ResearchPDF Available

Valuation of datacontributors to Platforms Ecosys

Authors:

Abstract

Data governance frameworks and data value chain models that may change dynamics forces in the new data driven economy
RESE A R C H P A P ER
By
NOHRA CHINA Catherine,
CEO B2CLOUD
December 2018
Valuation of data contributors to
platforms ecosystems
CEO B2CLOUD
Valuation of data contributors to
platforms ecosystems
1
ABSTRACT
INTRODUCT ION
THE VALUE OF THE DAT
A EC ONOMY
Data monetization business model challenges
Defining new data value chain
Changing dynamic forces in the data driven economy
Bringing back the consumers at the negotiation table
Considering data as property asset
Considering data as labor to break down the monopsony status quo
Exploring new user to
platforms ecosystems contribution models
CONCLUSION
BIBL IOGRAPHY
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The valuation of data contributors to
platforms ecosystems
A ECONOMY
Data monetization business model challenges
Defining new data value chain
TOWARD IM PROVMENT OF DATA GOV ERNAN CE FRAMEWORKS
Changing dynamic forces in the data driven economy
Bringing back the consumers at the negotiation table
Considering data as property asset
Considering data as labor to break down the monopsony status quo
platforms ecosystems contribution models
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Nohra China. 2018
platforms ecosystems
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ABSTRACT
PURPOSE
The purpose of this research paper is to present platforms ecosystems data based economic model, to analyze
the current data governance frameworks and to explore new data value chain that can benefit to all data
stakeholders along with data privacy issues, through the literature currently available
.
FINDINGS
Much of the value created through consumer information by 2020 will be due to online services and
platforms ecosystems. While relying on data monetization business model to support their growth, most of
the platform ecosystems missed to manage their data assets with improved data governance framework at
board level. More, we found that there is currently no data value chain model adapted to interconnected
societies and platforms ecosystem and that reflects the value created by data contributors. The survey that I
have conducted for the purpose of this paper reveals indeed that 94% of respondents are totally concerned
about the use of their private data on digital platforms and that 44.45 % of them will accept financial
compensation in exchange for private data sharing. The European General Data Protection rule will change
the dynamic forces in the data driven economy, by strengthening the consumer‘s protection with for instance
the right to object to the processing of personal data for commercial and marketing purposes. However, as
there are actually no rules that completely define data as property assets or intellectual property, the only way
to improve the valuation of data contributors to platforms ecosystems at European level, is to explore new
users to platform contribution model, such as financial compensation paid in exchange of user consents or
with regard to the final purpose of the use (collecting, aggregating, analyzing)
VALUE
This academic research paper provides a brand new perspective for the analyze of the platforms ecosystems
value chain through literatures review, think tank analyses and survey researches, along with legal
comparison. It provides with new data value chain proposal, data governance recommendations and legal
options to improve the valuation and the compensation of data contributors to platforms ecosystems at
European level.
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INTRODUCTION
In this research paper, we will explain through literatures reviews (Schreieck, Wiesche, & Krcmar, 2016)
why, as data collection and monetization has became during the last decades the most important growth and
fund raising factors for platform ecosystems, they meanwhile missed to manage their data assets with
governance strategic choices that could have improved value creation to consumers. (Hansen & Birkinshaw,
2007) (Miller & Mork, 2013).
We will propose a new data value chain model that could integrate all the data stakeholders and their
contribution effort for the companies value’ creation and we will provide with some recommendations to
improve the current data governance frameworks (Watson, 2016) (Lee, Zhu, & Jeffery, 2017).
Finally, we will analyze through legal and political perspectives (i.e. European General Data Protection Rule
–GDPR-) the various options to consider data as protectable assets, and the possibilities to bring back data
stakeholders at the negotiation table, not only to create and deliver the value to organizations, but also to
benefit from it.
.
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1. THE VALUE OF THE DATA ECONOMY
According to the (“The Value of Our Digital Identity,” n.d.), the value of personal data will indeed represent
€1 trillion in Europe by 2020, or roughly 8 percent of the combined GDP of the EU-27. For European
businesses and governments, the use of personal data will deliver an annual benefit of €330 billion by
2020—bringing growth to an otherwise stagnant economy.
The European Union Agency for Network and Information
security (“The Value of Personal Online Data — ENISA,” n.d.)
stated that the estimated ARPU – Average Revenue per User-
mainly controlled by Google and Facebook in digital
advertisement has reached $59 per person in 2017. With an
average of 3.8 billion active Internet users, we can estimate
generated profit made by these companies at $224 billion for
2017.
