A Forrester Consulting
Thought Leadership Paper
Commissioned By Cognite
February 2020

Contextualized Data And
Digital Twins Amplify
Digitization Value
Operationalize And Scale Models And
Applications With Trusted Industrial Data

Table Of Contents
1 Executive Summary
2 The Digitization Of Industrial Firms
Is Underway
6 Inexperience, Poor Data Quality,
And The IT/OT Divide Hinder
Digitization Progress
9 Secure Your Digital Future With A
Top-Down Approach From Senior
Leaders
11 Key Recommendations
12 Appendix

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Forrester’s Infrastructure &Operations research group

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Executive Summary
Industrial firms are forced to change the way they engage withcustomers. These new operational considerations are driving
investment in early industrial internet of things (IoT) and digitalmanufacturing.

Datacontextualization is
becoming crucial
to make informed
business decisions.

To thrive in today’s business environment, firms must move beyond
just automating existing and siloed business operations and become
software-proficient, if not software-native. This hinges on providing
data scientists, professional and citizen developers, and engineers withaccess to contextual, meaningful data at their moment of need.
Leaders in industrial firms must encourage shared responsibility acrossIT and OT teams, fostering collaboration and shared responsibility forboth physical and digital experiences. Solutions need to transcend
the organizational silos and legacy systems on which they’re built insupport of clearly articulated customer outcomes.
Digital twin functionality offers an approach to ensure data and insightsfrom multiple systems are shareable and can be acted on. Digital twins
rely on tools and services that fuse data, leverage advanced analytics,
improve visualization, and enable machine learning (ML) to support newuse cases that drive agility and value.
In October 2019, Cognite commissioned Forrester Consulting toevaluate adoption of contextualized data and digital twin in heavy asset
industries. To explore this topic, Forrester conducted an online survey
with 160 decision makers in IT and operations roles at global industrial
companies in oil and gas, manufacturing, transportation, and utilities.
We found that as digitization is increasing, data contextualization is
becoming crucial to make informed business decisions. But firms
struggle due to a shortage of digital skills, silos, and lack of expertise.
KEY FINDINGS› Data is key to digitization, but extracting insights remains
challenging. Over 80% of firms recognize the importance ofindustrial data in driving their business decisions and innovation.
Despite this, 83% experience challenges with using data to deliverinsights across the organization.
› Lack of data skills and IT/OT separation hinder firms’ digitization
progress. Unavailability of qualified digital talent is slowing down
the pace of digital transformation. This is a reality for 75% of IT andOT decision makers in our study. To make matters worse, available
resources (IT and OT) struggle to collaborate, creating further hurdles
to achieving digital initiatives.
› Artificial intelligence (AI) and machine learning boost innovation.
Firms show high interest in leveraging tools with advanced analytics,
visualization, and ML to support new digital use cases that drive
agility. Digital twin initiatives attempt to combine new techniques fromML with established physics-based modeling and simulation.1

1 | Contextualized Data And Digital Twins Amplify Digitization Value

The Digitization Of Industrial Firms Is
Underway
Economic uncertainty and political change are constants in today’s
world. This sort of market volatility has implications far and wide. For
almost 80% of industrial firms, it means changing how they engage withcustomers and how they operate. In turn, that drives early industrial IoT
and digital manufacturing investment.
By surveying 160 decision makers in IT and OT roles at global industrial
companies, we found that firms are:› Thinking strategically about their priorities. Most industrial firms have
expanded their top priorities beyond just operational (improving margins
and reducing costs) to also include strategic initiatives like becoming
data-driven (88%), differentiating customer experiences (88%), andchanging business models towards suppliers or customers (85%) (seeFigure 1).
› Ramping investment in digital. Whether it’s automating plant
processes, improving data tools for data scientists or app developers,
investing in connected products (new and old), or optimizing operations
with ML and AI, industrial firms are actively investing across digital
initiatives (see Figure 2).

Industrial firms arefocusing on morestrategic priorities,
investing in digital
initiatives, andutilizing advanced
analytics.

› Recognizing the value of advanced analytics. Three-quarters ofIT and OT decision makers report that data is critical or important inachieving their digital initiatives. More specifically, over 80% highlight theimportance of industrial data in driving business decisions and innovation,and using ML and data science to power future competitiveness.

