Insight Providers for IT and OT Data Analytics
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Solution Overview
Problem
Data analytics systems for predictive maintenance are resource-intensive and require high technical skills, limiting their broader adoption due to the need for complex processing and domain expertise.
Innovation Solution
Implementing a computer-implemented data analytics system with insight providers that include domain-specific models and configuration components, processing operational technology (OT) and information technology (IT) data to provide enriched data and graphical representations, enabling users to interact with the system for asset monitoring and maintenance tasks through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data analytics systems process combined IT and OT data using domain-specific models, then insight quality and decision accuracy improve, but system complexity and resource requirements increase
Solution Approach 1:
The system segments data analytics into multiple independent insight providers, each specializing in a specific domain (e.g., equipment performance, energy consumption, maintenance prediction). Each insight provider processes specific types of data independently and returns focused insights, reducing the complexity of any single processing unit while maintaining comprehensive analysis capabilities through the collection of specialized providers.
2Measurement precision
If data analytics systems require high technical skills for review and action, then analysis accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The system introduces domain-specific insight providers as intermediaries between raw data and end-users. These insight providers encapsulate complex analytical logic and domain expertise within modular components, translating complex data processing into simple, actionable insights through standardized interfaces. Users interact with pre-packaged insights rather than raw analytical processes, eliminating the need for deep technical expertise while preserving analytical accuracy.
3Measurement precision
If data analytics processing is resource intensive, then analysis depth improves, but productivity of other system functions deteriorates
Solution Approach 1:
The system divides comprehensive data analytics into multiple independent insight providers that can execute in parallel. Each insight provider performs focused analysis on specific data types or business questions, allowing resource-intensive processing to be distributed across multiple lightweight processes rather than consuming all resources in a single monolithic analysis. This enables concurrent execution of multiple analytical tasks, improving overall system throughput while maintaining analysis depth.
Data Source
AI summary
Methods, systems, and computer-readable storage media for providing an insight provider including a logic component and a configuration component, the logic component including a domain-specific model, the configuration component including one or more parameter values for processing data using the domain-specific model, receiving a set of assets including data indicative of one or more assets, retrieving asset data associated with at least one asset of the first set of assets, the asset data including OT data and IT data, the OT data being provided from one or more networked devices, the IT data being provided from one or more enterprise systems, and processing the OT data and the IT data using the domain-specific model of the logic component to provide a result set, the result set including one or more of a second set of assets and enriched data.