Building the
New Economy of Movement

March 2021

connected
Mobility Data
Marketplace
B U S I N E S S
MOBI CMDM0001/WP/2021
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P A P E R

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END OF TERMS AND CONDITIONSINTRODUCTION
The Mobility Open Blockchain Initiative Connected Mobility
Data Marketplace Working Group is a global, multistakeholder project working to co-design standards based onblockchain and distributed ledger technologies for connectedmobility ecosystems. The project engages stakeholders acrossthe transportation value chain, including vehicle
manufacturers, technology solution providers, governmental,
and non-governmental entities. This report is based on
numerous discussions, workshops, and research. Opinions
expressed herein do not necessarily reflect the views ofindividual members.
Sincere thanks are extended to those who contributed theirunique insights to this report.

Authors
Chris Ballinger, MOBI
Roger Berg, DENSO
Benjamin Diggles, Constellation Network
Griffin Haskins, MOBI
Derek Pierre, NuCypher
Robin Pilling, MOBI
Tram Vo, MOBICMDM Working Group Co-Chairs
Roger Berg, DENSO
Michal Filipowski, General Motors

CMDM Working Group Team Members
Loren Adams, Accenture
George Ayres, IBM
Tim Bos, ShareRing
Joshua Cartellone, Accenture
Benjamin Diggles, Constellation Network
Arjun Hassard, NuCypher
Thomas Hulse, Fifth9
Jerry Kim, AMO Labs
Suresh Kumar Sundararaj, Ford
Duy Linh Nguyen, Cognizant
Chalid Mannaa, Ocean Protocol
Jim Mason, DMX

Pramita Mitra, Ford Motor Company
David Noack, Continental
Evripidis Paraskevas, GM
Manan Patel, Ocean Protocol
Derek Pierre, NuCypher
Anthony Salamone, RouteOne
Liu Shaowei, CPChain
Sudha Sriram, Ford
Sukesh Tedla, Swedish Blockchain Association
Wanda Wang, Toyota Insurance Mgmt Solutions (TIMS)
Chris Wood, Filament

MOBI Team
Chris Ballinger, Founder + CEO
Tram Vo, Founder + COO
Rajat Rajbhandari, Working Groups Lead

Robin Piling, Technical Lead
Griffin Haskins, Fellow
Kelly Clark, Communications Manager

01

Executive Summary

04

Current State of Affairs
Data Economy
Data Value Chain
Data Marketplace and Exchange in the
Automotive Industry
Mobility Ecosystems
Approaches and Problems of Data
Marketplaces in the Mobility Industry

19

The CMDM Standard

27

Short-Term Use Benefits

31
37
41
43

Medium-Term Use Benefits
Long-Term Use Benefits
Conclusion
Bibliography

TABLE OFCONTENTS

data, and securely record transactions. The Standard defines arobust framework for a system that provides the above
functionalities, in support of the development of those use
cases within the mobility ecosystem.

Executive
Summary
The Connected Mobility Data Marketplaces (CMDM)
Working Group will be releasing its first technicalspecification in Q1 2021, focusing on Vehicle to Vehicle(V2V), Vehicle to Infrastructure (V2X), and Infrastructure
to Infrastructure (X2X) use cases. Core to all of theseuse cases is the focus on enabling connected devices(like connected vehicles, or sensors in infrastructure) toauthenticate each other’s identities, securely share

The CMDM Standard provides
a rich set of digital
infrastructure, providing
frameworks for identity,
permissioning, and more, withthe goal of achieving
interoperability between
siloed mobility services.

The CMDM Standard prescribes a set of logical schemas tosupport interoperable data sharing between connected
devices, and provides a new certificate for a connected sensor
identity. Moreover, it provides a rich set of digital infrastructure,providing a framework for identity, permissioning, and more.
These frameworks are intended to achieve interoperability
between the “walled garden” mobility ecosystems thattraditionally have an entirely siloed operation. Integrating thesesystems allows for many new solutions that provide operational
efficiencies, superior insights, new revenue streams, and muchmore. The CMDM Working Group was acutely aware of this,which is why the CMDM Standard does not focus on any one
particular application, rather, it ensures that appropriate
infrastructure is in place to support organizations in creating
their own applications within those use case areas.

This document will
investigate the impact of the
CMDM Standard by
overviewing some use casesthat are supported by theStandard, broken down into
their expected time-tomarket.

This business review document begins with an overview of thecurrent state of affairs with regards to mobility ecosystems anddata exchange within the automotive industry. Then, the CMDM
Standard itself will be described in greater detail. The
document will also investigate how the Standard enables someexample use cases on short, medium, and long term time
horizons. Particularly, the Working Group defined short term use
cases as being implementable within five years, while medium
term use cases become feasible after five years, and long term
use cases are likely to take more than ten years to reach scale.
Ultimately, this document evaluates the use cases consideredby the CMDM Working Group and discusses the value of theStandard supporting such use cases in real world settings.
© 2021 MOBI — CMDM Business White Paper

2Data Economy
Data is digital, machinereadable information that
represents a foundational
layer of wisdom in the digitalage; a fundamental driver ofthe economy, almost akin tooil.

1“Data is the new oil” – arguably one of the most cited quotes in
conjunction with digitization that portrays the ongoing shift into2
the wisdom age and the corresponding economic mechanisms.
The scope of this paper cannot stray too much into the
influencing factors. Yet, it will test certain assumptions. There
are three key assumptions that are important to understand the3data economy and its importance:
The importance of physical products and related services
for unique value propositions and revenue streams is
declining, as they increasingly turn into commodities.

Data is the foundational layer of wisdom in a digital
environment, which will be detailed further in thesubsequent passages.

CURRENTSTATEof AFFAIRS

Data has an underlying monetary value based on derived
knowledge - in this case, increased knowledge of users
results in a better service and product experience.

Increasing complexity, the pace of change, and inherent
determinants cause business leaders to actively seek
opportunities that allow them to dominate their markets in thewisdom era. The wisdom economy has strong interdependencies
with growing digitalization in the 21st century. Data represents
digital, machine-readable information, from which knowledge,
wisdom, and insights may be extracted.
1.

“The World’s Most Valuable Resource Is No Longer Oil, But Data.”, Economist, May 2017<https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuableresource-is-no-longer-oil-but-data>

2. John Findlay, “The Wisdom Economy.”, thewisdomeconomy.blogspot.com, August 2011,<http://thewisdomeconomy.blogspot.com/> [Accessed 1 January 2021].
3. Kiran Bhageshpur, “Data Is The New Oil -- And That's A Good Thing”, Forbes, November
2019, https://www.forbes.com/sites/forbestechcouncil/2019/11/15/data-is-the-newoil-and-thats-a-good-thing/#4d1da4dd7304

3© 2021 MOBI — CMDM Business White Paper

Economic power is derived
from data, but that process iscomplex. Turning big data into
actionable insights, which is
how value is actualized for big
data, requires ample
computing power andsupporting infrastructure.

Hence, they form a foundational layer of wisdom in the digitalage. In this environment, the ever-growing amount of
information is increasingly digested by machines. Humans must
rely on computing power to be able to consume the vast
volume of information and turn it into actionable insights. The
big data movement implies that more consumable datatranslates into more knowledge and hence more economic
4
power.
4.

“Big Data, What it is and why it matters”, SAS, accessed January 2021,
https://www.sas.com/en_us/insights/big-data/what-is-big-data.html; “Big Data
Analytics”, IBM, accessed January 6, 2021,
https://www.ibm.com/analytics/hadoop/big-data-analytics© 2021 MOBI — CMDM Business White Paper

4Current State of Affairs
Pricing data is highly complex
and difficult for two corereasons - firstly, there is nouniversal marketplace for
data, and secondly, mostdatasets are not comparable.

Oil and data do have different
market mechanisms, andpricing data is quite complex
and difficult, which isfundamentally due to the factthat data is a non-excludable
and nonrival good.