Competing for this new data rush gold, platforms ecosystems are indeed buying, trading and selling
consumer behavior, social and political orientation, money spending habits, health, lifestyle for online
targeted advertising, Internet ad-clicking business models and smart connected devices.
.
.
Data has surpassed oil as the
world's most valuable resource
and the enormous amount of data
that we all generate every day will
play a decisive role in the
economy of the future.
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1.1 DATA MONETIZATION BUSINESS MODELS CHAL LENGES
In an increasingly digital society, user’s data has indeed become of new form of currency and the biggest
challenge for political and business leaders is to establish the trust that enables that currency to keep flowing.
These accustomed users to surrendering data in exchange for free services were well described so
far.(Carrascal et Al, 2013). If many of the current platforms ecosystems (Smedlund & Faghankhani, 2015)
managed to raise a lot of capital to fund flawed business model, the fault, lies not with the concept of
Business Model, but with its distortion and misuse. “During the Internet boom, a company did not need a
strategy or a special competence, all it need was a web based business model that promised wild profits in
some distant, ill-defined future.” (Magretta, 2002). As these platform often need to change their business
models to develop a more profitable and viable model that has a compelling customer value proposition
(Garnsey, 2015), they also have to evolve in order to deliver a proposition that customers will be willing to
buy. (Demil and Lecocq 2010).
For instance, Spotify, a Sweden music streaming platform
reached a 26, 6% conversion rate in 2015 by providing buying
customers with real benefits such as no advertising, a higher
sound quality, offline availability and personalized music
playlist through algorithm based recommendation, along with a
very strong data confidentiality policy, whereas on average, a
good conversion rate for free to paid is only 4%.(Wagner,
Benlian, & Hess, 2013)
.
When unable to deliver a value proposition that compels to customers, some of the platforms ecosystems, as
Facebook and Twitter, were forced to raise value with advertising and user data monetization. They critically
missed that business models are also made of policies, assets and governance structures (i.e. contractual
arrangements that confer decision rights over policies or assets).
Choosing the use of data assets should indeed lead onto the governance strategic choice for the company whether
it should use them for its own business or sell it to a third party.
Indeed, making the strategic choice to use consumer data as asset, with no data governance structure is very risky,
specifically as, according to transaction cost economics –TCE- framework, “slight differences in the governance
of policies and assets can have dramatic effects on value creation and/or value capture.” (Casadesus-Masanell &
Ricart, 2010).
At this point, when changes occurs in the organization‘s policies, or assets, it may be efficient to separate business
model from strategy and to define a new data value chain.
User’s data has become of new
form of currency and the biggest
challenge for political and
business leaders is to establish the
trust that enables that currency to
keep flowing.
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1.2 DEFINING N EW D
ATA VALUE CHAIN FOR
However,
when data is provided by a user of a platform and access in a value net model by complementors
For instance,
the data value chain framework proposed by Noblis (Miller & Mork, 2013) examines “
enterprise level.”
Contributors
and
stakeholders
Bring
disparate
data
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ATA VALUE CHAIN FOR
PLATFORM ECOSY STEMS
Much of the value created and brought to the Platform
s ecosystems
came from user’s data (contents,
knowledge, preferences, consumer’s behavior etc). Data is usually mentioned in the literature, as a boundary
technology oriented resource for the co creation of the organization ‘s value
(Gawer & Cusumano, 2014)
when data is provided by a user of a platform and access in a value net model by complementors
(Nalebuff & Brandenburger, 1997), it should be analyzed through a market or a financial p
Whether it’s correlated to innovation usage (i.e. co
-
innovation) or to consumer information, these platforms
ecosystems enable all stakeholders, to create and bring value to the organizations.
Whereas in traditional organizations, the val
ue chain refers to a linear and transitive system of interconnected
activities that brings and creates value inside the organization.
