How to build a successful business model with big data platforms?Jos A.A.M. Verstegenjos.verstegen@wur.nl1Bart Doorneweertbart@source.institute0Source InstituteWageningen University and Research2017
A product lifecycle approach is used to describe the origin, history and development of current big data platforms, using literature sources, business case descriptions, interviews held with founding team members of platform businesses, and experiences in action research projects on big data platform development in Dutch agriculture. The paper concludes with an outlook on future developments and business models.
Thanks to great developments in sensor-, information-, and
communication technology large quantities of data are
becoming available at real-time and at low costs. This opens
the door for businesses to receive information on
supplychain variables such as delivery status, processing failures,
quality control, etcetera. On top of that, the granular usage
data, coupled with direct communication with end-users
allows to evaluate user satisfaction and to provide them with
more personalized products and services.
Platform opportunities emerge when the accumulated data
on a specific customer or user group becomes so insightful,
that it is possible to develop related services for a second
complementary customer or user group who wants to gain
access to the prior. For instance, search engines or social
media users generate profile data, which can service a group
of advertisers who seek to target those specific users.
Positive network externalities (also referred to as the
network effect) arise meaning that a good or service becomes
more valuable when more people use it [
1
]. The detailed
information about user preferences itself offers a resource
for new business opportunities. It is for that reason that data
is something referred to as the new oil [
2
].
Ignited by the promising market opportunities, the number
of big data platform initiatives are numerous. Many
companies are looking for ways to ensure long-term access
to big data. For this reason Monsanto, a large crop protection
company, acquired The Climate Corporation, a digital
agriculture company that examines weather, soil and field
data for $930 million to “unlock new value for the farm
through data science” [
3
]. Many other companies, either
seeing great opportunities or fearing to be disrupted, want to
follow suit and aim to develop their own platform. Yet,
developing a big data platform is no easy ride, and there is
no clearly set path to build a successful platform.
The objective of this paper is to describe the patterns and
stages that can be distinguished studying the origin, history
and development of current big data platforms, in order to
provide a context and inspiration to (clusters of) companies
and other institutions that consider or are in the process of
developing a big data platform.
2 METHODOLOGY
In this study we combine industry & academic (grey)
literature resources, including business case descriptions,
with interviews held with founding team members of
platform businesses, and our experiences in action research
projects on big data platform development in Dutch
agriculture [
4-6
]. In these public-private partnership research
projects Wageningen University and Research acts as a
knowledge broker providing both technical support on data
exchange protocols and support on business model
development and related governance issues.
We will use the product lifecycle perspective as an outline to
describe the patterns and stages that can be distinguished
studying the origin, history and development of current big
data platforms, building on the business model lifecycle
perspective on two-sided internet platforms of Muzellec et
al. [
7
] and the preparation, spread, evolution (PSE)
framework of Han & Cho [
8
].
The paper will be structured according to the original
product lifecycle categories of Raymond Vernon [
9
]: “new
product”, “maturing product” and “standardized product”.
We argue that the ultimate form of a big data platform is not
a simple question of choice, but the result of the intent of the
owners of the platform on the one hand, and the experience
that participants to the platform allow to be expanded on the
core value of the platform on the other hand. Both factors
shape the extent to which a platform can feasibly grow to in
size, and scope. In the final section we will discuss the
implications of historical development patterns and stages
for current platform initiatives and elaborate on alternative
roles and business models for companies and other
institutions that want to engage in big data developments.
3 NEW PRODUCT – THE ORIGING OF BIGDATA PLATFORMS
Looking at the earliest phase of the lifecycles of today’s big
data platforms, two types of origins can be distinguished,
namely 1) emerging platforms where a platform opportunity
revealed, only after a product or service has gained traction
with a single participant group, and 2) intentional platforms,
where the outset was to involve two or more participants
groups from the beginning.
3.1 Emerging Platforms
Many of today’s platforms did not start-off as a platform but
evolved to it after realizing that the user base, e.g.
accumulated through a popular app, has potential for a
platform function. When the platform’s origin is a
conventional pipeline business model with one user group or
customer group, it means that another group should be
‘seduced’ to use the platform as well. Usually there is no
imminent need for the first members of this peer group to
join the platform; they can be contacted with the same ease
through other channels. Yet, with a growing number of peer
group members joining the platform it will evolve into a
more important channel increasing the urge for other peer
group members to join as well. Think how LinkedIn used its
website as a professional profile formatting service, before it
was useful as a professional networking service. (choosing
focus on one vertical).