This is where the data economy comes into play. Economic
power directly derives from monetization of a product, which isdata. In that context, the basic components of any market
theory and corresponding mechanism apply. A marketplace as
the basis for any economy requires a buyer, seller and price forthe product, which is normally determined by supply anddemand. Unfortunately, finding a fair market price for data ishighly complex, as many influencing factors are involved. At its
core, this is based on two facts: Firstly, there is no universal
marketplace for data. Secondly, most datasets are not evenremotely comparable.
While data may be seen as “the new oil,” it’s clear that they act
quite differently within an economic context. Oil as a raw
material is traded on a global marketplace, which is not thecase for data (as of now). While oil can be clearly quantified
and priced (e.g. per barrel), this is not true for data: 1kb of raw
data does not always have the same value. There is no generally
5
applicable measuring unit. Unlike oil, data has two
characteristics which inhibit the smooth functioning of private
markets. Data is “non-rival” in the sense that one user’s
consumption does not preclude another’s consumption of thesame data. Data is also often “non-excludable,” meaning thatonce data is public, it isn’t feasible to charge for use. Thesesources of market failure are both a challenge and an
opportunity when considering new blockchain-based data
markets.6
5. “Data Economy: Radical transformation or dystopia?”, United Nations, January 2019,https://www.un.org/development/desa/dpad/wpcontent/uploads/sites/45/publication/FTQ_1_Jan_2019.pdf; Jennifer Belissent, PhD,
“Help Wanted: Data Innovation For The Data Economy”, Forrester, October 2017,https://go.forrester.com/blogs/help-wanted-data-innovation-for-the-data-economy/;“Keynesian Economics”, Investopedia, April 2020,
https://www.investopedia.com/terms/k/keynesianeconomics.asp; Jason Fernando, “Lawof Supply and Demand”, Investopedia, November 2020,
https://www.investopedia.com/terms/l/law-of-supply-demand.asp

Data Value Chain
To get a better understanding of the data economy, a closer
look at the data value chain is key. At its core, raw data provides
little value. Given that a lot of literature suggests a great
demand for data, this could be considered a bold statement:
market mechanisms imply that a great demand makes somethingvaluable and drives the market price up.
Raw data does not have muchvalue, but much of theliterature suggests a highdemand for data. Accessing
and storing raw data can bedifficult and expensive.
Limited accessibility implies
high demand, and high prices.

Looking at the demand side, it is clear that not all market
participants have the same prerequisites and access to raw
data might be limited. Obtaining and storing raw data can be
hard, often requiring dedicated efforts and resources – think ofconsumer data, production data or sensor data, among others.
With limited accessibility, high demand normally constitutes highprices.
Yet, this only holds true if the actual supply is limited, resulting ina scarcity of the raw material. In reality, quite the opposite istrue. Compared to oil, which is a limited resource and can only
be consumed once, there is a plethora of data out there,creating an abundance of almost unlimited supply. The
principles of supply and demand indicate that an oversupply
lowers the market price.
So why is data still considered valuable and how is a priceestablished? The analogy to oil can be used again: raw data
resemble crude oil, referring to a resource that, without refining,is useless. Data needs to be refined to become valuable. This
refinement process includes structuring, analyzing andcombining different datasets to turn them into actionable
information and services. Hence, data is most valuable when it’s

6. D. Bergemann and A. Bonatti. “Markets for information: An introduction”, Annual Review
of Economics, November 2019.

5© 2021 MOBI — CMDM Business White Paper© 2021 MOBI — CMDM Business White Paper

6Current State of Affairs
an integral part of a customer facing solution – the end of the
7value chain.
Like oil, raw data must be
refined and prepared in orderto create value by being used
in applications and solutions.
The process that raw datatakes, from generation, todata cleaning andpreparation, and finally ending
with usage in an applicationthat actualizes the data’svalue is called the data value
chain.

If a company wants to find out if raw data is needed andwhere/how it should buy it, the value chain has to be appliedbackwards. First, the final offering must be clearly defined.
Based on this definition, the chain can be worked backwards todetermine what raw data is required to generate the final
product. As a positive side effect, by starting at the service andactual application layer, there is a clear definition of the user
and the value that should be generated.
With a concept of the final product as the starting point, thecompany can determine what is needed to create it. In mostcases, the core problem is not the availability of raw data but
the clear definition of a marketable service. It is vital tounderstand that in most cases buying data does not resemble
the purchase of an end product, but raw materials orcomponents that are needed to manufacture a service.
In a metaphorical way, the data value chain follows theprinciple of WHY – HOW – WHAT. The definition of concrete setsof raw data (WHAT – the data input) comes last. Simply
generating and hoarding data as the holy grail of economic
7. Rado Kotorov, “The Data Value Chain: Steps for Monetizing your Data”, Integration,accessed January 2021, https://www.idevnews.com/stories/6998/The-Data-ValueChain-Steps-for-Monetizing-Your-Data; “The Data Value Chain Executive Summary”,
Open Data Watch, accessed January 2021, https://opendatawatch.com/reference/thedata-value-chain-executive-summary/; Rainer Sternfeld, “Designing a Better Planet withBig Data and Sensor Networks”, Intelligent Sensor Networks Conference, November
2014,https://www.slideshare.net/rainer_sternfeld/designing-a-better-planet-with-bigdata-and-sensor-network-for-isn-conference-philips-high-tech-campus; Sylvain
Santamarta, Rash Gandhi, and Michael Bechauf, “Big Oil, Big Data, Big Value”, Boston
Consulting Group, August 2019, https://www.bcg.com/publications/2019/big-oil-datavalue.aspx; Edd Dumbill, “Understanding the Data Value Chain, November 2014,
https://www.ibmbigdatahub.com/blog/understanding-data-value-chain

7© 2021 MOBI — CMDM Business White Paper

Successfully extracting value
from data means having aclear picture of how that data
will be used in an application.
Defining first WHY data isneeded, and HOW that data
will be obtained andprocessed is necessary
before ever moving onto
WHAT is needed, or the raw
data itself.

success is a dangerous fallacy. The first step is knowing which
service should create value for whom (WHY – the vision) andsuccess is a dangerous fallacy. The first step is knowing which
service should create value for whom (WHY – the vision) and how
the input should be transformed into this service (HOW – therefinement process). Otherwise data collection will onlytranslate into having a warehouse full of raw materials with no8
production process or final product to use them.
Bridging the gap back to the wisdom economy, data only
represents the foundational layer to wisdom and decisions – or in
9
economic terms – the final product/service.

Data Marketplace and Exchange in the Automotive Industry
Vehicles are increasingly
Connected, Autonomous,
Shared, and Electric, which
together provide the core
paradigm shifts driving
mobility’s shift into the digitalage.

The automotive industry is undergoing a fundamental changethat redefines market mechanisms and the roles of players in thevalue chain. While a magnitude of macroeconomic andmicroeconomic factors influences the ongoing shift, the most
relevant are summarized under the CASE acronym. CASE stands
for Connected, Autonomous, Shared and Electric and was
mainly framed by Daimler.
8. Dave Chaffey, “Golden Circle model: Simon Sinek’s theory of value proposition, start withwhy”, Smart Insights, October 2020, https://www.smartinsights.com/digital-marketingstrategy/online-value-proposition/start-with-why-creating-a-value-proposition-with-thegolden-circle-model/; Peter Götz, “Why – How – What: From Product Vision to Task”,
Scrum.org, March 2018, https://www.scrum.org/resources/blog/why-how-whatproduct-vision-task; Chuck Frey, “Customer Centric Innovation”, Innovation Management,
April 2004, https://innovationmanagement.se/imtool-articles/customer-centricinnovation/
9. Rob Lokers, “Analysis of Big Data technologies for use in agro-environmental science”,
ResearchGate, October 2016, https://www.researchgate.net/figure/DIKW-hierarchyfrom-Big-Data-to-decision-making-for-societal-challenges_fig1_307874029; Ayan
Brahmachary, “DIKW Model: Explaining the DIKW Pyramid or DIKW Hierarchy”,
CertGuidance, May 2019, https://www.certguidance.com/explaining-dikw-hierarchy/© 2021 MOBI — CMDM Business White Paper

8Current State of Affairs
This acronym gives a great overview of the most influential
determinants of the shift, which are interconnected with eachother as well as surrounding trends.10

Vehicles can still be privately owned by one consumer but
shared with other riders for a fee (see: Tesla robotaxi, in
conjunction with autonomous driving). Arguably, shared mobility
represents not only a technological shift but also a change inmindset of how the overall concept of mobility is perceived –which has the potential for far-reaching implications on value
chains and the role of established actors. From a technological
viewpoint, shared concepts demand data exchanges between
13
involved parties inside and outside the ecosystem.

