Data Readiness for Industrial AI.
A practical guide to assessing your OT data before you build or deploy AI models.
Is Your OT Data Ready for AI?
Before you train a model, you need to know whether the underlying data is complete, consistent and reliable enough for the job.
This guide covers the most common failure modes in industrial time-series data – from missing signals and flatlines to bad values, noise and issues introduced during historian-to-cloud migrations. It provides a practical framework for assessing data readiness and deciding what to address before your data reaches an AI workflow.
Built for: Engineering, data, analytics and digital teams working with industrial OT data.
Chapter Summaries
Understand why industrial AI and advanced analytics depend on data that has been validated for its intended use. This chapter explores the structural challenges created by data ownership boundaries, infrequent checking and the movement of time-series data from historians into cloud and analytics environments.
Explore the failure modes most commonly found in industrial time-series data, from flatlines, drift and out-of-range values to stale tags, mapping errors, compression and undocumented configuration changes. Each failure mode is examined in terms of what it looks like, why it can be missed and how it can affect a model.
Understand what can happen to time-series data as it moves from the historian into a lakehouse, cloud warehouse or analytics platform. This chapter focuses on transfer gaps, timestamp drift, precision loss and other changes that can disconnect data from its original values or context.
Apply a structured six-dimension assessment to the data in scope for your model. The framework covers completeness, continuity, validity, consistency, provenance and stability, with practical checks and criteria for determining whether each dimension is acceptable.
Turn data-quality findings into defined actions rather than simply documenting problems. The chapter sets out three responses for each finding: fix the problem at source, handle it downstream, or accept and document the limitation.
Understand where manual data-quality checks stop being practical at plant scale. The chapter looks at the challenge of continuously assessing thousands of tags, detecting gradual changes and identifying problems that may not be visible through periodic inspection.