Figure 1“How much of a priority are the following for your company?”High priority

Critical priority

Improve margins

42%

Reduce costs

36%

Become data-driven

54%

Differentiate customer experiences

47%

Change business models towards suppliers or customers

54%

Increase automation

40%

Connect siloed business operations

48%

Establish data science center of excellence

41%

Empower citizen data scientist

46%

48%
52%
34%
41%
31%
44%
32%
38%
27%

Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 2019

2 | Contextualized Data And Digital Twins Amplify Digitization Value

Figure 2“What are your company’s plans to invest in the following digital initiatives?”
Interested but no plans to invest
Planning to invest in the next 12 months
Invested, not expanding/upgrading
Expanding or upgrading investment
6%
24%

20%

9% 14%

26%

Data tools to derive and combine insights from ourmachines (e.g., moving from individual assets towards
analysis and optimization of the entire system)

6%
21%

29%

Retrofitting sensors to existing products (continuing tooperate legacy equipment but investing in the additionof connected sensors on and around that equipment)

4%
19%

32%

4%
15%

28%

11% 24%

24%

6%
27%

19%

Automation/remote control of the processes or plants
we operate
Data science tools for citizen data scientist and citizen
application developers

Investing in connected products (replacing older
equipment with newer connected models)Digital twin (the term “digital twin” refers to a digital
representation of a physical thing’s data, state,
relationships, and behavior)
AI and ML for operations optimization (using ML or AI
techniques to analyze data from industrial processes inorder to optimize those processes, predict failure, etc.)

Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 2019

DIGITIZATION REQUIRES PEOPLE, PROCESS, TECHNOLOGY
Digitization in the industrial world means “doubling down on industrial
excellence while adding prowess in software and services.”2 To succeedin this environment, industrial firms must take a holistic approach that
accounts for people, processes, and technology. Firms will require:› IT/OT alignment. As industrial firms become digitally augmented,
they will need prompt and actionable insight from the OT teams thatrun critical industrial equipment and the IT teams that run mainstream
enterprise hardware, including hybrid cloud environments and software.
› Automated processes. As firms automate plant processes, theylook to replace legacy systems (65%), integrate previously separated
processes in legacy systems with robotic process automation (RPA)(59%), and invest in hardware robots (51%). By putting in place remotecontrol for the processes or plants they operate, adding connected
sensors to existing legacy equipment, or replacing older equipmentwith newer connected models, firms acquire rich data that can leadthem to faster innovation, and therefore greater success (see Figure 3a).

3 | Contextualized Data And Digital Twins Amplify Digitization Value

42%

41%

38%

39%

50%

31%

45%

› Digital twin capabilities. The digital twin, which Forrester defines asa digital representation of a physical thing’s data, state, relationships,
and behavior, offers an approach to ensure data and insights frommultiple systems are shareable and can be acted on.3 These twinmodels use new tools and services that leverage contextualized data,advanced analytics, visualization, and ML to support new use casesthat drive agility and value. We found that 24% of IT and OT decision
makers at global industrial companies have already invested in someform of digital twin with no plans of expanding investment, 31% plan
to expand or upgrade their investment, and 24% plan to invest in
the next 12 months. Digital twins have many use cases, but firms
primarily use them to improve product quality (55%) and optimize
activities between business units (48%). Integrating data fromsuppliers and partners, as prerequisite for new business models, also
rates highly (43%) (see Figure 3b).
› Contextual data. Digital twin simulations depend on data froma range of sources, including real-time streams of sensor datafrom machines operating in the field, historical data on their past
behavior, and synthetic data from physics-based simulators andvirtual sensors. For an organization to benefit from a digital twin of its
products and assets, it needs easy ways to find, use, and share thedata behind them. This success hinges on providing data scientists,
developers, and engineers with access to contextual data (adding
context to data to improve usability and insight) at their momentof need. Most industrial companies (49%) risk full value capture
because they take a retrospective approach to contextualizing data,
instead of doing so automatically as the data is captured (30%).

