There Are New Markets for Industrial IoT Data
IIoT data amongst trusted, business
partners. This concept underpins present-
day interest in distributed ledger
technologies.
failure events for the purposes of developing
predictive algorithms). This could involve
users of machines from different suppliers
pooling time-history data for different
events (e.g. overheating, bearing wear, shaft
misalignment etc.) to create a learning base
for machine learning and pattern
recognition algorithms.
There will be greater scope for innovation
and new opportunities from an open
approach that encourages third-party
specialists, along the lines that Gold
Corporation experienced. A cautious first
step might be for data owners to release
data selectively and prove the viability of an
ecosystem that brings together analytics,
app-developer and service provider
partners.
H OW C AN I NDUSTRY R ESPOND ?
Industrial organizations are on the threshold
of new, IIoT opportunities. This holds true as
long as they initiate strategies around data
to complement their more traditional
product and service offerings.
In fragmented markets, an industry alliance
might be the right approach. It could act as a
neutral body to aggregate data for non-
operational issues. As an example, consider
Assuming that an organization’s executive
leadership accepts the need for an IIoT data
strategy, there are five avenues they can
3. Down-stream
Supply chain
1. Industrial IoT Organization
4. Join Other
Eco-system
5. Orchestrate
New Eco-
system
Data
Science
Data
Assets
Innovation
Technology
Source: more-with-mobile.com (2018)
• Walled garden
• Open marketplace
2. Up-stream
Supply chain
Figure 3 Framework to Prioritize and Target IIoT Data Opportunities
how manufacturers might develop a failure-
mode database for high-availability
machines (i.e. analysis of low incidence of
pursue. We can map these in the context of
an industrial organization and business-
partner interfaces.
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June 2018