The case of Glooko: Initially Glooko was launched as a
smartphone-based log of blood-sugar metrics for diabetics.
Before, diabetics had to read out the glucose levels from their
blood testing meters by hand. With Glooko this process is
automated using a smart cable connecting the glucose meter
and the smartphone. Glooko’s first bet was that they could
provide such a service via an app on the Apple App Store,
and generate revenues from app sales, as well as from sales
of the cable. Important part of this value proposition to
diabetics was that the cable was device agnostic, enabling
users to read out glucose data from a variety of device brands
(at the time, each glucose meter had its own cables, and
dataentry software). Once Glooko’s launching business model
gained traction with this early group of diabetics, it opened
the gateway for platform opportunities, where Glooko went
to look for ways for diabetics to share their data with their
physicians. Ultimately, it was aiming to tap into the
multibillion dollar opportunity of the insurance market. However,
none of these opportunities would be of any real value if
Glooko wasn’t able to onboard a substantial user base of
diabetic patients with a self-standing service that helped
them with the basics of managing their disease.
Consequently, Glooko’s next challenge was to have these
diabetic patients migrate to connect with other users on the
platform using the logbook functionality. The company had
no way of saying that their customers would be willing to do
that for certain, until they arrived at that point when they
could test it.
3.2 Intentional Platforms
There are always two or more participant groups to a
platform. But one of those participant groups is typically
more essential, and also decidedly more difficult to attract to
the platform than the other. Fishingbooker is an example of
an intentional platform, which can only work when two
parties are involved from the beginning. Our interview with
co-founder of Fishingbooker.com, Nemanja Cerovac, for
instance revealed how that platform had to deal with the
challenge of connecting people who wanted to book fishing
trips, and boat captains, who offer those trips.
There’s no rule to either supplying, or demanding platform
participants being the most critical to attract to the platform.
This varies according to context and platforms. Also, the
new roles that participants assume can be either accretive or
depletive. For example, consumers and producers can swap
roles in ways that generate value for the platform. Users can
ride with Uber today and drive for it tomorrow; travelers can
stay with Airbnb one night and serve as hosts for other , e.g.
customers the next [
10
]. But spotting the most difficult party
(side) to the platform can be done from the drawing board,
Cerovac said. You can imagine being able to attract people
who want to book fishing trips. But then to engage them with
the platform, there would need to be a credible offering of
boat captains on site who offer trips.
In Dutch agriculture we see that an intentional platform is
formed by a farmer organization and agricultural
cooperatives to facilitate farm data exchange and retain data
ownership at the members of the cooperatives, i.e., the
farmers [
4
]. Because most Dutch farmers already exchange
data with (one or multiple) of these cooperatives, there is
already a solid user base to start with.
3.3 Development of the platform’s business model
In the earliest phase of a product lifecycle a business model,
describing the underlying economic logic of how a business
can deliver value to its customers, has to become clear. The
same is true for a platform, where a two-sided (or
multisided) business model has to be defined. The biggest
challenge of creating a platform, regardless of whether it is
an emerging or an intentional platform, is to have appealing
value propositions for (at least) two groups of users. The
socalled chicken-or-egg problem [
11
] is a critical balancing
act, where first a group of the most hard-to-retain group of
users’ needs to be convinced to use the platform. That group
then needs to be suited with a matching engagement from
their matching parties. Each increment in the prior, needs to
be met with a proportional increment of the latter. This
balancing act is handwork at first, Cerovac said.
Fishingbooker onboarded its first fishermen through direct
contact, even going so far as travelling to the Florida Keys,
to have them sign up to the platform. Also Airbnb had initial
problems to get a good matching because the pictures of
accommodations of Airbnb listings were too bad to convince
anyone to book these. Having professional photographers
visit the hosts and make pictures turned out the solution.
Also, to develop the market in France Airbnb sent out teams
of people to organize parties, info sessions and other “on the
ground” activities to convince people to list their
accommodations on Airbnb [
12
].
3.4 The transition from push to pull
Platforms generally come to life, putting a lot of effort in
serving a specific small user group of early adopters, before
they can grow out to become the default of the market. The
first users are brought onto the platform by an active value
proposition push to on-boarding them. At a certain point,
data, and/or users will be accrue in the business model, and
can be used as a new productive resource, which enables a
new value proposition to be created [
13
]. Google for
instance, needed to collect enough users, and relevant data
before it could provide a targeting service to advertisers.
