Data Governance for Industrial AI

The Core Idea

Reliable Artificial Intelligence relies on strict data governance practices that maintain context, ensure integrity, and protect security throughout the entire process – from sensors to dashboards.

A key component of this is data cataloging; meticulously documenting what data exists and how it’s being utilized within the system.

Data Sources

Industrial AI systems draw data from a variety of sources, including Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems.

Other crucial sources encompass historians, Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) platforms, Computerized Maintenance Management Systems (CMMS), laboratory Information Management Systems (LIMS), and various vision systems.

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Data Storage & Ingestion

Data ingestion involves collecting streaming data from sensors, enforcing strict schemas to maintain consistency, and synchronizing timestamps accurately.

Furthermore, robust handling of late-arriving data is essential for real-time industrial applications. Data is typically stored in time-series databases for sensor signals, object stores for images and documents, and relational databases for master data.

Frequently asked questions

What role do data owners and stewards play in a robust data governance strategy?

Assigning specific owners and stewards to each dataset ensures accountability and facilitates effective management. These individuals are responsible for defining clear responsibilities and establishing key performance indicators (KPIs) related to data quality and usage.

How can change boards be implemented to manage modifications to schemas and data contracts?

Change boards dedicated to schemas and data contracts provide a centralized location for managing updates. These boards incorporate approval workflows to ensure that all changes are thoroughly reviewed and authorized before implementation.

What processes should be in place for handling data incidents requiring immediate attention?

Establishing clear on-call procedures for data incidents is crucial for rapid response. These protocols outline escalation paths to ensure that the right personnel are notified and involved promptly.

Case Study (Illustrative)?

Case Study (Illustrative)

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