Connected
Vehicles are increasingly connected with everything (V2X
communications). This includes, but is not limited to: drivers,
infrastructure, other vehicles, and third-party service providers.
Communication is based on connectivity (e.g. 5G networks) anda constant data exchange between vehicles and the mobility
11
ecosystem.
Autonomous
The vision of self-driving vehicles is gradually becoming areality. Based on the SAE automation levels ranging from zero(no automation) to five (full automation), current vehicle
generations are between level three and four. Automation isbased on different sensors, processing power, algorithms,
machine learning/AI and the ability of vehicles to exchange
12
data with other actors in traffic (machine or human).
SharedThe deeper meaning of shared is a change in the concept ofownership. It subsumes car-sharing, ride-hailing, public
transport and other fleet services that do not constitute private
ownership and sole use of a vehicle by a single person.
10. “CASE – Intuitive Mobility”, Daimler, accessed January 2021,
https://www.daimler.com/case/en/
11. Charles McLellan, “What is V2X communication? Creating connectivity for theautonomous car era”, ZDNet, November 2019, https://www.zdnet.com/article/what-isv2x-communication-creating-connectivity-for-the-autonomous-car-era/
12. “Automated Vehicles for Safety”, NHTSA, accessed January 2021,
https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety9© 2021 MOBI — CMDM Business White Paper

There are four key factors
driving mobility’s shift into
the digital age - Connected(connectivity in vehicles),Autonomous (autonomousvehicles and agents), Shared(sharing economy), andElectric (electric vehicles andeMobility applications).

Electric
Electric stands as a synonym for engines that are not based on
fossil fuels and includes hybrid drives, purely battery-powered
vehicles, fuel cells, and alternative synthetic fuels. For all
systems, new infrastructural solutions are mandatory and require
data to be exchanged between vehicles, passengers, and the14
infrastructure (e.g. smart grid and charging points).
All of these major drivers have one thing in common: they require
data as the fuel for future mobility ecosystems. However, thecomplexity of a mobility ecosystem is immense, and afunctioning ecosystem requires prospering data exchange andcollaboration between all actors.
13. Kirsten Korosec, “Tesla plans to launch a robotaxi network in 2020” TechCrunch, April
2019, https://techcrunch.com/2019/04/22/tesla-plans-to-launch-a-robotaxi-networkin-2020/; Anne Grosse-Ophoff, Saskia Hausler, Kersten Heineke, and Timo Möller, “Howshared mobility will change the automotive industry”, McKinsey & Company, April 2017,https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/howshared-mobility-will-change-the-automotive-industry; Tobias Schönberg, “Shared
Mobility”, Roland Berger, July 2014,
https://www.rolandberger.com/de/Publications/Shared-mobility.html
14. “Alternative Fuels and Advanced Vehicles”, US Department of Energy, accessed January
2021, https://afdc.energy.gov/fuels/; “Alternative Fuels”, US Department of Energy,
accessed January 2021, https://www.fueleconomy.gov/feg/current.shtml; “The Smart
Grid”, US Department of Energy, accessed January 2021,
https://www.smartgrid.gov/the_smart_grid/smart_grid.html© 2021 MOBI — CMDM Business White Paper

10Current State of Affairs
depending on the situation and reason for traveling: A privately
owned car, public transport, bicycle or shared mobility offerswith several types of vehicles. Most journeys even combine atleast two or more of these means. They can be best summarized
as the vehicle layer.

Mobility Ecosystems
By definition, a mobility ecosystem encompasses all modes oftransportation, a multitude of brands, single entities,
subsystems that provide transportation, the infrastructure itself(roads, traffic signs, electric grid), other supporting actors, andultimately, the consumers that require mobility. These moving
15
parts are all interconnected and interdependent.
Mobility ecosystems
encompass all modes oftransportation, entities,groups, companies,
subsystems that provide
transportation, infrastructure
itself (roads, highways), andeverything else that is usedor participates in mobility.

Normally, a mobility ecosystem includes all aspects andfunctions that provide transportation, be it short-, mid- or longdistance. Yet, a holistic scope on mobility that connects all dots
becomes extremely complicated. Various modes and providersof transport must be taken into account, different localities andinfrastructures in one country have to be aligned. Internationally,
even multiple languages, different time zones, and currencies
16
must be considered.

Imagine a city as a manifold: different components play
together like gears in a well-oiled machine to form the overall
concept of the city. This metaphor is also true for an urban
mobility ecosystem. The foundational layer is formed by theurban population that needs transportation for daily routinesand leisure. It can choose between different means of transport,
15. “Mobility Ecosystem: A wave of transformation”, Moveco, July 2018,https://medium.com/@moveco.io/mobility-ecosystem-a-wave-of-transformationc8f2eb7af658; Scott Corwin, Philipp Willigmann, “The evolving mobility ecosystem”,
Deloitte, accessed January 2021,
https://www2.deloitte.com/us/en/pages/manufacturing/articles/the-evolving-mobilityecosystem.html
16. Ralf Baron, Michael Zintel, Niklas Schemken, Christoph Uferer, “Integrated Mobility
Platforms”, Arthur D Little, February 2018,https://www.adlittle.de/en/insights/viewpoints/integrated-mobility-platforms; Shannon
Bouton, Stefan M. Knupfer, Ivan Mihov, Steven Swartz, “Urban mobility at a tipping point”,
McKinsey & Company, September 2015, https://www.mckinsey.com/businessfunctions/sustainability/our-insights/urban-mobility-at-a-tipping-point11© 2021 MOBI — CMDM Business White Paper

In a mobility ecosystem, thecar, bicycles, taxis, trains,
micro mobility options,motorcycles, etc. are allreferred to, collectively, as
the vehicle layer. The bridges,
roads, electric grid, and othercapital is referred to as theinfrastructure layer. The
drivers and travelers (users)
are referred to as theconsumer layer.

Aside from the vehicle layer, there is also the infrastructurallayer, including bridges, roads, the electricity grid, and traffic
management, among others.17 This infrastructure layer may
communicate with entities within its layer or with the vehicle
layer.
From a data viewpoint, there are data silos on every
aggregation point. Each consumer has their own set of devices,
applications, and services they use. We call this the personal
network, involving the individual datasets every inhabitant owns
and shares. On the vehicle layer, every entity has its own siloeddata network that incorporates routing, booking or payment
services, among others. Examples include the data networks ofbuses, train and taxi providers, car-sharing fleets, or the datadriven services of every vehicle manufacturer. These data silos
can intertwine within the vehicle layer. For example, if a carsharing fleet uses cars of multiple makes, every car and car
maker has their own branded network and portfolio of services,which are then combined in symbiosis within the network of the
fleet operator (e.g. for telematics data). Aggregation of silos
happens on various levels and in multiple combinations.
By exchanging data between the consumer and vehicle layer
networks, mobility services are created. These offers areconnected to the infrastructural layer that has its own set of
17. “How many times have you heard – "Automakers are sitting on a data goldmine"?” SBD
Automotive, accessed January 2021, https://www.sbdautomotive.com/en/newsautomakers-goldmine© 2021 MOBI — CMDM Business White Paper

12Current State of Affairs
Within the vehicle andinfrastructure layers exist
data silos - consider the data
networks of buses, carsharing fleets, vehicle
telematics, roadway sensor
systems, and more. To
expand the quantity andquality of mobility services,
data must move between
silos and aggregated on asuperseding layer, called theecosystem layer.

secluded data networks required for traffic management,
telecommunications, or energy supply within the city. A practicalexample can be given with Tesla’s (and other OEMs) charging
services that allow the consumer to search for available
charging points based on their location and incorporatecharging dynamically into routing services. Especially regarding
electric and autonomous vehicles, an open data exchange
between the infrastructural, vehicle, and even consumer layer is18
mandatory.
To allow for a functioning ecosystem, data must transcend
between clusters and be aggregated on a superseding layer,
described as the ecosystem layer.