Typically the value proposition, e.g., the functionality of the
platform, evolves over time, e.g. as new insights based on
user behavior become available.
4 MATURING PRODUCT – THE GROWTHAND MONETIZATION OF BIG DATAPLATFORMS
After the “new product” phase, the platform concept has
become visible and has proven to respond to the needs of the
(two or more) participant groups. Now the challenge
becomes to find a cost-effective way to grow the user base,
and create revenue streams that are substantial enough to
generate a profit.
4.1 Critical resource accumulation
In the maturing phase one can expect that competitors may
want to copycat the concept. Therefore, the prerogative is to
find a sustainable basis for business growth, i.e., to
accumulate critical resources by finding enough participants
to expand the platform, and build its clout amongst internet
users. Bringing on a critical user group to the platform
increases the stickiness of the platform for others. Only after
stickiness is nearly instituted will most successful platform
owners think of harvesting value from it [
14
].
The case of Fishingbooker: “Growth is not an implicit
consequence of a platform demonstrating its value, but of
careful, and thoughtful engineering of a growth engine”, as
Nemanja Cerovac of Fishingbooker.com stated in his
interview. In the case of Fishingbooker the challenge after
successfully matching the first fishing boat captains with
fishing tourists was to find more captains to sign onto the
platform. Previously this was handwork, with cold-calling,
and in-person visits. But in order to grow sustainably, this
process needed to become more scalable. Naturally,
Fishingbooker turned to online advertising, targeting fishing
boat captains through Facebook. However they soon realized
that their targeting, and call to action in the marketing
message were not optimized yet, resulting in a cost per
signon that was too high to sustainably grow the business. In
response to this dilemma, the Fishingbooker team changed
their focus to Mauritian fishing boat captains, as recreational
fishing is also a big activity there, yet Facebook advertising
there only costed a fraction of advertising in the United
States. This move allowed the team to experiment with their
advertising message, and optimize it for conversion to sales
online. Once the message had been tested, and showed
results, the team then opened the advertising to the United
States again, but this time with result. It proved to be the
basis for Fishingbooker’s exponential growth, making it the
current go-to booking site for fishing trips in the United
States.
Like Fishingbooker, many other platforms have faced the
challenge of finding a cost-effective growth engine. Notably
Airbnb used the classifieds website Craigslist, as a growth
channel, where Airbnb users could easily post their rental
homes on a platform with a huge audience [
12
].
Emerging platforms may have the option to revert to their
pipeline business model but intentional platforms that start
from scratch building a user base face the risk that they never
reach enough credibility for their offering. If they aren’t able
to crack the challenge of onboarding of their hardest
customers up till a certain velocity, they will never be able
to attract enough people from the other side of the market to
that platform.
4.2 Sustainable revenue streams
Wessel et al. [
14
] argue that “digital companies should delay
profitability for as long as they can”. Because of the network
effect “it will always be better to harvest value after further
increasing the stickiness of the platform”. Yet, only a limited
number of platforms receive sufficient funding from
investors to actually fuel their growth, but under very
specific conditions. Notably the taxi ride hailing app Uber
has received a lot of funding to fuel its growth. The condition
it met for this was that it had a great organic growth engine
to attract drivers, and passengers, and service them with a
profit [
15
], but that this model wouldn’t easily spread to
other geographies. Therefore it needed to be subsidized to
achieve this, spending an estimated $1,55 on every $1 it
makes [
16
]. Therefore Uber is applying a lot of investor
money to settle in a region, and then get its regionally
profitable business model implemented [
17
].
Most companies can’t follow Uber’s path because they have
investor bases demanding profit maximization today. As for
the cost-effective growth, this is a major hurdle for many
platforms. Most have to bootstrap fund their growth
engineering, and will either remain a niche or lose the
business if they fail to attract enough funding. That’s why
companies look for ways to draw value from the platform
thereby changing functionalities, introducing subscription
fees or including advertisements. However, these changes
are not without risks because they are frequently met with
scorn from user groups, who dislike the change in user
experience. Popular platforms like Twitter and Quora also
face this delicate balancing act as they search for ways of
generating revenue through the core value that they provide.
The challenge (and danger) here is to not lose grip on the
core utility, and user base in the process. Changes are
constrained by where the users want or permit value creation
to travel. Finding the right balance requires experimentation,
e.g., agile development using minimum viable products [
18
],
to find out what mix of participants, and platform services
can be combined together to create or maintain an optimal
platform experience. At the same time, frequently upgrading
the platform’s value proposition is essential as well.