(Porter, 1998)
This sequential and activities based value chain is no more suitable for interconnected societies and
platforms ecosystem, as they do not consider the value of the new constellation of users.
the data value chain framework proposed by Noblis (Miller & Mork, 2013) examines “
bring disparate data together (…) and create valuable information that can inform decision making at the
Bring
disparate
data
to provide
valuable
information
that improve
decision
making
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PLATFORM ECOSYSTEMS
came from user’s data (contents,
knowledge, preferences, consumer’s behavior etc). Data is usually mentioned in the literature, as a boundary
(Gawer & Cusumano, 2014)
.
when data is provided by a user of a platform and access in a value net model by complementors
(Nalebuff & Brandenburger, 1997), it should be analyzed through a market or a financial p
erspective.
innovation) or to consumer information, these platforms
ecosystems enable all stakeholders, to create and bring value to the organizations.
ue chain refers to a linear and transitive system of interconnected
This sequential and activities based value chain is no more suitable for interconnected societies and
platforms ecosystem, as they do not consider the value of the new constellation of users.
the data value chain framework proposed by Noblis (Miller & Mork, 2013) examines “
how to
bring disparate data together (…) and create valuable information that can inform decision making at the
that improve
decision
making
7
sequential, three-
phase process (…) through whom managers must perform six critical tasks
sourcing, cross-
unit sourcing, external sourcing, selection, development, and companywide spread of the
economy
(Normann & Ramírez, 1993)
Taking that in consideration, an
efficient
integrate
all the data stakeholders with their definition of roles and prescription level (
organization’ value creation, as
showed
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But as an IT and organizational value c
hain model based on a linear approach to data management, it do
not provide any specific enhancement to the Porter’s model.
In the same way, the innovation value chain
(Hansen & Birkinshaw, 2007)
presents innovation as a
phase process (…) through whom managers must perform six critical tasks
unit sourcing, external sourcing, selection, development, and companywide spread of the
idea. But with no real market perspectives to external contributors and stakeholders.
Indeed, it is well known that knowledge and relationship
(i.e. connexionism) with economic actors
suppliers, business partners, allies, customers
-
are part of the key resources that matters in today ‘s
(Normann & Ramírez, 1993)
.
efficient
data value chain model should not
be
multidimensional, not only economical
or technological centric but also legal
focused
all the data stakeholders with their definition of roles and prescription level (
expert, end user, community), including their data access rights, and their contribution efforts for the
showed
bellow:
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hain model based on a linear approach to data management, it do
es
presents innovation as a
phase process (…) through whom managers must perform six critical tasks
—internal
unit sourcing, external sourcing, selection, development, and companywide spread of the
idea. But with no real market perspectives to external contributors and stakeholders.
(i.e. connexionism) with economic actors
-
are part of the key resources that matters in today ‘s
be
linear but
focused
and it should
all the data stakeholders with their definition of roles and prescription level (
i.e. professional,
expert, end user, community), including their data access rights, and their contribution efforts for the
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2. TOWARD IMPROVEMENT OF DATA GOVERNANCE
FRAMEWORKS
There is however no chance of success to provide organizations and platforms ecosystems with new data
value chain unless improving current data governance frameworks.
We can find many definition of the data governance in the literatures, but the one provides by the Business
Intelligence Journal remains quite accurate as it considers “Data Governance as people, processes, and
technologies used to manage, protect, and use data so that organizations can leverage it as an organizational
asset (…) with the ultimate goal of creating value for the organization” (Watson, 2016)
Indeed, current data governance frameworks are focusing on generic goals and universal IT approach to
enterprise data management (document & content, data warehousing, master data management, data quality
etc…) whereas they shouldn’t be simply seen as an issue of technology.
There is no existing governance framework
study that actually addresses and /or
measure the contribution effort of platform
users and more over, the data ownership
definition and the access rights issues based
on users contributions.
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For that reasons, organizations that only consider data governance as IT or as business activity take the risk
to inadequately protect data assets.
Knowing that the governance of data is critical at the organization board level, not only to protect data but
also to add value for the entire data value chain, only a strong governance framework both focused on ethics
and legal issues remains the best possible option to peacefully face with the digital age.(Moens, n.d.).
As unable to identify a data governance model for the platform ecosystem, the School of Computer Science
of the University of new south Wales (Australia) has conducted last year a research study to understand how
data governance should be managed as involving multiples contributors through the entire data value chain
(i.e. data ownership, access, usage, profit sharing of collected, aggregated and derived data.) The
researchers managed to identify critical factors for four platform ecosystem data governance and they
highlighted how poor implementation or lack of data governance can have significantly destructive effects on
success. (Lee, Zhu, & Jeffery, 2017).
This study has the virtue of pointing out the needed for more
visibility into the data value chain to strengthen existing governance
models for platforms ecosystems. But with some limitations
however, as it only analyzes one hot spot of the data value chain,
missing the critical implication of the data broker’s chain to the lack
of transparency regarding consumer personal information.
