Industrial Platforms Complicate IIoT Analytics Decisions

“All you really need to know for the moment is that the universe is a lot more complicated than you might think, even if you start from a position of thinking it's pretty damn complicated in the first place.”

Douglas Adams, Hitchhiker’s Guide to the Galaxy

Because it just wasn't confusing enough...

I think Mr. Adams must have had IIoT and analytics in mind when he penned that one. The parallels are uncanny.

The challenge for industrial businesses trying leverage IIoT are the many gray areas that present hard-to-unravel barriers, or at least delays, to adoption. Connected ecosystems, data integration, cyber security, analytics and change management are just some of the issues with which businesses struggle.

In a digitally connected universe, intelligent use of data drives business decision making. Analytics is not simply an enabling technology solution; it delivers data intelligence as a core competency. Developing that competency is proving to be challenging for many companies. In large part, the challenge stems from confusion as to how different types of analytics work and, in many instances, how they are marketed. I’ve written about that confusion before, and here are a few blogs if you’d like a refresher:

• Advanced analytics chutes and ladders
• Analytics currently defy ranking
• Blurred lines between predictive and prescriptive analytics

However, as alluded to in the quote above, a confusing universe is getting more complicated. Industrial platforms with IIoT services are a signification reason why. They add another very complex layer to the often difficult decision making about analytics strategies. They do so because analytics are designed as a cornerstone component of what the platforms are intended to deliver. However, they are also built to do much more.

More options are in the mix

Typically, analytics solutions fell into one of two main categories: niche applications and analytics platforms. Though they can sometimes be difficult to distinguish as they increasingly blend, here are the basic characteristics of each approach:

Niche application: This approach optimizes the analytics to feed selected models for supporting specific use cases. Solution flexibility is typically traded off to improve purpose-built usability and ease of getting started. Many of these solution providers are also adding more platform-like capabilities to broaden the value they provide.

Analytics platform: This solution provides access to broader analytics capabilities and supporting tools. Purpose-built usability is typically traded off to improve flexibility of use. Many of these solution providers are also adding use case-specific applications to improve the ease of getting started with analytics.

Since many large companies are either providing or building industrial platforms with IIoT services, they now need to be considered as part of the analytics solution mix. From an analytics perspective, here are the basic characteristics:

Industrial platforms with IoT services: The capabilities these industrial platforms provide range from provider to provider. These industrial platforms provide analytics and related tools in the form of microservices, packaged business process services, app development and lifecycle management, visualization, mobility, etc. In many instances, these industrial platforms are the foundations upon which the niche application and analytics platforms solutions are built and delivered.

(As an aside, ARC’s IIoT team, which includes yours truly, continues to extensively research the build out of industrial platforms with IoT services. If you need more information on how these platforms work, just drop me a line.)

These three categories are just the starting point for sorting through available analytics solutions. Within them are multiple subcategories of solutions based on differences both large and small. These differences cover capabilities such as data management, sandbox environments, search and query, deployment and support services, market focus and in-house subject matter experts, visualization, accompanying knowledge bases, and prescriptive components, just to name a few.

Providers of industrial platforms are still evolving strategies

Providers of these industrial platforms are still creating and/or executing their solution vision and market strategies. As an example, some providers are acquiring companies with the idea of accelerating existing capabilities. Others industrial platforms are acquiring companies to shore up gaps or widen their potential user base. As well, one can also look at the large gaps in the maturity of industrial platforms app marketplaces as yet another example.

Companies engaged in digital transformation are still trying to determine how to best leverage both analytics and industrial platforms. Often, these initiatives – industrial platform use and analytics – are separate projects. That’s not necessarily wrong. Typically, companies are further ahead in using analytics than they are at broadly adopting industrial platform capabilties. Also, analytics use cases can often be very contained.

However, companies would be wise to at least make decisions within each of these initiatives with the other in mind. This will enable them to deal with the fluidity of the markets for analytics and industrial platforms. Tradeoffs of one approach versus another can then be better considered and a more cohesive digital transformation strategy will likely result.