Data Modeling Guidebook

Abstract

Authors: John Harrington, Tony Paine,  Aron Semle, and Torey Penrod-Cambra

The digital transformation is providing industrial organizations with an unprecedented amount of visibility and predictive insights into their operations. Every day, factories and other industrial environments are adopting new, smart technologies that are delivering vast amounts of data they can use to optimize production, predict machine failures, and improve quality. However, connecting these machines to data storage platforms or enterprise systems (in the Cloud or on-premises) isn’t always seamless. It requires a standardized approach to defining and categorizing data, so everyone across the organization has a single source of truth and can make faster, more informed decisions.

This guidebook provides a comprehensive overview of data modeling and why it’s essential for every industrial environment—no matter where the organization stands along its digital journey. Readers will gain a better understanding of how data modeling works, what it looks like, how it works with existing standards (such as ISA-95), and tips on how to establish a data-modeling strategy.