Approaches and Problems of Data Marketplaces in the
Mobility Industry
Collaboration andcoordination between
numerous players isnecessary to create that
ecosystem layer that enables
a transition into a “Mobility as
a Service(MAAS)” world.

Numerous players need to collaborate in the interwoven mobility
ecosystem to enable the transition into a “Mobility as a Service(MaaS)” world. A term that is often used in this context iscoopetition – cooperation on the network level and competitionon the service level. Information and data has to transcendorganizational boundaries and must be shared between multiple
19
organizations and even industries. Since most players pursue

divergent goals or compete on the service layer, data exchange
should be orchestrated in the form of a mediating marketplace.
The competition, then, is rooted in the insights derived from thedata, such as algorithms, that lead to optimized and highquality products and services.
Although it’s a massive
challenge to build a universal
marketplace for mobility data,
there are a number of
initiatives and platforms
focused on it. Moreover,most of the important large
players are pushing for thecreation of mobility dataexchanges. Understanding
the pain points that hinder
their creation is key.

Even though it is a massive coordinative challenge to implement
such a universal marketplace, multiple platforms and initiatives
have emerged over the recent years to tackle the problem. And
within certain secluded segments, there have been successful
examples, mainly driven by OEMs. Yet, all of these efforts stilllack traction and there has been no global breakthrough.
Considering that all actors can logically conclude that an open
exchange of mobility data is the foundational layer formeaningful services and future offers, the question is: whatstops them from just doing it?
To get a deeper understanding of pain points and why mobility
data exchanges are struggling to gain wider traction, existing
platforms have been inspected and related market studies havebeen analyzed. Overlaying the findings, five major pain points
that hinder open mobility data exchanges were identified.

18. Ricardo Bacellar, “What will the future mobility ecosystem look like?”, KPMG, November
2017, https://assets.kpmg/content/dam/kpmg/br/pdf/2017/11/br-kpmg-congressosae.pdf; “Charging Your Car”, Tesla, accessed January 2021,
https://www.tesla.com/support/charging
19. Warwick Goodall, Tiffany Dovey Fishman, Justine Bornstein, and Brett Bonthron, “The Riseof Mobility as a Service”, Deloitte, January 2017,https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/consumerbusiness/deloitte-nl-cb-ths-rise-of-mobility-as-a-service.pdf; Will Kenton, “Understanding
Coopetition”, Investopedia, May 2020,
https://www.investopedia.com/terms/c/coopetition.asp

13© 2021 MOBI — CMDM Business White Paper© 2021 MOBI — CMDM Business White Paper

14Current State of Affairs
A consensus on data accessrights, roles, tasks, andoverall framework is criticalto the development of anopen data exchange.

A universal shared data framework for the mobility sector stillneeds to be defined. It is less about technical hurdles – acommon rule is that structured data in machine-readable
formats are the preferred choice. The bigger issues are accessrights and format as well as the infrastructure of datasets.
Players closely guard the data for their own use and treat theirinventory as proprietary. A consensus for data access rights andthe degree of openness is required to establish a functioning
data exchange.
A totally open exchange would support innovation and excel
transitional speed of the ecosystem by allowing data-based
services to prosper. What becomes apparent is that all
initiatives suffer from an unclear distribution of roles and tasks:
who decides what is public and what remains private, whomanages access rights, who supplies, and who consumes.

Data quality is commonly
determined by many factors,
including its accuracy,completeness, consistency,
validity, integrity, andcurrency.

2. Quality of Data
Mobility services depend on up to date, reliable, andadequately complete data within the ecosystem. Especially for
applications needing real-time data, the half-life is extremely
short. A lot of data must be updated daily, hourly, or, in somecases, every second. If a service provider wants to acquire
datasets, they need to know that he receives quality – reliable
sets without corrupt data. In that manner, quality does not only
involve refresh rates, but also quality of the supplied datasets,
referring to associated data points, format, and structure.
Usually, data quality is assessed along the following dimensions:accuracy, completeness, consistency, validity, integrity, and15timeliness/currency. However, without a central governing bodyand given the magnitude of potential use cases for data in a
mobility ecosystem, a universally applicable consensus on thelevel of quality can be hard to define.

1 . Data Sharing Standards© 2021 MOBI — CMDM Business White Paper

Without a sharedunderstanding andframework for valuing data
and measuring its quality,
determining an agreed value
for data is extremely difficult.

The perception of quality in data is thus largely built on trust.
Without knowing if a source can be trusted, a mediating trusted
third party or product samples that give the consumer the
chance for a product trial are required, assuming the data
supplier wants to hand out freebies since it reinforces a20
freeloader syndrome: taking the data, without sharing their own.
3. Monetization of Data
Considering the data value chain, data suppliers have nointerest in sharing their information for free. Companies withaccess to raw data hoard it and have no natural interest in
sharing, thereby creating data silos. Data owners are afraid to
openly share or sell their data because they risk missing out on
more valuable applications or services someone else could buildwith them.
Even if the provider is willing to sell its data, there is no universal
measuring unit or definition for the value of data, as it is mostly
based on perception and individual motives. Every dataset isdifferent and for maximum use and interoperability, raw dataneed a level of normalization. Establishing a fair price fordatasets still typically require negotiations in individual deal
20. Christian Bussmann, Magdalene Piec, Dr. Andreas Gutweniger, “MaaS and Data Sharing:
Five Issues Closely Related to Practice”, Detecon Consulting, April 2016,
https://www.detecon.com/en/knowledge/maas-and-data-sharing-five-issues-closelyrelated-practice; “Data Done Right: 6 Dimensions of Data Quality”, SmartBridge,
accessed January 2021, https://smartbridge.com/data-done-right-6-dimensions-ofdata-quality/© 2021 MOBI — CMDM Business White Paper

16Current State of Affairs
flows between buyer and seller. Yet, data buyers are hesitant tobuy, because accessible data might already have been
tampered with and there is no comparative market price.
Data sellers are hesitant tosell their data, because thatdata could be used to power a
new service or product whose
profits they want to capture.
Data buyers are hesitantbecause there is nocomparable market price.
Consequently, there is a lackof monetary incentives fordata sharing.

It lays within the nature of private companies to be profitoriented. Hence, sacrificing potential business models for thegreater good is against their nature. Enforcing this throughpublic organizations and governmental regulations could be a
distortion of competition. In addition, processing and making
data available in good time simply costs time and money, so
free sharing is a calculatory loss for the provider.
Consequently, there is a lack of monetary incentives in sharing
data. The only non-monetary incentive is the exclusive access toother mobility data through sharing, similar to peer-to-peer filesharing. However, this only works if there is a consensus on open
sharing and everyone plays by the rules. This is also where the
network effect comes into play, as the value of the exchange
increases with active participants – so who will make the firstmove?

5. Security
There are two major concerns that are closely linked to eachother: protection against malicious attacks (cyber security) anddata privacy. The data exchange has to be secured at the actor
and network level to provide the necessary level of protection
and prevention of a single point of failure. Especially in
combination with autonomous driving, the security of datatransfers is a top priority.
Liability from potential data
breaches or exposure of PII
makes data generators
reluctant to share data, andso without robust security,
the potential liability of data
sharing makes the endeavor a
calculatory loss.

Another factor is the protection of personally identifiable data:a unified approach to protect consumer data is required, which
involves legal, technical, and ethical considerations. Pseudoanonymous data can be reengineered to reveal identities, e.g.
by cross-referencing location and other datasets. Reverse
engineering of personalized data must be prevented in thedesign of the system architecture to comply with dataprotection laws. In addition, consent structures have to be set up
for personal data or datasets have to be anonymized to a21
degree that does not require individual consent of end users.