Otherwise users may defect to another platform e.g., with a
more international scope, superior functionality or other
appealing features. In the case of social networks, it may be
wise for companies to have alternative platforms and
communities because typically young people tend to defect
a certain social network after a while, e.g., when their parents
have joined the social network [
19-20
].
The case of Instagram: Instagram was initially a platform
where users could share their photo’s online with their
followers. The attraction to users was the ability to apply
various kinds of filters on photographs to give them extra
effect. During the several years Instagram’s user base has
grown, it is increasingly being used for lifestyle marketing,
to promote personal brands. This evolution towards
marketing happened autonomously, driven by users
themselves. This despite the fact that Instagram is not fully
equipped as a marketing service. For instance, the use of
URL’s in comments, and in photo captions is prohibited,
making it hard to convert traffic to other destinations.
There’s no doubt that Instagram was a great hit to its users.
With its launch in 2010, it grew to one million users in two
months’ time [
21
]. Yet the business model didn’t have a
solid revenue stream yet. To mediate this, Instagram has
been working with different options for advertising
revenues. A notorious move by the company to strengthen
itself as an advertising platform was the change in the terms
of service in 2012, In this change Instagram provided for
some essential provisions to enable them take ownership of
content provided to the platform, and to show advertisements
in conjunction with users’ content. This raised a big backlash
from users [
22
]. As a consequence Instagram tweaked its
policy, and didn’t start with advertising until 2013 [
23
].
Currently even, it is still experimenting with new advertising
products, like the most recent one for business accounts with
which it is backtracking on some of the ways that leading
accounts use the platform for online marketing [
24
].
5 STANDARDIZED PRODUCT – ALL SETFOR FURTHER EXPANSION BYFANNING OUT OVER MULTIPLEINDUSTRY VERTICALS
Once a company has tackled the complexity of offering a
platform at scale, effectively captured an interaction between
participants and firmly established stickiness to the platform,
the quest then turns to applying the resources that are
accruing to the business model (user bases, data,
infrastructure) to fan out over multiple industry verticals.
This may take the form of expanding to new markets, or
fulfilling a new role in the market. Even at this late stage, the
right step to expansion is still found through business
experimentation [
25
]. Eisenmann et al. [
26
] refer to the
“fanning out” phenomenon as platform envelopment:
entering another platform market combining its own
functionality with that of the target market in a
multiplatform bundle. For instance, Microsoft used its dominant
position with Windows operating system to promote
Windows Media player in the same bundle thus attacking the
till then dominant streaming media platform of
RealNetworks (Real) and Google used its dominant position
as a search portal to launch Google Shopping thereby
attacking the price comparison and market outlet services by
platforms such as Ebay and Amazon.
At a subsequent stage you can typically observe that the
earnings from offering web services (e.g. infrastructure and
access to user bases) to affiliated companies and developers
are becoming a more vital element of the big data platform
business model than the revenues from actually selling own
commodities on the platform. Industry experts are
hypothesizing that this is the final status in development of
the company, where it focusses on being the fertile landscape
on which other ventures can grow. Thompson [
27
] expresses
the evolution that is taking place at Amazon as follows:
“Amazon may have started as The Everything Store but its
future is to be a tax collector for a whole host of industries
that benefit from the economies of scale, and AWS (Amazon
Web Services) is the model.”
Uber is progressing along the same line of development as
Amazon. The algorithm used to connect drives, and
passengers can potentially be used for a whole range of other
services. Starting with a specialized offering of offering taxi
rides, the company is experimenting [
28
] with other logistics
applications like food delivery (UberEATS), and local parcel
delivery (UberRUSH), which potentially hold more revenue
for the company.
The case of Amazon: News of Amazon’s acquisition of
food retailer Whole Foods made headlines for 2017 [
29
]. For
many in the food industry this came as a revelation. But
when one looks at Amazon’s track-record with spreading
over different industry verticals, the acquisition is part of
how the business intends to develop over the coming time.
Amazon’s approach to industry has been systematic, and
backed by solid business experimentation rigor. Starting off
as an online bookstore in 1995, Amazon built a company
model that is able to continuously try-out new propositions,
and test new business models, whilst executing on existing
successes. Upon founding the company, Amazon
immediately took off on the market. It provided its users an
experience of huge selection of book title to choose from.