It is to be noticed that the 2014’ Federal Trade Commission-FTC-
(“Data Brokers,” 2014) report totally disregarded that most of the
GAFA and NATU platform ecosystems have signed agreements
with the largest US data brokers for commercial data use.
In a 110 pages of “Call for Transparency and accountability” report, not one line is dedicated to data
governance principles. We know that the US Fair Credit and Reporting Act- FCRA- enacted in 1970 only
cover the provision of consumer data where it is used or expected to be used for decisions about credit,
employment, insurance, housing, and similar eligibility determinations, but it does not cover the sale of
consumer data for marketing and other purposes.
We also know that US privacy policies are mainly based on business recommendations and that there are no
strong regulations around data privacy, except for the Privacy Shield Program (“Privacy Shield Program
Overview | Privacy Shield,” n.d.) for data exchange between US and EU which remain purely voluntary,
comparing to the mandatory 2018 EU General Data Protection Rule-GDPR-(“Data protection,” n.d.)
Only a strong governance
framework both focused on
ethics and legal issues
remains the best possible
option to peacefully face with
the digital age
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2.1 C HAN GIN G D YNA MICS F ORC E S IN THE DATA DRI VEN ECONO MY
The EU GDPR-General Data Protection Rule- may definitively change the dynamic forces in the data driven
economy.
From the user perspective, GDPR will provide clear information about how the user’s data are being used,
giving them the right to delete them. From the provider perspective, cookies and targeted ads based will be
no longer legal.
For instance, a UK based company named Expressly and founded by previous Mc Kinsley Consultants, has
developed in 2017 a new marketing tool to help publishers to transform anonymous visitors into registered
users, with just one click user consent. The company provides publishers with a technology to transform any
link (e.g. banners, emails, SMS/social network, and native articles), into a "power link" that transparently
asks the person if they want to visit the advertiser's site, immediately creating a full profile using their same
existing data from the publisher. The CEO of Expressly explained that “getting to know users personally is
better than harvesting and profiting from their data behind their backs. When reading the privacy policy
terms and agreement of the company, it appears that they may collect automatically IP address, with no
previous user consent, and other private data (birth data, profession, preferences information), with no
explicit transparency regarding the user consent. More, the company use of data policy refers to the 1998
Data Protection Act and not to the 2018 European General Data Protection rule.
Driving by new mandatory regulation around
data privacy, new business models have yet
emerged to transform anonymous visitors into
registered users
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2.2 B RINGI NG BACK THE CONSUMERS AT THE NEGOTIATION TABLE
Some researchers have previously highlighted the social problems with the culture of “free” online, in which
users are neither paid for their data contributions to digital services nor pay directly for the value they receive
from these service. « One often attends to say that digital platforms should pay for the data we provide to. In
practice however, some of these platforms do it, not through a financial compensation, but through non
tariffed services. “(Jaron Lanier, 2013) (Tirole, 2016).
Basically, the “deal by default” was and still is that individuals could benefit from free access to online
services by accepting to give free access to their personal data in exchange: the benefit is the service
obtained, and the cost remains the loss of privacy. Do we not each day agree for hundred of cookies on our
computer and almost 1500 terms and conditions pages/year for free online consumption purpose? (Mc
Donald et Al 2008 p 543
)
On another side, a French Think Tank called “Generation Libre” and composed by lawyers, engineers, PhD
teachers and researchers is trying to shift the line, by proposing a new "patrimoniality" for private data (i.e. a
proprietary right instead of an intellectual right.) “ Even if the use of data enable to provide enhanced
services to citizen-consumer, and even if this improvement is a
form of value that answer real expectations, the share of value
returned to consumer, in the form of benefit is undoubtedly weak
compared to the total value gained from providers through the
resale of information. “ (Landreau, Peliks, Binctin, & Pez Perard,
2018).
The Think Tank is indeed trying to find a way to give users the
ability to directly contract with providers and/ or third party for the use of their personal data, while giving us
the right to monetize their data with providers with regard to new contractual terms, and the ability to pay for
a right to privacy.
Sounds challenging but still requires clarification around the legal aspect of personal data and specifically
around the data legal regime.
A French Think Tank tries to
propose a new patrimoniality for
the use of private data
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2.3 CONSIDERING DATA AS PROPERTY ASSET
According to the lawyer Christopher Rees, “the protection of the economic value inherent in personal
information should be grounded in property rights acknowledged by the law”.(Rees, 2014).
