Industrial IoT and Analytics Maturity Model

Author photo: Michael Guilfoyle
By Michael Guilfoyle
ARC Report Abstract

Industrial IoT and Analytics Competency Is Key to Transformation Success

The Industrial Internet of Things and advanced analytics are cornerstones for industrial digital transformation; it cannot be achieved without them. Yet, many companies struggle, unable to identify manageable, cost-effective ways to start and grow their digital transformation efforts.

A successful digital transformation program steps an organization through a maturity process that takes it from current state to a desired future state in an organized way. For industrial organizations, this often involves moving from a local, siloed procedural operating model to a more comprehensive model that is integrated to the enterprise, responsive to customers and market changes, and optimized in the context of overall business performance.

Too often, transformation initiatives devolve into technology projects with a pure focus on ROI. Striving to get high-risk decisions right for things they are doing for the first time, organizations view Industrial IoT and analytics through a limiting lens: a technology or set of technologies (e.g., platform) to drive all or most aspects of change. However, technology is just one component of maturity as the organization must also evolve its business, operations, and people to support a desired state.

To successfully transform, an organization must first understand where it is in its transformation maturity relative to peers, competitors, and customer expectations. An Industrial IoT and analytics maturity model provides this baseline. Combined with a maturity assessment, the model can provide value for companies of all sizes: small, large, or somewhere in between, as the vast majority of industrial organizations need to mature digitally. 

Three Stages of Maturity

Industrial IoT and analytics

ARC Advisory Group’s Industrial IoT and analytics maturity model identifies three stages of maturity:

  • Discovering and Informing is the lowest level of maturity. Overall, there are high levels of skepticism and resistance within the organization to new technologies and a pervasive, cultural mindset that it can't be done successfully.  Small, expert groups within the organization are simply trying to understand what digital transformation entails and how Industrial IoT and analytics are involved.
  • Identifying & Transitioning is a medium level of maturity. Leadership recognizes the value of digital transformation overall as well as the criticality of Industrial IoT and analytics. However, core competencies and processes are being established unevenly within the organization based on the intersection of blended IT/OT skills and advanced technologies. Experiments are common, with results balanced between significant success and struggles with scalability and sustainability.
  • Transformed characterizes a digitally mature organization. A culture of “digital-first” underpins all efforts, from leadership to the front line, and speed of response to customer needs differentiates. Previously, specialized digital transformation skills were commonly embedded within operations, expanding the number of initiatives undertaken and people involved.


Maturity Model

To be effective and accurate, a digital transformation maturity model must consider a much broader view than just technology.  The ARC Industrial IoT and Analytics Maturity Model does so by encapsulating the three categories of industrial operations:

  • People and Culture: This describes the organization’s culture of innovation and willingness to change, including its ability to engage customers in new ways. It incorporates human capabilities, resources, and skills available for change and how worker performance is measured.
  • Analytics Data and Technology: This covers the technology, methods, and processes necessary to deploy, use, and scale analytics and Industrial IoT. It also addresses the necessary governance changes inherent in the broader, more democratized use of data.
  • Operational Infrastructure: This entails the virtual/physical asset environment within the organization across its operational footprint. It also encapsulates the lifecycle management of assets as well as the cybersecurity of the related data.

Within each category, there are progressions made to maturity. For example, for People and Culture, a maturing company becomes more customer-centric in terms of how it uses Industrial IoT and analytics. Competitive differentiation of the end goal, rather than just operational excellence. Transformed companies can react to market and customer signals with a speed and accuracy their competition cannot match.

Beginning with Discover and Inform, ARC’s Industrial IoT and Analytics Maturity Model defines three levels of increasing maturity across the categories of People and Culture, Analytics Data and Technology, and Operational Infrastructure:

A Snapshot of Progress

ARC Advisory Group and The Digital Transformation Council (DTC) recently conducted an Industrial Digital Transformation Snapshot survey intended to help respondents gain a better understanding of their relative progress toward industrial transformation and how they compare to their peer organizations within the industrial world.

Part of this survey included feedback on how mature companies are in relation to ARC’s Industrial IoT and Analytics Maturity Model. Respondents were asked to describe their progress relative to an end state of maturity in People and Culture, Analytics Data & Technology, and Operational Infrastructure.


Table of Contents

  • Industrial IoT and Analytics Competency Is Key to Transformation Success
  • Three Stages of Maturity
  • Maturity Model
  • A Snapshot of Progress
  • How Do You Compare?


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