Through loopholes in the procurement system of large book
wholesalers, which would still ship small volumes of books
if they were part of large orders of books that were out of
stock, Amazon was able to keep inventory costs low, and
still offer a wide variety of choice to its customers. The
company was such a hit, that after launch, it only took
Amazon 2 months to make sales from all states of the United
States, as well as 45 other countries. Early growth was
realized by listings on search engines like Yahoo, and
Netscape, citing Amazon as a great resource on the tips these
sites used to suggest to its user on their landing pages. Also
Amazon built affiliate programs as early as 1996, where
people who shared their reviews of books online, could offer
an Amazon link to their readership, where they could buy the
book. Affiliates were then offered a commission on those
sales. These measures, along with a host of other features
that boosted customer conversion to sales, and retention to
the platform, generated Amazon’s sticky growth engine. The
platform grew to a million served customers in 27 months,
and became the United States’ 3rd largest book seller by
1998 [
30
]. From 1998 onward, Amazon went into sales of a
broader range of products, starting with music, but also
adding categories like toys, garden furniture, and apparel
over the subsequent years. With every step in expanding
their product range, they also were attracting a broader range
of participants to the platform (even opening the platform to
3rd party resellers in 2000), and deepening the development
of its technology, and data infrastructure. This progression
as a platform has enabled Amazon to take a different look at
its technology resources. Amazon has the data and the
infrastructure to develop new offerings for a wide variety of
different markets. This technology infrastructure comes at a
very high fixed cost, and is only economically feasible at
scale, which keeps competitors from duplicating this model.
But the technology infrastructure has become so extensive
that it can support other business to run on it as well. Amazon
discovered this ability when it was working with larger 3rd
party vendors like Target, and Marks & Spencer’s online
efforts. It even contributed to a more optimal utilization of
resources [
31
]. This realization gave birth to Amazon Web
Services, which is an operating system to the internet for
other developers.
6 DISCUSSION AND OUTLOOK
Above we have described the patterns and stages of
development of big data platforms. The product lifecycle
categories of Vernon (1966) offered an adequate structure to
discuss the development paths of the big data platforms we
have today. But what does this mean for organizations that
are now looking for opportunities to start building a big data
platform? Is this still feasible or has the world market been
divided by the large corporations such as Amazon, Google
and Facebook who are very keen not to lose their dominant
positions and make sure to take over the companies that may
become a threat to them such as WhatsApp, Instagram,
YouTube, etc. And what if you do not want or cannot
generate the resources to build your own platform but still
want to benefit from the large market opportunities of big
data. What alternative business models are available?
6.1 Does the winner take all?
Today we see big data platforms highly dominating certain
markets: Alibaba accounts for over 75% of Chinese e‑
commerce transactions, Google accounts for 82% of mobile
operating systems and 94% of mobile search, and Facebook
is the world’s dominant social platform [
10
]. It is obvious
that those companies have a strong position but there will
always be room for new entrants, especially when those new
entrants manage to develop a distinctive value proposition
and generate or collect enough funds to build market share.
Back in 2007 the five major mobile-phone manufacturers—
Nokia, Samsung, Motorola, Sony Ericsson, and LG—
collectively controlled 90% of the industry’s global profits.
Nokia and the others had classic strategic advantages that
should have protected them: strong product differentiation,
trusted brands, leading operating systems, excellent
logistics, protective regulation, huge R&D budgets, and
massive scale. For the most part, those firms looked stable,
profitable, and well entrenched. Yet, in 2015 Apple’s iPhone
singlehandedly generated 92% of global profits, while all but
one of the former incumbents made no profit at all (Van
Alstyne et al., 2016). Airbnb also started as a small company
at a time that Craigslist was the dominant website for
offering private accommodations for rent. However, Airbnb
managed to become the dominant platform for booking
accommodations via the development of a stronger value
proposition by avoiding the scam on Craigslist and arranging
high-quality pictures, and by ‘wickedly’ piggy-backing [
11
]
Craigslist’s user base [
12
]. By doing this Airbnb unbundled
the multi-purpose portal of Craigslist. A similar unbundling
happened with AOL that offered clients a large bundle of
services from dialup to all the information services that you
use, all in one thing. Yahoo came along and unbundled all
the content from the access. And then one of the features of
Yahoo was search, Google came along and unbundled
search. The fanning out on industry verticals and above
examples show that bundling and unbundling of products
and services is a continuous process, often facilitated by
underlying technology change but always directed towards
increasing customer value [
32
].
