

industrial equipment and work practices offer

Risks. The main risks affecting the possibility ofsignificant potential energy efficiency savings,

energy saving efficiencies are as follows:

particularly in the face of high and volatile energy•

pricing.

Assets with smaller power requirements thanthose described in the composite and that are not

In interviews, customers said that they typicallyin active production for as many days.

achieved energy savings of 15% with Cognite Data•

Fusion.

Price volatility in the gas and oil production
markets.

One interviewee used Cognite Data Fusion data•

contextualization to examine the optimum speed forInability to capture savings because peak
production aligns to peak energy pricing.

their large sea vessels to save money on engine fuelconsumption, which was then scaled to the entire

Results. To account for these risks, Forrester

fleet of 78 vessels.

adjusted this benefit downward by 10%, yielding a

Modeling and assumptions. Based on the interview

three-year, risk-adjusted total PV of more thandata, Forrester makes the following assumptions:$5.1 million.

•

The composite sees energy efficiency savings
beginning in Year 3 across all 30 large assets.

Energy Efficiency Savings
Ref.

MetricSourceE1

Number of large assets using Cognite Data Fusion

Composite

30

E2

Average energy costs per large asset per day

Composite$10,000

E3

Average number of days each large asset remains in
production

Interviews

336

E4

Average savings from increased energy efficiency

Interviews

15%

E5

Proportion of energy savings attributable to Cognite
Data Fusion

Assumption

50%

Et

Energy efficiency savings

E1*E2*E3*E4*E5

Risk adjustment

↓10%

Etr

Energy efficiency savings (risk-adjusted)
Three-year total: $6,804,000

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Year 1

Year 2

Year 3$0$0$7,560,000$0$0$6,804,000

Three-year present value: $5,111,946

13

ANALYSIS OF BENEFITSOPTIMIZATION OF HEAVY MACHINERY IN•

A 15% productivity gain is possible in Year 2,
increasing to 16% in Year 3.

CONTINUOUS PRODUCTION
Evidence and data. The largest benefit arising from

Risks. The key factors likely to dampen the benefits

the deployment of Cognite Data Fusion is through theof heavy machinery optimization in continuous

optimization of heavy machinery and industrial

production are:processes. The longer an asset is in production andoperating effectively, the greater the revenues and•level.

margins will be from that asset.
Interviewees observed important improvements inperformance from the combination of heavymachinery and machine learning. In one case, an

An asset that is already performing at a high•An asset that has fewer opportunities to calibrate
the interactions of heavy machinery along its
workflow.

interviewee described that by using ML and real-time

Results. To account for these risks, Forrester

data, they were able to optimize performance to theadjusted this benefit downward by 20%, yielding a

best levels previously achieved. Other interviewees

three-year, risk-adjusted total PV of nearlyhighlighted how the improvement in data visibility$9.0 million.

meant that the SMEs and data scientists were able tooptimize processes and settings, which kept
production running longer.
Modeling and assumptions. Based on the interview
data, Forrester makes the following assumptions:•

The model is based on a two-year scaling
process, from 25 large assets in Year 2 to 30 inYear 3.

•For the composite, the average revenue per day
from a large asset is $200,000 and in Years 2
and 3, on average, each asset is open for 335
and 336 days, respectively.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION“The drilling speed rate ofpenetration — it's the weight on
the drill, how quickly you're
turning the bit, and the floor rate.
So you can vary those three[with Cognite Data Fusion], andthat way we get the best
performance.”
Product manager, oil and gas

14ANALYSIS OF BENEFITS

Optimization Of Heavy Machinery In Continuous Production
Ref.

MetricSourceF1

Number of large assets using Cognite Data Fusion

F2

Year 2

Year 3Composite

25

30

Average productivity gain per day of large asset operation

Composite$200,000$200,000

F3

Average number of days large asset remains in
production

Interviews

335

336

F4

Total annual revenue of large assets using Cognite Data
FusionF1*F2*F3$1,675,000,00
0$2,016,000,000

F5Productivity improvement

Interviews

15%

16%

F6

Productivity gains

F4*F5$251,250,000$322,560,000

F7Operating profit margin

TEI standard

12.5%

12.5%

F8

Total production gain attributable to Cognite Data Fusion

Interviews

20%

20%

Ft

Optimization of heavy machinery in continuous production

F6*F7*F8$0$6,281,250$8,064,000

Risk adjustment

↓20%
$0

$5,025,000$6,451,200

Ftr

Optimization of heavy machinery in continuous production(risk-adjusted)
Three-year total: $11,476,200

