Data Types in IOTA

IOTA standardizes data integration by defining four core data types. These data types ensure consistent search, visualization, and analysis across different source systems.

All data types, except datasets, have a dedicated search tab in the UI. Datasets are primarily searched as tags.
For more details on searching and filtering data, see Data Search.

When mapped to a dashboard, all data types are referred to as channels. Channels can be configured within each component’s Channel Settings, allowing users to control how data is displayed and processed.
For more information, see Channel Settings.

IOTA interacts with various data sources through drivers, which act as translators, ensuring that different systems can communicate seamlessly. Since the available data types depend on the source system, refer to the Driver Overview page for more details on supported data types and capabilities.

Tags

A tag is a continuous data stream that logs values over time, typically sourced from sensors, devices, or systems. Each entry consists of:

• Value (e.g., 72.5°C)
• Timestamp (e.g., 12:30 PM)
• Status (optional, e.g., "Good," "Bad Data," or "Questionable")

Example:
A tag like Tank1_Temperature records 72.5°C at 12:30 PM, allowing real-time monitoring and trend analysis.

System-Specific Terminology:

• PI System → PI Point
• Seeq → Signal

Assets

An asset is a logical representation of a physical or virtual entity (e.g., a pump, tank, or production line) that organizes related data for analysis, visualization, and contextualization. By structuring data around assets, IOTA enables consistent, reusable representations that simplify data exploration and visualization.

Asset Attributes

Assets consist of attributes, which define their properties and determine how data is visualized:

• Time-series data – Continuous sensor readings, similar to tags
• Static metadata – Fixed values such as equipment specifications, setpoints, or classifications
• Derived (calculated) values – Aggregated efficiency metrics, computed performance indicators

Dynamic Asset Swapping & Attribute Mapping

IOTA enables dynamic asset swapping, allowing a single visualization or dashboard to display data from different assets without reconfiguration. This is made possible by attribute mapping, which ensures that the correct asset attributes are displayed dynamically based on the selected asset.

For more details, see Attribute Mapping.

Generic Mapping: Flexible Attribute Matching

Beyond standard attribute mapping, IOTA offers Generic Mapping—a flexible mechanism for linking components to asset attributes by name, rather than by fixed template.

In the asset mapping dialog, a custom attribute name can be specified. When a different asset is selected, the system automatically binds the component to the attribute with the same name, regardless of the underlying asset template.

This approach is especially useful in scenarios where assets lack consistent templates, such as with Seeq integrations. By using Generic Mapping, a single dashboard configuration can be reused across diverse assets, eliminating the need to build separate dashboards for each asset type.

Asset Groups for Scalable Visualization

Assets can also be used to create Asset Groups, which bundle multiple components into reusable templates that visualize different data points. This templating approach makes it easy to replicate complex dashboards without manually configuring each component.

Learn more about Asset Groups here.

Structuring Assets in IOTA

Some data sources already provide structured asset hierarchies, while others do not. When a source system lacks a built-in asset model, some IOTA drivers—such as Generic SQL—allow assets to be structured hierarchically, ensuring that data remains well-organized and accessible.

Example:

A Pump asset may include:

• Flow Rate → time-series data from a sensor
• Maximum Pressure → static specification
• Efficiency Ratio → derived from multiple inputs

System-Specific Terminology:

• PI System → PI Asset Framework (AF)
• Seeq → Asset Groups

Timeframes

A timeframe defines a start and end time to track specific events, process phases, or conditions. It provides context for analyzing operational data and may include additional metadata.

Example:
A Production Batch timeframe logs:

• Start Time: 12:00 PM
• End Time: 2:00 PM
• Batch Quality Score: 95%

System-Specific Terminology:

• PI System → Event Frames
• Seeq → Capsules
• Batch Processing → Batches

Datasets

A dataset is a structured or semi-structured collection of related data, commonly used for queries, reporting, and analytics. Unlike tags, which capture continuous time-series data, datasets store broader information such as event logs, transactional records, or summarized metrics.

Example:
A maintenance dataset may include:

• Timestamp
• Machine ID
• Failure Type
• Repair Cost

Search in UI: Datasets are retrieved via tag-based searches, as they do not have a separate tab.