Incumbents from more traditional industries are also waking
to the need for business model innovation, and
experimenting with new ways to better service their
customers. Recently Disney announced that it will be
starting its own, dedicated movie streaming service, and is
going to pull distribution licenses to other services, like
Amazon, and Netflix by 2019 [
33
]. This business model
innovation implies significant changes to competitive
balances, as Disney movies, including big titles like Star
Wars, and Toy Story, tend to draw large numbers of viewers.
In the same way, the recent Whole Foods acquisition by
Amazon, is also likely to provoke changes with incumbents,
suggesting that the battle for supermarket retail has stepped
up pace, and is not decided as of yet.
Another reason to believe that there will remain room for
new entrants is the fact that ancient transaction costs theories
[
34
] will also remain valid in this ‘new economy’ (although
equilibria may shift considerably). Sure, positive network
externalities exist meaning that a good or service becomes
more valuable when more people use it [
1
]. This will fuel the
emergence of large, international corporations with gigantic
user bases and various industry verticals in different parts of
society. Yet, these conglomerates will also face diminishing
returns to management and thus increased costs of
organizing a large firm, particularly in large firms with many
different plants and differing internal transactions (such as a
conglomerate). This is especially true when frequent
innovations are essential to prevent users (producers,
consumers, developers) from defecting to other platforms,
making the conglomerates vulnerable to disruption.
Moreover competition authorities and societal resistance
will limit the growth of conglomerates. Stickiness of a
platform can also become too extreme when denying proper
access to or listing of third parties prevents those parties
from having fair competition. Platforms thrive on new ways
of connecting participants, enabling them to share
information, and organize business exchanges. Often these
ways of working are so novel that the legal implications of
the platforms business mode are not clear yet during the
earlier stages of the platform. But eventually as platform
growth pushes on, and as it starts fanning out to different
verticals, legal frictions start to become apparent. An
increasing amount of lawsuits of competition authorities
result in high penalties for platforms allegedly abusing their
dominant position.
The European Union recently has fined Google a
recordbreaking €2.42 billion for antitrust violations pertaining to
its Google’s Shopping search comparison service [
35
]. In
The Netherlands, law suits were held by real estate brokers
to get access to and a proper listing on the dominant portal
Funda.nl [
36
]. Uber’s business model thrives on independent
drivers as customers to its platform, not as employees. But
recent labor lawsuits indicate that Uber has a significant
employer responsibility towards their drivers, which would
make its business model significantly less scalable than it
currently is. Other legal liabilities, like Uber’s infringement
of local taxi transport regulations, would jointly have such a
large exposure, that it exceeds Uber’s valuation, and cash at
hand [
37
]. The same goes for Amazon as it will face scrutiny
from competition authorities following acquisitions like
Whole Foods. Airbnb is another platform that has faced legal
challenges, notably regarding city taxes. As the platform
grew, it was able to do so without being noticed much by the
city authorities. But as the platform grew, and existing
hospitality services started pointing to the unfair advantage,
Airbnb was faced with tax compliancy, and a significant
administrative burden, and friction to the platform’s growth.
This uncertainty to growth comes at time, that Airbnb is also
looking for new directions in which to expand the platform
into the travel industry [
38
].
6.2 Other business models
What other business models exist if you do not want or
cannot generate the resources to build your own platform but
still want to benefit from the large market opportunities of
big data? Chen et al. [
39
] describe a chain of big data
applications that can form the starting point to develop
alternative business models besides offering a platform.
Kempenaar et al. [
40
] have summarized their chain in six
chain stages. Business models can be developed around
products and services on data capturing, data storage, data
transfer, data transformation, data analytics and data
marketing. It would take another paper to elaborate on all
these models individually but it is clear that the market
opportunities from big data platforms will also boost new
market opportunities in other parts of the data chain, ranging
from more requests for sensor companies to capture data to
more trusted advisors who can filter, combine, analyze, and
interpret big data flows to come up with relevant information
to support specific customers. Such a trusted personal
advisor would then no longer be exclusively for the very
wealthy people on our planet but thanks to big data could
become available to everyone everywhere [
41-42
].
ACKNOWLEDGEMENTS
This work was partially supported by the Data Fair NL
TKIAF-16101 Project and the Personalised Nutrition and Health
TKI-AF-15262 Project.
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