4. Ownership Models
Ownership and profit models
around data sharing areunclear, resulting in friction
between data sharing
entities.

17

Control and ownership of aggregated data result in economic
power. Hence, most actors pursue assuming the role ofintegrators and aggregators – in other words: mobility service
providers. As a result, other actors would be degraded to data
suppliers missing out on the monetization opportunities in theservice layers. Naturally, this creates conflicts of interest. On
another note, legal transfer of data ownership can be an issue
if personal data based on previous opt-ins are involved.

© 2021 MOBI — CMDM Business White Paper

21. Regina Clewlow, “The Opportunity To Reshape Cities With Shared Mobility Data”, Forbes,
October 2018, https://www.forbes.com/sites/reginaclewlow/2018/10/10/theopportunity-to-reshape-cities-with-shared-mobility-data/#5cb5ff3e617f; Nick Ismail, “Thenew front line of cyber security is mobility”, Information Age, March 2018,https://www.information-age.com/front-line-cyber-security-mobility-123471096/; Leon
Nash, Greg Boehmer, Mark Wireman. Allen Hillaker, “Securing the future of mobility”
Deloitte, April 2017, https://www2.deloitte.com/us/en/insights/focus/future-ofmobility/cybersecurity-challenges-connected-car-security.html© 2021 MOBI — CMDM Business White Paper

18

THE CMDM STANDARD
Modern vehicles, trains, bikes, and scooters are increasingly internetconnected and intelligent. Even infrastructure like highways, bridges, andferries are being endowed with sensors, telematics, and secure identities
to generate and share data. Truly exciting services become achievable
when these devices are able to seamlessly and easily share data witheach other, with the necessary shared functionality guaranteed. Theseabilities are called data interoperability and functional interoperability;
both are difficult to achieve at scale. In mobility ecosystems today, there
is a dearth of such interoperability, across both the vehicle andinfrastructure layers, resulting in the natural formation of silos at eachaggregation point. Individual Mobility Service Providers operate entirely
within their own silos, limiting potential value creation from new services
by preventing the use of datasets across multiple silos. Collaboration
between data owners necessitates costly integrations due toincompatible datasets, functionalities, etc.

19

© 2021 MOBI — CMDM Business White Paper

Connected devices, and the success of markets that monetize thosedevices’ data, are defined by the degree of interoperability that exists inthe ecosystem. Indeed, every use case involving connected devices relies
on secure, scalable data sharing. Blockchain provides a secure avenue
for these stakeholders to authenticate each other’s identity and to
immutably record/securely expose their collected information to otherstakeholders. The immutability and shared governance of distributed
ledger systems provide a necessary, foundational layer for creatinginteroperable applications on top. However, breaking down these silos
requires more than just technology - it requires community andcoordination.

© 2021 MOBI — CMDM Business White Paper

20The CMDM Standard
Exciting new services andapplications are madepossible when different silos
in the vehicle andinfrastructure layers are able
to seamlessly share data
between themselves, with
the necessary sharedfunctionality guaranteed.
These features are called
data interoperability andfunctional interoperability.

Interoperability can only be achieved when there are common
standards for core infrastructure - identity, data, and functions.
The CMDM Standard prescribes core services, certificates, andlogical schemas for the sharing of data between connectedvehicles and the connected infrastructure they drive through. In
other words, the CMDM Standard is a key step towards
achieving data interoperability and functional interoperability inmobility ecosystems. This allows data to smoothly flow between
these silos, which enables a rich variety of applications.
The logical schemas prescribed by the CMDM Standard define
the set of attributes, data formats, and their associatedstandards for data-generating devices to register an “event.”
An “event” is anything that results in the generation of data from sensors in an engine detecting a part failure, to a vehicle
detecting debris on the roadway, or even the detection of highconcentrations of particulate matter in the air. The CMDM
Standard provides a universal data sharing framework for all ofthese devices (vehicles, air quality sensor systems, etc.) tocommunicate and share information. These schemas are how
the CMDM Standard accomplishes data interoperability. By
prescribing underlying core services (identity, settlement,
discovery, and more), the CMDM Standard also addresses
functional interoperability.
CMDM-compliant systems will generate clean, easily parsed,
and similarly organized data. For data scientists and engineers
seeking to utilize that data within their own applications, thismeans much less time cleaning and processing data, and amuch more efficient data pipeline from source to endpoint.
From the perspective of the data value chain, this is critical. As
previously explained, raw data has little to no value. It is whenthat raw data is processed, organized, and utilized in the21© 2021 MOBI — CMDM Business White Paper

extraction of insights and execution of services that value isgenerated. By greatly reducing the time and resource cost of
ETL for raw data, that data is effectively moved further down thevalue chain, minimizing the number of steps required for thatdata to ultimately provide value at the end of the chain.
Blockchain allows for entities
to authenticate each other’s
identity, as well as immutably
record and securely expose
collected information to eachother. But this can only beachieved when those entities
have common standards for
identity, data, and functions.
This is a major focus of the
CMDM Standard.

In developing the CMDM Standard’s schemas, the Working
Group focused on ensuring that the schemas could
appropriately capture all possible events - uniformly
representing that event’s associated data regardless of what
type of data is involved. For “simple” events, like a recording ofan engine’s oil temperature, or a weight sensor detecting thatsomeone has sat down, this is not relatively difficult. This is thecase for most of the types of sensors that the Working Groupconsidered, which also include force, torque, inertial, optical,
ratio, speed, etc. However, when considering more complex
data, such as LiDAR, camera, or video, representing them
uniformly becomes much less straightforward. In order for dataexchange to provide as much value as possible, it must support
all possible sorts of data. The CMDM Standard thus alsoprescribes methods for registering large, complex data, primarily
by creating a smaller representation of that data for use as apointer.
Perhaps most importantly, the Standard is also the key first stepin enabling data monetization and efficient data marketplaces.
As with any good, the price of data is determined by its value what that good sells for in an efficient market. But no efficient
market for data exists today, so there is no ability to prescribe aprice or common metric of value. The first step to achieving anefficient data market is for there to be secure systems for
exposing and sharing data. Without such systems, there would
not exist a secure, scalable way of delivering data to buyers.

© 2021 MOBI — CMDM Business White Paper

22

The CMDM Standard
Traditionally, data is shared bilaterally, or within one closed,
siloed ecosystem. Such one-to-one transactional structures
don’t lend themselves to a scalable marketplace model. But
more importantly, the question is: why did the market for data
evolve this way, while markets for other goods, like oil, did not?
The CMDM Standard provides
a structure for data to act asa private good, allowing forthe creation of an efficient
market for it.

This paper has already discussed some causal factors. As
previously mentioned, data is not a private good, as it is notrivalrous; in other words, the same data can be copied anddistributed without additional marginal cost. For such goods,
the key is to be able to charge for the usage of the goods. For
example, consider roads. If one driver is on the road, they are
not preventing others from driving on that same road (excluding
issues of traffic congestion, of course). There is also no way toprevent someone from getting in their car and driving on a road.
Roads are not private goods, and as a result, they represent a
sunk cost for local governments, as they are a significant
investment without direct returns. Governments solve thisproblem by introducing road user fees, often through tollstations or automated charging systems. Drivers are charged fortheir use of the road and can be turned away or fined for not
paying that charge. By creating a structure that allows forcharging for use of non-private goods, they can act as private
goods within that structure, and an efficient market can be built
for it.
Applying this model to the issue of creating an efficient market
for data, it becomes clear that structures must be put into placethat allow for entities to be charged for the usage of data.
Charging for usage of data requires two sets of functionalities:
secure access management of data, and logic for the executionof payments. The structures prescribed by the CMDM Standard
allow for actors to securely grant data access to other actors.

