To make industrial data actionable, evolve your data historian with an AIoT strategy

Step one involves leveraging next-generation data historians to democratize data access, ensuring that everyone within a plant and across the enterprise – regardless of skill, training, tenure, or expertise – has equal access and ability to tap into data that sits in any source, across the plant, fr...

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Bibliographic Details
Published inCIO
Main Author Scudder, Bill
Format Trade Publication Article
LanguageEnglish
Published Framingham Foundry 29.11.2021
Subjects
Algorithms
Artificial intelligence
Data management
Decision making
Historians
Production scheduling
Profitability
Subject specialists
Online AccessGet full text
ISSN0894-9301

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Summary:Step one involves leveraging next-generation data historians to democratize data access, ensuring that everyone within a plant and across the enterprise – regardless of skill, training, tenure, or expertise – has equal access and ability to tap into data that sits in any source, across the plant, from the edge to the cloud. [...]a generic AI model trained on plant data may not be as nimble as you want and expect an AI model to be – for instance, being able to respond to real-time market changes and adjusting production schedules accordingly. To support and achieve their profitability, production, and sustainability goals, industrial organizations must evolve their current data historians into next-generation, industrial-grade data management solutions powered by an AIoT strategy, which provides the anchor technology for deploying Industrial AI applications across the enterprise.
ISSN:0894-9301