Since 1978, various laws and European directives used to define data as a property. Under the French
property law, the possession of data covered by confidentiality or by control (i.e. the owner behavior) gives
the owner the right of property (Dross, 2017). If data can be considered as goods that we can own and
control, they remain distincts from the person.
According to the French civil code: “The secret, the control or the intimacy allow a possessing right on the
incorporeal asset of which data that are voluntary controlled by a person”. Another very important aspect
that defines the French common property law is related to the exclusivity that ensures the owner to benefit
from its good and to eliminate other beneficiaries.
Following the logical scope of the French law, any private data can be sold, rent and transferred to another
party with an appropriate commercial agreement. But it’s quite more
complex.
Indeed, data status are also determined by the collector capacity,
whether it’s comes from a free collection or from the use of public
power prerogatives. Therefore, there is no right to data property,
except for the collector capacity on which the collect purpose remains
essential.
There is indeed no intellectual property that regulates data privacy and
control, but only property laws that can be applied as long as the
control is maintained by the owner.
In 2016, the French Law for Digital State (“La loi pour une République numérique,” n.d.) added new data
portability rights while devoting data control to the initial issuer. Whereas the French law establishes a right
for the free disposal of private data (i.e. right to oblivion, right to digital death; secrecy of private
correspondence), and whereas that the French property right establishes the right to the owner to sell or rent
data, the French council of state report established in 2014, that private data could not be monetized, in the
name of the protection of fundamental freedom. “The person being considered inalienable and not being a
subject of trade, the data annexed to her should be excluded from the market”.(Conseil d’Etat, France, 2014).
Otherwise, the acceptation of the individual property right on data could raise serious legal issues for public
institutions, which should then have to justify the collection and processing of citizen data for
the purpose of
public utility. To avoid those legal issues, the French law for digital state, has reinforced the open data
mechanism, thus preventing citizen from data property and heritage.
There is indeed no intellectual
property that regulates data
privacy and control, but only
property laws that can be
applied as long as the control is
maintained by the owner.
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2.4 CONSIDERING DATA AS LABOR TO BR EAK DOWN T HE MONOPSONY STATUS QUO
Others researchers recently considered that the most concern from data use will come later with Artificial
Intelligence that may replace human workforces while using their knowledge’s. “More broadly, many AI
systems depend on active participation by humans to generate relevant data. (Arrieta Ibarra et al.; 2017).
Instead of treating data as a natural exhaust from consumptions collected by companies (i.e. -Data as
Capital), they investigated whether or not data should be treated as Labor, and thus as users possessions that
should primarily benefits their owners. “Whereas DaC sees the online social contract as free services in
exchange for prevalent surveillance, DaL sees the need for large-scale institutions to check the ability of data
platforms to exploit monopsony power over data providers and ensure a fair and vibrant market for data
labor.”(Arrieta Ibarra et al., 2017)
But if users were aware about the total value created by their data, would they likely demand compensation
in exchange? The Ponemon Institute 2015 report (“Privacy and security in a connected life,” 2015) reveals
that US responders value nearly all their personal information higher than respondents from other countries.
Besides cultural differences, this could also be due to how much US consumers value their privacy, and how
their day-to-day lives revolve around their own personal information with the boom of social media.
As many AI systems depend on active
participation by humans to generate relevant
data, data should be treated as labor and as
users possession, benefitting their owners
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For the purpose of this paper, we also have conducted between February and May 2018 an independent
survey based on a European panel of mixed profiles of education level and gender, according to the
following topics: Does privacy matters? What kinds of protections measures are taken and do privacy and
personal data have a price? 47, 22% of respondents confirmed to be concerned about the use of their personal
data and 94.44% of respondents feel disturbed by the use of their private data for monetization purposes (Fig
1). The survey also highlighted that 50% of respondents will be willing to pay to access to digital platforms
in exchange for the guarantee of non use of their personal data, depending on the price to pay and 44.45%
will accept to share more private data in exchange for financial compensation, regarding the type of
information shared and the level of compensation. 55.56% will never accept to share private data for
financial compensation. There were no correlation between the level of completed education and the related
knowledge around data privacy and security issues, except for the financial aspect of the survey, as it seems
that the highest level of completed education (doctoral degree) are less willing to be paid for private data
exchanges.