Figure 3a“Which of the following are/will be part of your company’s efforts to automate or remotely control plant processes?”(Select all that apply.)
65% Replacing legacy software systems with more modern alternatives

59% RPA to integrate previously separated processes in legacy software systems

51% Investing in hardware robots

46% Investing in lights-off operation of our remote facilities

40% Engaging an outsourcer to rearchitect legacy software systems and processes
Base: 139 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 2019

4 | Contextualized Data And Digital Twins Amplify Digitization Value

Figure 3b“How does your company use/intend to use digital twin functionality?” (Select all that apply.)
55% Improve product quality

48% Use data from sensors to orchestrate and optimize activities between previously isolated business units

43% Integrate data from suppliers and partners

41% Receive real-time recommendations to optimize the way in which our assets meets business goals

40% Assess product performance and predictive maintenance to reduce downtime

38% Accelerate time-to-market

38% Make sense of data collected from our machines

35% Drive decisions in the real world

34% Allow systems to autonomously tweak our machines for optimization

31% Predict the way that assets will perform in specific future scenarios

31% Gain visibility on global operations

30% Provide a single source of truth for engineering diagrams, operations manuals, etc.

1% Don’t know
Base: 128 decision makers in IT and operations roles at global industrial companies who are planning to invest, have invested, or are
expanding investment in digital twin
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 20195

Inexperience, Poor Data Quality, And The
IT/OT Divide Hinder Digitization Progress
While industrial firms have taken steps to innovate around theirprocesses, the road to digitization is filled with hurdles. Nearly 90%
of IT and OT decision makers report experiencing challenges withachieving their digital initiatives, primarily due to:› Lack of digital skills. A shortage of digital skills is the biggest
barrier firms face with digitization. Whether it’s a general lack of skills
inside the organization or misdistribution of existing skills, 75% of
IT and OT decision makers say that unavailability of qualified digitaltalent is slowing the pace of their digital transformation. The aging
workforce in industrial organizations adds insult to injury. Over 70%
of respondents say that as their workers retire, their firms will facesignificant challenges ensuring safe and efficient operations. When
combined, these hurdles will result in serious operational implications,especially as the use and upkeep of capital-intensive machinery is
critical to industrial firms’ survival (79% agree). Firms must address
this challenge head-on, building partnerships with companies that
can help them while also investing in upskilling existing workers andattracting new recruits.4› Poor data quality and insights. It’s well-accepted that data fuelsbusiness. However, the jump from intent to commitment holds backfirms from becoming insight-driven. Firms struggle to integrate,
manage, govern, and activate data.
• Eighty-three percent experience challenges with using data todeliver insights across the organization, ranging from lack of
capabilities to derive insights from data, data exploration difficulties,
disparate data management systems between IT and OT, lack ofdata quality or cleanliness, and inability to access data in real timeor the time of need. No singular challenge stands out from the rest,
which is an indication that firms struggle across the board to use
data to deliver insights across the organization (see Figure 4).
• Data is abundant, but it’s what organizations do with it that matters.
Adding context to data improves usability and insight acrossbusiness units. But 85% of firms in this study face issues as aresult of not providing contextually enriched data to data scientists,
engineers, citizen developers, and citizen data scientists. As a result,firms wade through avertible inefficiencies because it takes too muchtime to locate, access, use, or make sense of data (see Figure 5).
• When it comes to data governance strategy for new advanced
analytics applications in production optimization and predictive
maintenance, half say their firms are not yet addressing this because
their digital applications remain in proof-of-concept phase, not in
production. For those that are, only 29% apply the same principles
of data lineage/quality monitoring to all their applications. Individual
data projects may be well-managed, but without common controls
and procedures, it will be either risky or impossible to pull data frommultiple sources to power the enterprisewide view that decision
makers increasingly need. Data governance and maturing digitalfrom proof-of-concepts to production at scale go hand in hand.

6 | Contextualized Data And Digital Twins Amplify Digitization Value

Almost 90% of
industrial firms
struggle to achieve
their digital initiatives
because theylack skills, data
insights, and crosscollaboration.