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Year 1Three-year present value: $8,999,775

15

ANALYSIS OF BENEFITSUNQUANTIFIED BENEFITS

designed to work with the full range of industrial

Interviewees mentioned the following additionalorganizations and is compatible with all common

benefits that their organizations experienced but were

third-party apps; it also works with end-user

not able to quantify:

apps, bespoke apps, and its own out-of-the-box•

Cognite applications.
Health and safety. By using Cognite Data
Fusion, some interviewees reported that theirFlexibility would also be quantified when evaluated asorganizations are able to map incidents and near-part of a specific project (described in more detail in

misses on large assets which could reduce the

Appendix A).

amount of human movement through potentially
dangerous hot areas, reducing risks toemployees.

reporting.

“We were able to reduce thenumber of people and the timethey spent in potentiallyhazardous areas.”

FLEXIBILITY

VP, data governance, oil and gas•

ESG reporting. Some organizations had begun
to develop use cases to examine how they mightuse contextualized data to help with ESG

The value of flexibility is unique to each customer.
There are multiple scenarios in which a customer
might implement Cognite Data Fusion and later
realize additional uses and business opportunities,
including:•Flexibility with source data and data
contextualization. Cognite Data Fusion
combines a wide range of different industrial datatypes — structured and unstructured data, timeseries, IoT data, ERP, events, work orders, 3D,
and photogrammetry from a wide range ofcommon platforms. The Cognite Data Fusion
platform contextualizes all this data using acommon, flexible data model. New data is
continuously contextualized, providing manypotential additional use cases and opportunities
to leverage such rich data for machine learningand scaling across assets.

•

Flexibility in tools and services. Cognite Data
Fusion is an open architecture combining
3D/CAD visualization, digital dashboards,
software development kits (SDKs), low-code
development for use cases for SMEs, and aphysics simulator. Cognite Data Fusion is

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

16Analysis Of Costs
Quantified cost data as applied to the composite

Total Costs
InitialYear 1

Year 2

Year 3

Total

Present
Value$240,768

$0

$0

$0

$240,768$240,768

Subscription fees

$0$682,500$1,155,000$2,100,000$3,937,500$3,152,761

Total operating costs$0$698,500$918,500$808,500$2,425,500$2,001,529$240,768$1,381,000$2,073,500$2,908,500$6,603,768$5,395,058

Ref.

Cost

Gtr

Implementation costs

Htr
Itr

Total costs (risk-adjusted)

IMPLEMENTATION COSTS•

Forrester assumes a fully burdened blended
hourly rate for members of the implementation

Evidence and data. Implementing Cognite Datateam of $57 per hour.

Fusion involved collaboration between the clientorganization and the Cognite team to deploy and

Risks. There are a range of risk factors that will

integrate the Cognite Data Fusion platform.

affect the implementation of Cognite Data Fusion:

Given the challenges and complexity of industrial•

The greater the number of facilities where data isdata, interviewees said that it took time to prepare

being ingested, the more chance there is of

their large assets for data ingestion with Cognite Data

experiencing challenges with legacy systems.

Fusion.

•industrial data systems on the large assets.

Modeling and assumptions. The implementation
costs for the organization are composed both of the

The complexity and digital maturity of the•

The size of the large assets.

internal effort to prepare assets for integration as wellas the integration process itself.
•

Results. To account for these risks, Forrester
adjusted this cost upward by 10%, yielding a three-

Based on interview data, costs related to internal
development effort are estimated at six FTEs foryear, risk-adjusted total PV (discounted at 10%) of$241,000.

planning, coordination, and integration for aperiod of four months.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

17

ANALYSIS OF COSTS

Implementation Costs
Ref.