23© 2021 MOBI — CMDM Business White Paper

That is, the CMDM Standard provides the structures for data to
act as a private good, which is critical for its monetization in adata marketplace.
Data privacy, security, andcompliance are core to the
CMDM Standard.
Unobfuscated data is neverdirectly shared. Additionally,
the Standard is flexible
enough to allow for various
different governance models,reflecting that different sortsof data may require different
marketplace structures.

The CMDM Standard was crafted with data privacy, security,and compliance as core considerations. The Standard’s
framework for data ensures that unobfuscated data neverleaves any given actor’s data lake, rather, attestations are madeto a distributed ledger, with robust access management for any
data reads. Moreover, data provenance can be guaranteed bycomparing data to its recorded attestation. As that attestation
exists on a distributed ledger, that record has no single point of
failure. Security and data privacy exist on a protocol level and istherefore guaranteed in all CMDM-compliant applications, and(within reason) in all possible governance models. Notably, theStandard does not prescribe governance models for data
markets, as these are better left to the implementers. Rather, it
prescribes the necessary functionalities for the decentralized
operation of such a marketplace. In all likelihood, there will exist
many data marketplaces operating independently, each withdiffering features, datasets, etc. Depending on each market’s
context, they may prefer a holistic integrator to manage the top
level of the mobility ecosystem, or a more distributed approach.
In light of this fact, the Standard ensures applicability in avariety of governance models.
The CMDM Standard also introduces a brand new
identity/ownership certificate for sensors. Currently, when asensor generates a data point, there does not exist any
protocol-level functionality for associating that data point witha given sensor. There are exceptions to this. Somemanufacturers create more powerful devices that typicallycontain many sensors, and some of these devices will have some© 2021 MOBI — CMDM Business White Paper

24

The CMDM Standard
The CMDM Standard provides
sensor identity certificatesand sensor ownershipcertificates for use in large,
complex sensor networks.

The CMDM Standard’s threecore components - coreservices, logical schemas,
and sensor
identity/ownershipcertificates provide the basestructure required forbridging the current silos in
mobility ecosystems.

25

sort of device ID, but there is no standardization. As a result,complex systems with many sensors can struggle withdiagnosing failures, software/firmware consistency acrossdevices, and more. The certificate simultaneously gives a unique
ID to each sensor, as well as associates that sensor with a given
owner. Attestations of that certificate go onto the distributed
ledger, and are therefore given the same security guarantees.
This is key, as sensors generate raw data, which begins at thefirst step in the data value chain. Moving that data through thevalue chain, by processing, refining, extracting insights, etc.
belies an often enormous, complex system that has to handle
and organize data, which is streaming in real-time (or nearlyreal-time) from sensors. Sensor IDs and Ownership allow for
much, much more precise control and understanding of a sensor
suite.

and value as adoption grows over time. Therefore, the remainderof this paper will be spent discussing some use case examples
that are feasible on short, medium, and long term time horizons.

Core Services

Logical Schemas

Sensor Identity/
Ownership
Certifciates

Figure 1 — CMDM Main Components

Ultimately, the CMDM Standard’s three main components - coreservices, logical schemas, and sensor identity/ownershipcertificates - provide the structures required for smoother data
sharing, allowing communication and collaboration between thesiloed ecosystems of each Mobility Service Provider. Suchstructures lay the foundation for robust, distributed data
marketplaces operating at scale. The Standard carefully andeffectively distinguishes between what sorts of data andfunctionality must be standard across all implementations,versus what considerations are best left up to the engineers. Of
course, there are many hurdles to overcome in achieving an atscale data marketplace, but the CMDM Standard represents a
key step in addressing many of those challenges. Standards aremuch like mobility ecosystems, data marketplaces, anddistributed ledgers, in that they all rely on network effects toprovide value. The CMDM Standard enables an exciting array ofuse cases, and those use cases will grow in number, complexity,© 2021 MOBI — CMDM Business White Paper© 2021 MOBI — CMDM Business White Paper

26

SHORT-TERM USE BENEFITS
The breadth and depth of use cases that are enabled by the CMDM
Standard increase with the Standard’s adoption. Even still, the CMDM
Standard enables exciting applications in the immediate term. Modern
vehicles are endowed with a suite of sensors, sufficient (and rapidly
increasing) computing power, and communications capabilities. These are
more than enough to support many v2x applications, and thesecapabilities will only grow more rapidly over time.
Vehicles can use their ECUs to collate sensor data to enable insights on
safety factors. When a vehicle’s optical sensors or cameras detect anobstruction on the roadway, its ECU can collate that information andsend the key insights (the existence of the obstruction, its location, etc.) toother vehicles nearby, as well as to transportation authorities, and anyother relevant parties. This capability - to detect events in theenvironment, measure and record that event, and share the resulting datawith available parties (in the immediate vicinity or at distance) enables

27© 2021 MOBI — CMDM Business White Paper

many safety-related V2X applications. For two simple examples, consider
Intersection Movement Assistance (IMA) and Left Turn Assistance (LTA). In
an IMA application, a vehicle that is about to run a red light can alert
other vehicles before entering the intersection, allowing other vehicles toadjust course. In an LTA application, a vehicle turning left can broadcast
22
its intent to begin turning, and issue alerts to nearby cars attempting todrive straight through. Alternatively, a vehicle turning left can receive analert from a vehicle that is about to come straight through, andautomatically brake to avoid a crash. These applications are not only
23
buildable and achievable today, but are made quite simple with the tools
and infrastructure provided by the CMDM Standard. The National Highway

22. “Safety - Intersection Movement Assist”, US Department of Transportation, accessed January 2021,
https://www.its.dot.gov/infographs/intersection_movement.htm
23. “Safety - Left Turn Across Path”, US Department of Transportation, accessed January 2021,
https://www.its.dot.gov/infographs/left_turn_across.htm© 2021 MOBI — CMDM Business White Paper

28

Short-Term Use Benefits
The CMDM Standard allows
for vehicles andinfrastructure to sharesafety-related information,
like geolocation andmovement intention, which
can be used in many safety
applications like Intersection
Movement Assistance andLeft Turn Assistance.

The usefulness of safety
data can be augmented bycross-validating geolocation
with other vehicles.
Moreover, validation ofgeolocation data andavailability of highly granularlocation information is
directly monetizable.

Traffic Safety Administration estimates that implementing justthese two applications could prevent up to nearly 600,000
crashes every year in the United States alone, which would savemany lives, avoid many injuries, and additionally prevent up to24
728,000 property-damage only crashes. The returns on these
two simple, but effective applications, are enormous. When
additional safety applications are considered (Do Not Passwarnings, Forward Collision warnings, roadway debris detection,and more), the value in terms of reduced death and injury, as
well as property damage, grows exponentially.
Key to many of these safety applications is the vehicle havingrobust geolocation functionalities. If the vehicle is not able tocommunicate its location, then the content of its data is mademuch less useful, For example, if a given vehicle is experiencing
a catastrophic failure, but can’t properly communicate where itis while the failure occurs (perhaps sending faulty data, or evenno data), then other vehicles, emergency services, or otherentities are not able to take action to help. This could occur incase of a fire, part failure, or other factors. However, there is asilver lining - other vehicles nearby could register that said
vehicle is in danger, and broadcast its location on its behalf.
What is occurring here is effectively a cross validation ofgeolocation data, and that application has value far beyond
safety considerations. Location services are critical, and theability to harness a vehicle’s ability to sense its surroundings andgeolocation for highly granular location data is highly useful andmonetizable. This is true for safety applications as well as
commercial applications. Vehicles that are communicating with
24. John Harding, Gregory Powell, Rebecca Yoon, Joshua Fikentscher, CharleneDoyle, Dana
Sade, Mike Lukuc, Jim Simons and Jing Wang, “Readiness of V2V Technology for
Application”, US Department of Transportation, August 2014,
https://www.nhtsa.gov/staticfiles/rulemaking/pdf/V2V/Readiness-of-V2VTechnology-for-Application-812014.pdf

29© 2021 MOBI — CMDM Business White Paper

other vehicles in overall service of accident prevention anddriver safety have a major competitive advantage over vehicles
that are not doing so. And the highly granular location data
achievable through cross validation can be utilized directly inthe creation of new applications, or access to that data can besold to others - both of which are direct monetization avenues
for that geolocation data.
The CMDM Standard’s
underlying core services andsoftware infrastructure thatpower these simple safety
applications also power more
exciting and complex
applications, like coordinatedautonomy.