› Separation of IT and OT. The IT/OT divide adds to the pain. For 56%
of firms, most digital projects sit inside the IT organization, leaving OT
teams in the dark on these initiatives. What spurs this divide? Up to 82%
of respondents cite conflicting interests and perceived lack of technicalcapability, strategy, and will or acceptance as the top barriers for IT
and OT collaboration. However, as industrial firms make the transition
to digitization, “this separation between IT and OT has graduated fromminor inconvenience to potential dealbreaker” (see Figure 6).5
Figure 4“What challenges, if any, does your company experience with using data to deliver insights across the organization?”(Select all that apply.)
26% Lack of capabilities to transform our existing data into useful insights; only use a fraction of the data available

25% Difficulty with data exploration and discovery

24% Lack of a unified data management strategy between IT and OT teams

24% Lack of confidence in data quality

24% Inability to access data in real time

24% Inability to combine data from disparate systems

23% Unavailability of data at the time of need

22% Documents missing structured computer-readable metadata

19% Inability to customize data models based on new data science needs

17% Lack of data cleanliness

17% None of these; we don’t experience any challenges with gaining insights from data
Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 20197

Figure 5“What difficulties do you encounter as a result of not providing contextually enriched data to data consumers (e.g.,
data scientist, citizen developers, and citizen data scientists)?” (Select all that apply.)
31% It is difficult to assess the accuracy and health of our data catalog.

30% Data consumers spend too much of their time locating relevant data.

29% Data science work is significantly slower because of reliance on scarce subject matter experts.

28% It is difficult to scale citizen data scientist and citizen developer initiatives because data is difficult to understand and use.

25% Digital application development teams are reliant on scarce subject matter experts.

22% Data from operational systems is difficult to access and remains underutilized.

19% We cannot programmatically query complex data, but we always need to involve subject matter experts.

19% It is difficult to understand the connections between IT data and OT data.

15% None of these; we don't experience any challenges with providing contextually enriched data to data consumers.
Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 2019

Figure 6“How much of a challenge are the following for your company’s IT and OT collaboration?"
Mildly challenging

4

Very challenging

Conflicting short- and long-term interests

34%

30%

Perceived lack of technical capability

28%

35%

Strategy

26%

26%

Funding

31%

27%

18%

Will/acceptance

30%

29%

17%

Vision

25%

31%

18%
19%
26%

19%

Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 2019

8 | Contextualized Data And Digital Twins Amplify Digitization Value

Secure Your Digital Future With A TopDown Approach From Senior Leaders
The best industrial firms have grown to success through meticulous
product testing and iterations over many years. This methodical
approach to improvement has worked in the past, but it’s limiting in the
fast-paced, agile environment that businesses face today. Keeping thispace undermines firms’ nimbleness to deal with unexpected market
changes, responsiveness to customer needs, and the ability to thrive inthe digital era.6
To be successful, solutions for moving forward must transcend theorganizational silos and legacy systems on which they’re built. From acultural perspective, leaders in industrial firms must encourage shared
responsibility between IT and OT teams, fostering collaboration andshared responsibility for both physical and digital experiences. Froma solutions perspective, industrial firms must leverage contextualized
data, advanced analytics, physics-based modeling, visualization, andML to support new digital use cases that drive agility.
Industrial firms are taking steps to drive transformation forward by:› Acknowledging the need for advanced analytics. Seventy-three
percent agree that in order to successfully digitize their companies,
they need prompt, actionable, and contextually relevant insights fromboth IT and OT in one place. Close to 80% agree that data needs tobecome self-explanatory to data consumers without needing subject
matter expert support. And a company will only achieve digitalmaturity when data scientists, engineers, and citizen developers
feel empowered to do more with advanced data and analytics bythemselves.
› Leveraging expert support. The preferred approaches to delivering
industrial AI are to buy packaged AI software applications (41%) or
to outsource it to a systems integrator partner (24%). In both cases,
firms are recognizing that it doesn’t make sense for them to attempt
to duplicate skills and solutions already available on the open market.
Instead, it makes more sense to use them, and to differentiate byusing the resulting insights more smartly than competitors.
› Increasing investment in tools that enable cross-teamcollaboration. Almost all (96%) express an interest in industrial data
and AI platforms.
An industrial data and AI platform is a single place to extract insightand build advanced analytics applications from data that includes AI
and ML capabilities.
Unsurprisingly, 98% expect benefits from using such a platform toconsolidate and contextualize data from disparate IT and OT systems(see Figure 7).