These studies remain incomplete since they are only based on individual perceptions but it shows an
increasing awakening of citizens-consumers regarding the value of data assets.
To raise the citizen awareness of the advertising incomes that they generate to the benefit of platforms
ecosystem, some Spanish researchers have thus developed a very interesting approach with the Facebook
Data Valuation Tool. (FDVT, n.d.).
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2.5 EXPLORING NEW USER T O PLATFORM S ECOSYSTEMS CONTR IBUTION MODEL S
Considering the possibilities of bringing back the consumer to the negotiation table, while defining the new
data value chain comes necessarily the need to clearly define new options for user’s contributions to platform
ecosystems.
It could be built on financial compensation paid to the citizens, in exchange of their consents for the use of a
commercial exploitation or with regard to the final purpose of the use (Wright & De Hert, 2016) and
(Landreau et al., 2018)
1. The data contributor may be paid for a compensation of the raw data provided to data
aggregators. Different compensation mechanisms could indeed be provided with regard
to existent regulations, such as trademark and licensing contract, where a citizen may
register it data legacy as a trademark using for instance the NICE European “45”
classification (“Nice Classification (trade marks),” n.d.).
2. The right to use the data trademark could be given by licensing contract and could be
managed either by an insurance provider or a bank.
The main issue for that model is that beyond the obligation to pay for the data legacy trademark, the
trademark can be registered only for statics data and for a certain period.
Data contribution models could
rely on a declarative system based
on database creation, thus
requiring the citizen to become a
database creator
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Trading data using copyright and intellectual property are on the other side totally legal but very complex to
apply, as they require identifying the initial data issuer and could imply a lot of expenses in case of litigation.
3. The new data contribution model could be based on a declarative system based on
database creation, thus requiring the citizen to become a database creator (rather than a
producer), and as a result, the only owner of the raw data and the generated data.
With this option, the best alternative remains to apply a declarative system by usage, through a licensing
contract agreement with the data value chain provider, and preferably with the data collector.
It also requires to made adjustments with the Intellectual Property Code, such as introducing a new definition
for the database creator.
Some experiences around private data monetization have already been conducted in Europe, such as for
MiData in UK (“The midata vision of consumer empowerment,” n.d.), or MesInfos from the French New
Internet Generation Foundation (“english | MesInfos,” n.d.).
Where only very specific and local initiatives has emerged to provide new models for consumer’s
empowerment, one on the main questions that have been not solved so far remains how to specify a new data
value chain integrated in an enhanced data governance framework to improve the data contributor’s
valuation to platforms ecosystems.
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CONCLUSION
The initial objective of this research paper was to find out whether or not we could improve the valuation and
compensation of data contributors to platforms ecosystem at European level.
This is why it was decided to do this research first by conducting a specific survey which targeted European
platform ecosystems users, to get valuable results, then by analyzing the platform ecosystems business
models, data value chain and data governance frameworks and finally by searching whether or not data could
be considered as protectable assets, through legal and political perspectives (i.e. European General Data
Protection Rule –GDPR-.)
These are what we found.
While relying on data monetization business model to support their growth, most of the platform ecosystems
missed to manage their data assets through improved data governance framework at board level.
More, whereas much of the value created and brought to the Platform ecosystems (such as Facebook,
Twitter, YouTube, LinkedIn) came from user’s data, the vast majority of users declare to be disturbed by the
use of their data for monetization purpose, even if half of them will be willing to share more private data in
exchange for financial compensation.
As there is currently no data value chain model that reflects the value created by data contributors, and
no rules that completely define data as property assets or intellectual property, the only way to improve
the valuation of data contributors to platforms ecosystems at European level, is to explore new user to
platform contribution model as financial compensation paid in exchange of user consents, or with regard
to the final purpose of the use (collecting, aggregating, analyzing).
It seems that the European GDPR has now opened a valuable breach into the user data privacy debate,
by providing them with clear information about how their data are being used Usus , by giving them the
right to delete them and the right to be forgotten ‘i.e. Abusus, while providing also so the right to data
portability.
If considering later the data propertisation aspect (i.e. the fructus), then citizen valuations and
compensations for data contribution could be conceivable at the European scale, but could also generate
higher risks for opportunistic behaviors, and financial imbalance with non EU countries.
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BIBLIOGRAPHIE
Arrieta Ibarra, I., Goff, L., Jiménez Hernández, D., Lanier, J., & Weyl, E. G. (2017). Should We Treat
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