There Are New Markets for Industrial IoT Data
#5 Orchestrate New IIoT Data Eco-system
involving supply-chain partners and new,
third-parties. This will depend on IIoT data
owners taking bold steps to treat data as an
industrial asset, similar to volume-produced
machines and factory tools.
A more ambitious prospect is to create and
orchestrate a new IIoT data eco-system.
Open data portals, as in the case of London
Datastore, and data exchanges 15 are
examples of pioneering efforts to explore
new market opportunities.
By adding the data-equivalent of hardware
features
to
machines,
industrial
organizations will discover new business
models to capture value from their data.
Many of these features will be familiar from
the hardware world – leasing and ownership
rights, certification marques, service level
agreements, and, warranties etc. – although
their implementation will have to deal with
assets that exist as software.
The challenge with open initiatives is one of
scope. In addition to the issue of sourcing
and sharing IIoT data, the eco-system
orchestrator also has to address issues of
data certification and licensing as well as
policing the operations of participants to
prevent rogue behavior.
An alternative approach is to pursue walled
garden strategies. This allows a coordinator
to manage data-exposure and proprietary
algorithm risks by limiting access to a
verified group of business partners.
New business models will force industrial
organizations into new directions, opening
up the possibility of multi-sided business
models and individual assets being of value
to several customer segments. Industrial
firms will have to develop packaging,
distribution and trading relationships in
peer-to-peer environments and horizontally
integrated value-chains as distinct from
today’s vertically-aligned structures.
W HAT C ONCLUSIONS C AN W E D RAW
A BOUT II O T D ATA ?
Data has value, perhaps much more than
manufacturers and operators of industrial
plants currently realize. Lessons from the
consumer market show multiplier effect of
using more data, especially when combining
several different viewpoints.
These developments will cause certain
amount
of
industry
disruption.
Organizations that supply data-intensive,
industrial products and services look well
positioned to capitalize. Examples include
sensor manufacturers, instrumentation and
data-logging
specialists,
remote-
connectivity providers and alliances that
pool data for cross-population analyses. For
others, the challenge is to avoid being left
The challenge for industrial organizations is
to find ways of extracting and capitalizing on
IIoT data. Internal teams of data scientists
will unearth some of the opportunities.
Many more might be possible through open,
but controlled, data sharing strategies
15 ATIS: Data Sharing Framework for Smart Cities - http://www.atis.org/smart-cities-data-sharing/
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June 2018