Moreover, discussion of these applications here is focused onwhat is achievable in the short term. But, like the value of theStandard and of distributed systems in general, what isachievable in the realm of V2X safety technologies is entirely
determined by the technological capabilities of the vehicles andthe network effects and adoption of the Standard. This meansthat as these technologies continue to progress at a rapid pace,and adoption of the Standard grows with them, safety
applications will, too, become increasingly robust, complex, andprevent more and more accidents. Consider a future scenario fully autonomous vehicles coordinating to safely traverse anintersection. Those vehicles must be able to capture andcommunicate information about their surroundings. Even further,
they must be able to process that information into actionable
steps and communicate those steps as their movement intention
to other vehicles nearby. This requires those vehicles to have asecure, distributed system for identifying themselves (otherwise,
they would not be able to verify the authenticity, and therefore
actionability, of information received from other sources), andthe ability to quickly and seamlessly share data about theirsurroundings. These are some of the exact core services that the
CMDM Standard focuses on - the same core services enabling
more straightforward, immediate term use cases like
communication of safety data, or geolocation cross-validation.
More generally, the applications that the CMDM Standard can
immediately enable will provide more value over time as
technology improves and adoption grows.

© 2021 MOBI — CMDM Business White Paper

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MEDIUM-TERM USE BENEFITS
As time goes on, network effects and adoption will increase as theecosystem matures. This means that there will be many more devices
connected to the ecosystem. Consider the environment in which thisecosystem is functioning - an urban space, with diverse modes oftransportation to traverse it. One fairly common option is personal vehicle
ownership, driving that vehicle on the city roads to reach a destination.
There are often subways, or overground trains, that move on fixed paths,
and carry large amounts of commuters. There are complex bus systems
that move many commuters across large geographic areas. There are ondemand rideshare services and taxis available, or on-demand motorizedscooters or bikes available for rent or purchase. There are numerous
modes of transportation operating in any urban environment.
All of these different modes of transportation have a few sharedcharacteristics. They all involve using a device (vehicle, bus, scooter, etc.)that is capable of generating data about itself and its surroundings. They

31 © 2021 MOBI — CMDM Business White Paper

often intersect with each other - for example, people taking the subway,then using a rideshare service for last-mile transportation. Or someone
driving their vehicle to a parking garage, then using an e-scooter to travel
the rest of their commute to work. Finally, they are all services. All involvea relationship between a traveler and a Mobility Service Provider. Theirservices are both providing direct value (in the form of transportation) to agiven user, as well as indirect value (to be realized further down the data
value chain) as they generate useful data about the environment, trafficand commuter flows, and much more. That data is moved from thesedevices (broadly, the vehicle layer) to be used in applications. For
example, rideshare vehicles expose their location and identifying
information to the rideshare application in order to deliver that data to
the rider. Municipal transit authorities utilize data on commuter flow inorder to prepare more resources for peak travel times. These mobility
services generate useful data, whose value is currently only realized by its
generating entity, within their silo.

© 2021 MOBI — CMDM Business White Paper

32Medium-Term Use Benefits
Although different modes oftransportation have manydifferent definitionalfeatures, they share corecharacteristics that allow
them to exist as equallyprivileged peers in a mobility
ecosystem.

The CMDM Standard provides
the necessary infrastructure
to support a connected
multimodal transportation
service, coordinatingscheduling, booking,payments, and othernecessities across different
modes of transportation.

The key is that, though transportation modes vary widely in theirform, operation, and entire role, these shared characteristics
enable them to all participate in one ecosystem. More and moretransportation modes will connect to that ecosystem as it
matures. And with them, their Mobility Service Providers, theusers of those MSPs, and their data silos will also connect to theecosystem. The CMDM Standard enables data to be securely
shared within that ecosystem layer. With a diverse collection ofdata-generating devices, organizations, travelers, and otherplayers, the ecosystem would represent a data source, for all
different types of mobility data, that is far, far more complete
and diverse than any other source of mobility data. Such a data
source is able to support a wide range of new services.
When the ecosystem is more mature, with ample mobility data
being produced and made available, organizations are able tocreate many more, exciting services. One example of such a
new service would be a connected multimodal travel planningplatform. The platform could construct trip plans involving manypotential transportation modes in order to optimize travel timeor other commuter preferences. By combining multiple modes oftransportation - for example, utilization of on-demand services
like rideshare vehicles or e-scooters for first/last miletransportation, along with a subway or bus route for the bulk of
the journey - would be able to create much more efficient,affordable travel for many people in urban environments.
The key to such an application is its ability to access thenecessary data from each mode of transportation, or, in otherwords, each mode of transportation’s ability to easily,interoperably, and securely expose its data to one application.
The CMDM Standard is precisely the mechanism for this. The
applications extend far beyond multimodal trip planning. Yet all

33 © 2021 MOBI — CMDM Business White Paper

ultimately require the same functionality - access to data, fromdiverse sources, that are nowadays generally inaccessible. In
the medium term, the CMDM Standard will allow such access,enabling organizations to offer exciting new applications. In
other words, the CMDM Standard, over time, will allow for thecreation, purchase, and sale of new mobility services.
The CMDM Standard allows
for entities to wed themonetization of their data to
the success of theapplications and services
powered by their data, whichis key to ensuring proper
incentives around data
sharing.

This paper has already discussed some of the difficult factors inreaching the production, scaled version of such a dataexchange ecosystem. As previously discussed, one of the mostsalient of those factors is the fact that nobody wants to share
their data for free. Companies extract value from that data, ddo not want to lose out on monetization opportunities
associated with the data they generate. In the real world, data
providers seek to monetize their data by themselves and would
require a comparable return from making that data available toothers. Making that data available in a data marketplace, inwhich data providers can structure commercial transactions fo
data access as they see fit, is key. For example, some data
providers will insist on repeated costs for each call to their
database, while others may accept one-time payments foraccess. The CMDM Standard provides the core services andinteroperability required to construct a marketplace that allows
for applications to easily make calls to data providers, fetchthat data, and pay for that access.
While providing structures for the monetization of data is key formarketplaces to reach production on a meaningful scale, there
are many examples of data sharing occurring without direct
monetization avenues. Over the last 30 years, many P2P filesharing services have gained enormous popularity and usage.
Many users offer their storage space and bandwidth, at nocost, to host files for other users to access. All of these services© 2021 MOBI — CMDM Business White Paper

34

Medium-Term Use Benefits
have relied on platforms, some of which were centralized, andothers open sourced or otherwise operated in a decentralized
way. This is fine in a B2C context, but when businesses are theusers, it is much less straightforward.
There is enormous value inbeing the owner of an
ecosystem layer, which
disincentivizes joining any
existing marketplace or
ecosystem. Having that
ecosystem layer be operated
in a neutral way, and having
network effects exist fromthe beginning are some of thedriving forces that led to theformation of MOBI.

There is much value in being the owner of an ecosystem layer,so joining any already existing ecosystem is highly
disincentivized. This is the problem of achieving network effects,
that there’s no incentive for anyone to be the first. This isprecisely why MOBI was formed - to have a community of
coordinating players to jointly deploy these systems. In themedium term, these network effects will exist, allowing for thecreation of data marketplaces that provide the ability tomonetize the sharing of data. The CMDM Standard addresses
the previously discussed pain points that have prevented thesuccess of previous data marketplaces. The success of thesemarketplaces are the singular most important piece of enabling
the monetization of data sharing in the ecosystem layer.

When both secure dataexchange and monetization
exist, the ecosystem reaches
an inflection point, as these
key barriers separating datasilos are finally addressed.

