Creating a successful Internet of Things data marketplace
Monetizing
the flood of information generated by the Internet of Things requires a
well-executed strategy that creates value.
The Internet of Things
(IoT) will turn the
current rush of industrial data into a rogue wave of truly colossal
proportions, threatening to overwhelm even the best-prepared company. As the
gigabytes, terabytes, and petabytes of unstructured information pile up, most
organizations lack actionable methods to tap into, monetize, and strategically
exploit this potentially enormous new value. McKinsey research reveals
that companies currently underutilize
most of the IoT data they collect. For
instance, one oil rig with 30,000 sensors examines only 1 percent of the data
collected because it uses the information primarily to detect and control
anomalies, ignoring its greatest value, which involves supporting optimization
and prediction activities. One effective way to put IoT data to work and
cash in on the growing digital bounty involves offering the information on data
marketplaces to third parties.
How a digital marketplace creates value
Digital marketplaces
are platforms that connect providers and consumers of data sets and data streams,
ensuring high quality, consistency, and security. The data suppliers authorize
the marketplace to license their information on their behalf following defined
terms and conditions. Consumers can play a dual role by providing data back to
the marketplace .
Third parties can offer
value-added solutions on top of the data the marketplace offers. For example,
real-time analytics can make consumer insights more actionable and timelier
than ever before. The marketplace also has an exchange platform as a technical
base for the exchange of data and services, including platform-as-a-service
offers. Six key enablers of the data marketplace can help companies put their
data to work more effectively:
·
Building an ecosystem. By assembling multitudes of third-party
participants, companies can increase the relevance of their own digital platforms.
·
Opening up new monetization
opportunities. Today’s interconnected and digitized
world increases the value of high-quality data assets while creating innovative
revenues streams. One digital marketplace, for example, adds value to Europe’s
electric-automobile market by providing information and transactional gateways
for businesses such as charging-infrastructure providers, mobility-service
players, and vehicle manufacturers. Charging-station operators, for example,
are free to determine their own pricing structures based on data available
about customer habits and market trends.
·
Enabling crowdsourcing. Data marketplaces make it possible to share and
monetize different types of information to create incremental value. By
combining information and analytical models and structures to generate
incentives for data suppliers, more participants will deliver data to the
platform.
·
Supporting
interoperability. Data marketplaces can define
metaformats and abstractions that support cross-device and cross-industry use
cases.
·
Creating a central point of
“discoverability.” Marketplaces offer customers a central
platform and point of access to satisfy their data needs.
·
Achieving consistent data
quality. Service-level agreements can ensure
that marketplaces deliver data of consistently high quality.
Designing a data-sharing platform
As they consider the
process of setting up a data marketplace, company leaders need to work through a
number of critical questions. An enterprise might ponder the following issues
as it clarifies its data-market strategy:
What is the data
marketplace’s scope? In most cases, a
data marketplace begins when companies set up a central exchange for data
within their own organizations. Later, they determine which categories of
information within that internal exchange are appropriate (from a security and
a profitability perspective) and then allow other players outside their
organization (and perhaps outside their industry) to access that data.
How is the marketplace
best structured? To foster a
dynamic ecosystem, the data marketplace needs to assume a neutral position
regarding participants. The legal/tax entity that the marketplace becomes and
the structures that govern and finance it are key to this neutrality. Among the
guiding principles that players follow in setting up data marketplaces are that
a) the marketplace must finance itself through transaction-related fees and
commissions, and b) neutrality must extend to future participants that provide
or receive data or services, offering indiscriminate access to all interested
players under fair terms and conditions. And while the data marketplace will
support the creation and definition of data licenses, the data suppliers must
nevertheless take responsibility for enforcing and legally auditing them. With
respect to the marketplace’s governance, two business models are leading the
way. Data marketplaces tend to be either independent platforms or limited
ownership hybrids. Under the former model, data sets are bought and sold, while
fully owned data-as-a-service providers sell primary data in specific segments
or with services and solution wraps. Under the latter, the marketplace collects
and aggregates data from multiple publishers or data owners and then sells the
data.
Who are the data
marketplace’s customers? Once
the marketplace is commercially viable, customers will include all types of
data providers, and the marketplace system should actively source new kinds of
data to become more attractive. The key providers of data will be the companies
that capture it, own it, and authorize its sharing. At some point, however,
application developers will offer infrastructure and support services that
further increase the value of the data by offering a relevant analysis of it
and facilitating its delivery.
What are the marketplace’s
overall terms and conditions, and data categories? During the marketplace’s
technical setup phase, data suppliers define their licensing conditions
independently, and the platform provides benchmarks for licensing conditions.
The overall terms and conditions of the marketplace apply to all traded data.