998% expect anindustrial data
and AI platform toconsolidate andcontextualize datafrom disparate IT
and OT systems.

Figure 7“What benefits would you expect as a result of using an industrial data and AI platform that consolidates andcontextualizes data from disparate IT and OT systems?” (Select all that apply.)
39% Improve relationship with suppliers and partners as a result of insight sharing

39% Provide strong data governance (e.g., data lineage and data quality monitoring)

39% Enable predictive maintenance

39% Provide common data architecture for IT and operations

39% Enable efficient data science workflows

38% Innovate new digital products and services

36% Unlock production optimization opportunities

34% Move ML applications from proof of concepts to production

30% Scale ML applications from one piece of equipment to full fleet of similar equipment

26% Empower citizen data scientists

21% Enable rapid/low code analytics application development

2% None of these; we don’t expect any benefits from an industrial data and AI platform that consolidates andcontextualizes data from disparate systems
Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 201910

Key Recommendations
Forrester’s in-depth survey of IT/OT decision makers about industrial
digitization yielded several important recommendations:
Bridge IT/OT divide to deliver on digital promises. IT and OT teams
hold a wealth of expertise, but a historic, rigid division of roles stifles
collaboration. At its worst, it normalizes ducking of responsibility andassigning of blame. Senior leaders must communicate loud, clearly,
and often that the company needs and values both IT and OT, but thatit’s no longer acceptable for them to operate entirely independently.
Establish joint projects in which success requires shared ownership andshared action. Assign shared goals and compatible KPIs. Encourage
temporary reassignments and bottom-up ideas for new cross-cutting
projects. Then get out of the way.
Integrate and contextualize data from across the organization to
support decision making. Effective decision making requires insights
based on data from a wide range of sources: real-time operational datafrom IoT sensors, historic behavioral data from historians and data
warehouses, synthetic data from physics models simulators and virtual
sensors, order and supply chain data, and information from third parties
on external factors such as the weather. As customers demand thatcompanies become nimbler and more agile, it becomes increasingly
important to ensure that relevant data can be — and is — available
when, where, and in the formats needed to support effective decision
making. Invest to unlock the organizational and technological silos
within which most of your data is currently locked. Make contextualized
data available and understandable to all data consumers within andoutside the industrial enterprise.
Invest in both skill building and partnership nurturing to succeed.
Few industrial organizations have the skills to plan, implement, andsustain their own digital transformations. Fewer could credibly argue
that such a capability would be sufficiently differentiating for them tojustify the investment required to deliver it. Instead, these organizations
must find and nurture partnerships with the software companies, data
scientists, and systems integrators that are already well-placed to takeon the bulk of this work. By all means, invest in building an internalteam with the skills and mandate to understand what’s happening, toexperiment for the future, and to oversee the work of others. But, formost, it makes sense to let your capable partners do the bulk of thework at your direction.

11 | Contextualized Data And Digital Twins Amplify Digitization Value

Appendix A: Methodology
In this study, Forrester conducted an online survey of 160 IT and OT decision makers from industrial companies
across the world to evaluate their use of digital twin functionalities and contextual data to drive forward digitaltransformation. Survey participants included decision makers in IT, operations, data science/digital, andexecutive management. Questions provided to the participants asked about their priorities, digital initiatives,
challenges, and interest in solution to help them overcome their issues. Respondents were offered an incentive
as a thank you for time spent on the survey. The study began and was completed in October 2019.