MetricSourceInitialG1

Total number of FTEs required for implementation

Interviews

6

G2

Average fully burdened hourly salary for developers
building integrations

TEI standard

$57

G3

Developer hours for implementation

Interviews

640

Gt

Implementation costs

G1*G2*G3

Risk adjustment

↑10%

Gtr

Implementation costs (risk-adjusted)
Three-year total: $240,768

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

Year 1

Year 2

Year 3$218,880

$0

$0

$0

$240,768

$0

$0

$0

Three-year present value: $240,768

18

ANALYSIS OF COSTSSUBSCRIPTION FEES•

The subscription over the three-year period for

Evidence and data. Cognite Data Fusion has a

the composite was based on implementation on

subscription-based licensing model based on a range

20 large assets in Year 1, 25 in Year 2, and all 30

of factors including the number of large assets (sites),in Year 3.

the number of tags used by clients, the types of data•

to be fused, and the number of data fusion services

The subscription price is based on the composite
using all data fusion services and all data types.

deployed. The data fusion services in the coreplatform include application frameworks, manage andRisks. Forrester understands that subscription fees

configure data, explore and build, contextualize data,

may vary markedly by organization, and the best way

and data integration.

to determine the costs is to speak directly with a
Cognite representative. Factors to consider when•Interviewed customers said that the subscription

estimating subscription fees include:

provided them with data contextualization thatcreated relationships between a range of siloeddata sources including time series data, ERP andwork orders, 3D/CAD, and IoT logs.
•

Customers said that the subscription also
provided them with Cognite training, SDKs and•Number of large assets.

•

Number of tags used.

•

Data fusion services required.

•

Types of data for ingestion.

open APIs, and solution support, as well as

Results. To account for these risks, Forrester

dashboard creation for SMEs.

adjusted this cost upward by 5%, yielding a three-

Modeling and assumptions. Given the interviewee

year, risk-adjusted total PV of $3.2 million.

responses, Forrester makes the following
assumptions:

Subscription Fees
Ref.

MetricSourceH1

Subscription fees

Interviews

Ht

Subscription fees

Interviews

Risk adjustment

↑5%

Htr

Subscription fees (risk-adjusted)
Three-year total: $3,937,500

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

InitialYear 1

Year 2

Year 3$650,000.00$1,100,000.00$2,000,000.00$0$650,000$1,100,000$2,000,000$0$682,500$1,155,000$2,100,000

Three-year present value: $3,152,761

19

ANALYSIS OF COSTSOPERATING COSTS

Modeling and assumptions. Based on interview

Evidence and data. Using Cognite Data Fusion

responses, Forrester makes the following

introduced a set of ongoing costs for organizations toassumptions:develop new use cases and scale proven solutions to•

It takes two FTEs at the composite to manage

further large assets. These additional costs took theand maintain the integration, the use cases, andform of management and oversight of the work withthe scaling across assets.

Cognite and not the delivery team, who would be•working on data operations solutions regardless.

The average fully burdened cost per year foreach member of the oversight and software

Typically, this delivery team working on new use

development team is $100,000.

cases and scaling production will be a team of six toeight FTEs, including project lead, link to SME

Risks. The operating costs will vary in terms of theoperations, software developers, industrial data

complexity of use cases, the range of data, the digitalscientists, industrial data analysts, and software

maturity, and the sheer scalability of the large assets.

engineers. This team will be working full-time in agile•

The complexity of the use cases may lead tosprints on use cases viewed through the prism ofchallenges in being able to transform data quickly

Cognite Data Fusion.

and in an organized fashion.

From the interviews, customers also highlighted that•

The more types of different data that are beinga key part of the operating costs were ongoingingested and transformed in the data fusion

professional services from Cognite. These

process, the more involved the organizational

professional services could include developing

and operational delivery will be.

challenging use cases, work around scalability or•

support for additional operational services like

The digital maturity of data systems on large
assets will affect data ingestion and integration.

industrial robots.

Results. To account for these risks, Forrester
adjusted this cost upward by 10%, yielding a threeyear, risk-adjusted total PV of $2 million.

Operating Costs
Ref.