These two pieces - enabling secure data exchange andenabling the monetization of data shared externally - are two
key milestones in the development of the new mobility
ecosystem. It is these two factors that will truly catalyze theecosystem in its creation of new solutions and value. In the shortterm, the CMDM Standard enables many exciting applications,with truly profound outcomes. But it is when these two pieces
are achieved that the ecosystem reaches an inflection point, asdata providers will finally have a proper incentive system toshare their data externally. All of their combined data, within
this ecosystem, will ultimately become critical infrastructure,
supporting valuable and useful tools and applications for MSPs
and users alike.

35 © 2021 MOBI — CMDM Business White Paper

LONG-TERM USE BENEFITSOver a longer time horizon, the share of vehicles on the road with robust
V2X communication capabilities, powerful ECUs, vehicle wallets, andother key technologies will be very large. These vehicles will have ample
computing power - powerful enough to execute large scale computations
on bigger data. Equipped with the machinery and infrastructure provided
by the CMDM Standard (and other MOBI Standards andimplementations), these vehicles will be able to easily and securely sharedata and insights, allowing coordination. This coordination andcommunication will not only be happening between vehicles, but public
transportation (trains, busses, etc.), stoplights, and other infrastructure.
This fully realized interplay between traditionally siloed systems allows for
exciting use cases centered around coordination, especially within thearea of autonomous vehicles, big data, and edge computing.

37 © 2021 MOBI — CMDM Business White Paper

Autonomous vehicle systems developed to date do not rely heavily on V2X
connectivity. Instead, self-driving capabilities in most AV prototypes andearly production systems rely primarily on in-vehicle sensors and on board
computing to navigate safely through their environment. This “standalone”
autonomy is inherently limited by the data available to that vehicle’s
sensors. For example, on board sensors cannot see around corners,
through traffic, or over obstacles. “Coordinated” autonomy has enormous
potential to improve AV capabilities through greater data availability andconsensus. Faster vehicle connections and edge computing will erase
these limitations. Vehicles will be able to share information about roadconditions, hazards, and traffic. They will be able to share their intentions
and modify their behavior accordingly. Such coordination would present awide array of benefits - large scale reductions in accidents and smoother,faster transit with less traffic are two key examples. Coordinatedautonomy will far exceed the safety and efficiency of standalone
autonomy.

© 2021 MOBI — CMDM Business White Paper

38

Long-Term Use Benefits
In the long term, as thesesilos become fully bridged
and integrated, autonomy will
shift away from the current
standalone model, towards
coordinated autonomy formore robust autonomous
decision making.

Standalone autonomy is limited by the data availability it implies- purely what one vehicle is able to ascertain about its
surroundings with its sensors. The benefit of secure data sharing
and coordination, in part, is due to the increase in data
availability; rather than relying entirely on their own data,
vehicles are able to access and make actionable decisions
based on the data of other vehicles around them. In otherwords, the dataset that a vehicle can generate about its
surroundings is much, much smaller than the dataset that an
entire intersection’s worth of vehicles can generate about theirsurroundings. This is true both within an autonomous driving
context, as well as other contexts. Organizations seeking tocarry out analytics and extract insights from vehicle data for any
purpose gain a similar benefit - the smooth and frictionlesscreation of bigger, cleaner, and more diverse datasets than canbe extracted from one vehicle, or even one fleet of vehicles. It’s
when a vast majority of vehicles have the ability to carry out thiscommunication and data sharing where the resulting datasets
can provide the most value, and enable the most use cases.

Coordinated autonomy allows
for autonomous vehicles tomake decisions that rely not
only on the data that they cangenerate, but also the datafrom other vehicles andinfrastructure around them,
vastly increasing theavailable pool of informationthat informs driving
decisions.

Much of the use cases discussed above rely on a vehicle’s ability
to generate data, guarantee that data’s provenance, and shareit with peers (other vehicles, infrastructure, servers, etc.).
Modern, connected vehicles are already generating extremely
large amounts of data, with some low estimates at 25
gigabytes of data per hour, or up to 300 terabytes of data per
year. With hundreds of millions of vehicles on the roads, thisresults in truly enormous quantities of data being generated
every hour, which presents a myriad of challenges for OEMs,
telecoms, and other mobility players. Such large amounts ofdata aren’t able to be affordably transferred via cellular orother network solutions, which prevents the majority of that datafrom reaching server farms for storage and statistical analysis.

39 © 2021 MOBI — CMDM Business White Paper

Large computing power within
vehicles and otherinfrastructure, along with
their ability to securely
authenticate, share data, andcoordinate allows for larger
workloads to be processed on
the edge. Edge computing is
key to scaling the Internet ofThings.

This data is valuable and contains insights that can power
monetization and value extraction further down the data value
chain. However, much of this data is simply deleted, as there isjust no mechanism to handle it at scale; in other words, this issue
represents enormous lost economic opportunity. In the long term,
however, these vehicles will accumulate greater and greater
13
computing power, and with the
CMDM Standard, the ability tocommunicate and coordinate. So, instead of sending raw data
to a datacenter to extract insights, these vehicles could pool
their computational resources to carry out that statisticalanalysis in the vehicles themselves and send the resulting
insights to a server for storage. Such a system would quickly
result in gigantic savings from reductions in network costs alone.
Edge computing has been a large focus for the distributed
computing community, and the MOBI community believes thatthe widespread growth of connected and autonomous vehicles
represents one of the most exciting and salient opportunities foredge computing to provide cost savings and new revenue
opportunities.

© 2021 MOBI — CMDM Business White Paper

40

CONCLUSION
This paper has discussed a variety of factors that have hampered thegrowth of data marketplaces, even as the meteoric rise of the data
economy has clearly demonstrated that data itself has economic value.
The key is that raw data, itself, doesn’t provide any direct economic value.
Rather, the economic value of data comes from the services that rely on
the insights and analytics from data where economic value is realized.
That process - the path that raw data will take from generation toproviding direct economic return - is called the data value chain.
For organizations relying on data to power services, there is a direct costof revenue that is implied here. Successful extraction of insights from data
at scale implies the existence of a robust data pipeline, ample server
power and space, skilled data scientists capable of executing suchanalytics, and more. Naturally, after making such an investment,
organizations are reticent to make their data and insights open andavailable to others for free. Each organization would rather provide thatend service and capture its full value, rather than sell it to a competitor.
Additionally, since the value of data is subjective based on its use, there isno universal measurement of its value, necessitating individual
negotiations between organizations for each transaction. Moreover, if an

41 © 2021 MOBI — CMDM Business White Paper

organization does wish to share their data with another entity, theirsystems may not handle and record data in the same way, necessitating
costly integrations.
All of these factors have contributed to this odd reality, that in our
modern, service-based, and data reliant economy, there are no scalable
marketplace structures for data as a commodity. The result is a patchworkof siloed ecosystems, largely unable to communicate. The key to solving
this problem lies in standardization efforts and industry-wide coordinationand collaboration. Data sharing standards, monetization structures,
ownership models, governance, and much more must be rigorously
defined. But the CMDM Standard, and the MOBI community behind it,
represents a critical step in this journey. By standardizing data and coreservices, the CMDM Standard provides a robust infrastructure to support
secure data sharing between devices and entities. This is a foundational
layer, on top of which marketplaces and many other applications can bebuilt. With scalable structures that support the sharing, provenance, andvalue exchange of data, the CMDM Standard will provide theinfrastructure for a multitude of use cases across the mobility ecosystem.

© 2021 MOBI — CMDM Business White Paper

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MOBI is a nonprofit industry consortium of many of the world’s largest vehicle manufacturers,
along with many startups, NGOs, transit agencies, insurers, toll road providers, smart city
leaders, and technology companies working to accelerate adoption and promote standards inblockchain, distributed ledgers, and related technologies.
MOBI is creating simple blockchain-based standards to identify vehicles, people, andbusinesses in order to securely exchange and monetize data, and pay for mobility services,with the goal of making transportation more efficient, affordable, greener, safer, and lesscongested. MOBI itself is technology and ledger agnostic. For additional information about or
joining MOBI, please visit our website.

dlt.MOBI@dltMOBI

MOBI@dltMOBI

cmdm@dlt.MOBI