Appendix B: Demographics/Data
SECTORGEOGRAPHY
32%

United States

Industrial machinery and electrical equipment

10%

China

9%

Logistics and post

9%

United Kingdom

8%

Energy production

9%

Japan

8%

Oil/gas exploration/production

8%

France8%

Industrial products and construction materials

8%

Singapore

7%

Energy distribution

8%

Germany

7%

Rail and road

6%

India

6%

Oil refining

6%

South Africa

5%

Metals, mining, and forestry products

5%

Spain

3%

Shipping equipment manufacturing

4%

Italy

2%

Oil/gas distribution

4%

Canada2%

Utilities supply manufacturer

4%Australia

2%

Aerospace and defense

3%

Philippines

1%

Shipping and ports

2%

Airlines and airports

2%

Retailing hydrocarbon products

2%

Oil/gas field services

2%

Oil and gas machine manufacturing

1%

Life sciences

1%

Industrial electronics

1%

Automotive and transportation equipment

1%

Water supply and distribution

1%

Water management

1%

INDUSTRY
Manufacturing and materials

26%

Transportation and logistics

21%

Energy, utilities, and/or waste management

21%

Oil and gas

20%

Chemicals and/or metals

5%Electronics

4%

Agriculture, food, and/or beverage

3%

COMPANY SIZE
20,000 or more employees
13%
5,000 to 19,999 employees
18%

100 to 499 employees
15%
500 to 999 employees
19%

1,000 to 4,999 employees
35%
Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 201912 | Contextualized Data And Digital Twins Amplify Digitization ValueIT RESPONSIBILITIESOPERATIONS RESPONSIBILITIES

Data integration and data
warehousing

85%

Operations cost reduction

62%

Cybersecurity

76%

Production management

62%

Data analytics

74%

Automation operations

56%

Multicloud architecture

73%

Plant management

54%

On-premises architecture67%

Predictive maintenancedevelopment

51%

Data quality and data lineage

67%

Security management

46%

Strategy/enterprise architecture

66%

Production optimization

46%

65%

Engineering

44%

64%

Executive management

41%

Software/application management
and maintenance
Master data management anddata cataloging
Machine learning and data science

57%

Health and safety

41%

Software/application development

57%

Performance based contractingwith suppliers

38%

Single cloud architecture

55%

Maintenance

38%

Data modeling

52%

General oversight andadministration

36%

DATA SCIENCE RESPONSIBILITIES
Data modeling61%

Data typing/enrichment/inference

61%

Business application graphicaluser interface (GUI) development

56%

Data quality monitoring

56%

Model development, tuning, andmanagement
Business application GUI
maintenanceOperationalizing and managing
advanced analytics models

C-level executive
21%
Director
54%
Vice president
26%

56%

DEPARTMENT

50%

Executive
management/leadershipof service line
8%
Data science/digital
11%

50%

Data search and discovery

50%

Data integration and data
warehousing

50%
39%

Data harmonization
Metadata development and datacataloging
Scaling models across equipment
and asset fleets
Integrating physics simulators

RESPONDENT POSITION

IT
57%

39%
33%
11%

Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 201913 | Contextualized Data And Digital Twins Amplify Digitization Value

Operations
24%

DIGITAL TRANSFORMATION EFFORTS“Digital is embedded into most key processes acrossour business and is no longer considered to be a
separate project or initiative”
15%“We are beginning our digital transformation”
32%“We have made significant progress with our
digital transformation agenda”
53%

Base: 160 decision makers in IT and operations roles at global industrial companies
Source: A commissioned study conducted by Forrester Consulting on behalf of Cognite, October 2019

Appendix C: Supplemental Material
RELATED FORRESTER RESEARCH“Untangle The Digital Twin As Part Of Your Digital Product Strategy,” Forrester Research, Inc., October 2,
2019.
“Bridge The IT/OT Divide To Win With Smart Manufacturing,” Forrester Research, Inc., July 24, 2019.

Appendix D: Endnotes
1

Source: “Untangle The Digital Twin As Part Of Your Digital Product Strategy,” Forrester Research, Inc.,
October 2, 2019.

2

Source: “Bridge The IT/OT Divide To Win With Smart Manufacturing,” Forrester Research, Inc., July 24, 2019.

3

Source: “Untangle The Digital Twin As Part Of Your Digital Product Strategy,” Forrester Research, Inc.,
October 2, 2019.

4

Source: “Offer A Deeper Purpose To Tempt Digital Talent Away From Silicon Valley,” Forrester Research, Inc.,
August 6, 20185

Source: “Bridge The IT/OT Divide To Win With Smart Manufacturing,” Forrester Research, Inc., July 24, 2019.

6

Ibid.

14 | Contextualized Data And Digital Twins Amplify Digitization Value