MetricSourceI1

Developer FTEs maintaining and managing with
Cognite Data Fusion

Interviews

I2

Average fully burdened annual salary for adeveloper salary (fully burdened)

I3

Year 1

Year 2

Year 3

2

2

2

TEI standard$100,000$100,000$100,000

Total cost of FTEs maintaining and managing
Cognite Data Fusion

I1*I2$200,000$200,000$200,000

I4

Professional services

Interviews$435,000$635,000$535,000

It

Operating costs

I3+I4

$0

$635,000$835,000$735,000

Risk adjustment

↑10%
$0

$698,500$918,500$808,500

Itr

Operating costs (risk-adjusted)
Three-year total: $2,425,500

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

InitialThree-year present value: $2,001,529

20

Financial Summary
CONSOLIDATED THREE-YEAR RISK-ADJUSTED METRICS

Cash Flow Chart (Risk-Adjusted)

Cash
flows

Total costs

Total benefits

Cumulative net benefits$30.0 M

$25.0 M

The financial results calculated in the
Benefits and Costs sections can beused to determine the ROI, NPV, andpayback period for the composite
organization’s investment. Forrester
assumes a yearly discount rate of 10%
for this analysis.

$20.0 M

$15.0 M

These risk-adjusted ROI,
NPV, and payback period
values are determined byapplying risk-adjustment
factors to the unadjusted
results in each Benefit andCost section.

$10.0 M

$5.0 M-$5.0 M
InitialYear 1

Year 2

Year 3

Cash Flow Analysis (Risk-Adjusted Estimates)
InitialYear 1

Year 2

Year 3

Total

Present
Value($240,768)($1,381,000)($2,073,500)($2,908,500)($6,603,768)($5,395,058)

Total benefits$0$450,000$9,462,813$24,922,800$34,835,613$26,954,467

Net benefits($240,768)($931,000)$7,389,313$22,014,300$28,231,845$21,559,409

Total costs

ROI
Payback period(months)

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

400%
14

21

Appendix A: Total Economic
ImpactTotal Economic Impact is a methodology developed

PRESENT VALUE (PV)by Forrester Research that enhances a company’s

The present or current value oftechnology decision-making processes and assists(discounted) cost and benefit estimatesvendors in communicating the value proposition of

given at an interest rate (the discounttheir products and services to clients. The TEIrate). The PV of costs and benefits feed

methodology helps companies demonstrate, justify,

into the total NPV of cash flows.

and realize the tangible value of IT initiatives to bothsenior management and other key businessstakeholders.

NET PRESENT VALUE (NPV)TOTAL ECONOMIC IMPACT APPROACH

The present or current value ofBenefits represent the value delivered to the(discounted) future net cash flows given

business by the product. The TEI methodology

an interest rate (the discount rate). A

places equal weight on the measure of benefits andpositive project NPV normally indicatesthe measure of costs, allowing for a full examinationthat the investment should be madeof the effect of the technology on the entire

unless other projects have higher NPVs.

organization.
Costs consider all expenses necessary to deliver the

RETURN ON INVESTMENT (ROI)

proposed value, or benefits, of the product. The cost
category within TEI captures incremental costs overA project’s expected return inthe existing environment for ongoing costs

percentage terms. ROI is calculated byassociated with the solution.

dividing net benefits (benefits less costs)
by costs.

Flexibility represents the strategic value that can beobtained for some future additional investmentbuilding on top of the initial investment already made.

DISCOUNT RATE

Having the ability to capture that benefit has a PV
that can be estimated.

The interest rate used in cash flow
analysis to take into account theRisks measure the uncertainty of benefit and costtime value of money. Organizations

estimates given: 1) the likelihood that estimates will

typically use discount rates between

meet original projections and 2) the likelihood that

8% and 16%.

estimates will be tracked over time. TEI risk factors
are based on “triangular distribution.”
PAYBACK PERIOD
The initial investment column contains costs incurred at “time0” or at the beginning of Year 1 that are not discounted. Allother cash flows are discounted using the discount rate at theend of the year. PV calculations are calculated for each total
cost and benefit estimate. NPV calculations in the summary
tables are the sum of the initial investment and thediscounted cash flows in each year. Sums and present value
calculations of the Total Benefits, Total Costs, and Cash Flowtables may not exactly add up, as some rounding may occur.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

The breakeven point for an investment.
This is the point in time at which net
benefits (benefits minus costs) equal

initial investment or cost.

22Appendix B: Endnotes
1 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s

technology decision-making processes and assists vendors in communicating the value proposition of theirproducts and services to clients. The TEI methodology helps companies demonstrate, justify, and realize thetangible value of IT initiatives to both senior management and other key business stakeholders.

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

23

THE TOTAL ECONOMIC IMPACT™ OF COGNITE DATA FUSION

24