Manufacturers investing in AI, advanced analytics, and autonomous systems face a recurring problem: the data underneath these initiatives is not ready to support them. Amazon Web Services and HighByte published a 25-page white paper, “A Maturity Model for Industrial Data Management,” that gives manufacturing organizations a structured way to assess where they stand and what needs to change before AI can deliver results.
What Does the Industrial Data Management Maturity Model Cover?
The white paper defines three progressive stages of data maturity for manufacturing organizations: Foundation, Intelligence, and Transformation. Each stage maps to specific use cases for both discrete and process manufacturing, with technology stack requirements, best practices, and role-based assessment questions for production managers, maintenance teams, quality personnel, IT/OT leaders, and executives.
The paper also includes a generative AI readiness assessment at each stage, helping organizations determine whether their current data capabilities can support AI adoption or whether foundational gaps need to be addressed first.
How Does the Framework Measure Data Readiness?
The model uses a four-step assessment process. Organizations start at Stage 1 to confirm whether foundational data collection and governance are in place. If those answers are satisfactory, they move to Stage 2 questions on analytics and optimization capabilities, then Stage 3 on autonomous operations and AI integration. The classification result, low, medium, or high maturity, tells organizations where to focus investment next.
Assessment questions are organized by role: production managers answer questions about how they track metrics and identify bottlenecks, maintenance teams evaluate their approach to equipment monitoring and failure prediction, quality personnel assess how they trace defects back to process parameters, and IT/OT leaders examine their data architecture and integration strategy. The paper includes over 50 questions across all three stages.
Who Should Download This White Paper?
The framework was built for manufacturing leaders evaluating AI investments, plant managers responsible for operational data, IT/OT teams designing data architecture, and anyone tasked with scaling digital initiatives across multiple facilities. The role-based structure allows each function to evaluate its own readiness independently, then compare results across the organization to identify the gaps that matter before committing resources to AI.
The white paper is authored by Ashtad Engineer, Worldwide Head of Manufacturing Solutions at AWS, and John Harrington, Chief Product Officer at HighByte. It covers the AWS Industrial Data Fabric architecture and how HighByte Intelligence Hub supports industrial data integration from edge to cloud.
Sponsored by HighByte
