The PI System
Time series intelligence software
Manufacturing intelligence software
Time series databases
Stream analytics software
Data warehouse solutions
Meter data management systems
Database software
Data integration tools
Cloud data integration software
Utilities software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is The PI System
The PI System is an industrial data infrastructure platform used to collect, store, and contextualize high-frequency time-series data from operational technology (OT) sources such as sensors, PLCs, SCADA, and historians. It supports operations, engineering, reliability, and data teams that need near-real-time visibility, analysis, and data sharing across plants and enterprise systems. The product centers on a time-series data archive with an asset-based information model and a set of connectors, APIs, and visualization/analytics components for operational reporting and integration.
Industrial-grade time-series historian
The PI System is designed for continuous ingestion and long-term retention of high-volume operational time-series data. It supports common industrial patterns such as tag-based collection, compression, and event-driven updates from OT systems. This makes it well-suited for manufacturing and utilities environments where data arrives continuously and must remain queryable over long periods.
Strong asset context modeling
The PI System includes an asset framework that maps raw tags to equipment, hierarchies, and attributes, enabling consistent context across sites. This improves reuse of calculations, templates, and naming standards compared with storing time series as isolated signals. The approach supports cross-asset analysis and operational reporting without requiring every consumer to understand tag-level details.
Broad integration and APIs
The platform provides connectors and interfaces for common OT data sources and exposes data through APIs and integration components for enterprise consumption. It is commonly used as a hub to move operational data into analytics, reporting, and data platform environments. This reduces the need for custom point-to-point integrations when multiple downstream systems need the same operational data.
Complex deployment and administration
Implementations often require specialized skills in OT connectivity, tag management, and information modeling. Designing a scalable asset model and governance for naming, templates, and calculations can be time-consuming. Ongoing administration can be heavier than newer cloud-native time-series and analytics services.
Licensing and cost considerations
Total cost can increase with large tag counts, high availability requirements, and enterprise-wide rollouts. Organizations may need additional components for visualization, advanced analytics, or cloud connectivity depending on use case. Budgeting can be less straightforward than consumption-based services for some buyers.
Not a full data warehouse
While it stores and serves time-series operational data effectively, it is not designed to replace a general-purpose analytical warehouse for broad subject areas and complex SQL workloads. Many organizations still replicate or curate PI data into a separate analytics platform for enterprise BI, data science, and cross-domain joins. This adds data engineering work when PI data must be combined with ERP, CMMS, or customer datasets.
Seller details
AVEVA Group plc
Cambridge, United Kingdom
1980
Public
https://www.aveva.com/
https://x.com/AVEVA_Global
https://www.linkedin.com/company/aveva