In the subsequent commercialization phase, the marketplace relies on centrally
defined data categories and related licensing agreements as expressed in its
general terms and conditions. This strategy enables players to license
crowdsourced data independently of specific suppliers.
How does the
marketplace relate to other licensing models? When dealing with proprietary data, suppliers
usually hold certain information apart and do not share it in the marketplace.
However, data suppliers that also offer services can make use of their
proprietary data to create services they can trade on the marketplace. For
other licensed data, information suppliers can freely create licensing
agreements that extend beyond the marketplace—for example, with their strategic
partners. Both data amount and type, along with the scope of licenses for using
the information, can vary from that of marketplace-supplied data. Likewise,
suppliers can also impose separate licensing arrangements for data already
traded in the marketplace if buyers intend to use it under different
conditions.
What are the role and
value-creation potential of the marketplace company or participating data
brokers? The potential
value of the data will differ depending on whether the marketplace is in the
technical start-up phase or has achieved full commercialization. In
the former, the marketplace acts as a data normalizer, defining standard data
models, formats, and attributes for all of the traded information. It
syntactically verifies all incoming data compared with the defined standard and
continuously manages and extends the data inventory. Once the marketplace
enters the commercial stage, it becomes a data aggregator. At this point, in
addition to normalizing data and verifying incoming information, it aggregates
data and organizes it into logical bundles. For instance, it will enable users
to combine data for a given region and offer it to service providers.
Choosing a monetization model
While traditional
licensing will provide marketplace revenue streams, participants can also
develop transactional models to monetize data and services, with on-demand
approaches constituting the preferred approach. With traditional licensing,
companies can pursue either perpetual or one-off deals and collect customer
fees using several approaches. For example, they can sign contracts with fixed
fees and run times, renegotiate expired contracts, or earn revenues at the time
of sale (this final approach typically provides less stability in revenue
forecasting). At the transactional level, the two primary alternatives are
on-demand and subscription services. With on-demand services, customers either
pay as they go or choose volume pricing and pay charges based on metrics such
as usage volume, the number of incidents, or hardware-related fees.
Subscriptions can involve flat fees—typically applied on a monthly or yearly
basis—or free/premium (“freemium”) offers, which provide the basics free of
charge while offering additional features for a flat fee.
Another monetization
option is the “give and take” model, which offers incentives to data providers
to share their information. The incentive can be monetary or take the form of
something like highly relevant, aggregated data as an enticement to share. The
marketplace then aggregates and anonymizes the data and offers it along with
associated data-focused services to customers.
One give-and-take
example is an Internet-based service that offers geolocated real-time aircraft
flight information. The service reportedly has one of the largest online
aviation databases, covering hundreds of thousands of aircraft and flights as
well as large numbers of airports and airlines. Data suppliers receive free
radio equipment that collects and transmits aircraft data and a free business-level
membership to the service worth $500 a year for as long as they transmit data.
In another case, a large European credit bureau offers credit-rating
information for consumers and corporations. Data suppliers provide information
that includes banking activities, credit and leasing agreements, and payment
defaults. In return, they receive credit-ranking data for individuals or
businesses. Yet another give-and-take marketplace focuses on data and
performance analytics on mobile-operator network coverage. It trades apps and
coverage information to data suppliers in exchange for crowdsourced data that
can generate mobile-network coverage maps and reveal a mobile operator’s
performance by region and technology (for example, 3G or 4G networks).
Assessing the competition
A wide variety of
traditional commercial data services currently exists, although these services
are largely in silos that focus on specific topics, such as healthcare,
finance, retail, or marketing. This balkanization provides an opportunity for
new, more holistic data-business models. One advantage of the current ubiquity
of data providers is that most companies are already familiar with dealing with
them. In fact, some sources estimate that 70 percent of large organizations
already purchase external data, and all of them are likely to do so by the end
of the decade. The value potential inherent in data marketplaces is attracting
key players from a variety of advanced industries. A number of aerospace
companies, for example, offer systems that provide guidance to customers in
areas such as maintenance and troubleshooting. Similar efforts are also under
way in the agricultural and mining-equipment industries, among others.
The IoT’s big data
promises to help companies understand customer needs, market dynamics, and
strategic issues with unmatched precision. But in pursuing this goal,
organizations will amass previously unimaginable quantities of information. The
data marketplace offers them an innovative way to turn some of that data into
cash and reap the benefits that will accrue from building a self-reinforcing
ecosystem, enabling crowdsourcing, supporting interoperability, satisfying
customer data needs, and improving data quality.
By Johannes Deichmann, Kersten Heineke,
Thomas Reinbacher, and Dominik Wee
http://www.mckinsey.com/business-functions/business-technology/our-insights/creating-a-successful-internet-of-things-data-marketplace?cid=other-eml-alt-mip-mck-oth-1610
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