Data from IIoT machines and operational applications allows industrial organizations to collaborate and innovate in new ways. Think of data as a different type of lubricant for industrial processes. But that is not its only use.
The data exhaust from connected machines and processes in industrial firms has several alternative uses. For evidence, take a look at similarities with the e-commerce arena, where data holds economic value. As a result, user profile and transactional data have gained currency as a tradable asset in a completely new advertising and data-broker ecosystem.
The IoT market is reaching a stage of maturity where industrial organizations should anticipate that a similar pattern will affect IIoT data. This calls for a conscious strategy to capitalize on new opportunities. Industrial firms have to innovate in different areas, treating data as an asset class and identifying new value propositions. This leads to a set of organizational implications and choices for engaging with other market participants.
Organizational planning for industrial IoT data
A five-point checklist that industrial organizations and their supply chain partners should run through is as follows:
- Develop IIoT data capabilities inside the organization. Begin by cataloging IIoT data assets. Then surround this resource with three capabilities. One of these takes the form of a data science team. Another is to install technology foundations to manage data securely and reliably. The third is a set of innovation capabilities spanning business development, intellectual property and regulatory functions.
- Formalize upstream supply chain opportunities. Ensure the organization controls data from suppliers as in the case of outsourced facilities management. This action ensures that changes in reporting frequency or data quality (e.g., granularity) do not incur time or cost penalties.
- Explore downstream supply chain opportunities. Explore downstream supply chain constraints and opportunities. In many cases, industrial firms will learn about data reporting conditions that channel partners may have preemptively imposed.
- Join other IIoT data ecosystems. Some industries will spawn value through external IIoT data ecosystems. An example of this is in the health and wellness sectors, be they human- or machine-related. The value stems from aggregating data for a given class of machines. One example is the provisioning of failure mode insights in the case of traditional assets, such as standby power generators or hydraulic pumps, for example.
- Orchestrate a new IIoT data ecosystem. Ambitious firms can create and orchestrate new IIoT data ecosystems. Open data portals, as in the case of London Datastore, and data exchanges are examples of pioneering efforts to explore new market opportunities by sharing data beyond an organization’s boundary. It raises issues of data certification and licensing so that downstream users can build service on reliable sources. There is also a need for oversight functions to deal with substandard data providers and to prevent rogue behavior.
Anticipate market evolution to succeed
The latent value in IoT data will expose new commercial opportunities beyond the application benefits from improved industrial operations. This change in perception surrounding IIoT data development will force industrial organizations into new business models. Firms will discover that data from individual assets creates value for several different customer segments.
To capitalize on the new opportunities, industrial firms will have to master new skills. These include the abilities to package and distribute data via horizontal trading relationships. This is a very different picture from today’s vertically aligned structures.
Without doubt, there will be a certain amount of industry disruption. Organizations that supply data-intensive industrial products and services look well-positioned to take advantage of data innovation. 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 behind and becoming detached from IIoT-enabled value chains.
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