In our increasingly connected world, the challenge for traditional industries is no longer collecting data, but putting collected data to use. While sensors are widely deployed across sites and equipment generates more data than ever before, that data remains scattered across different platforms, protocols, and standards. These fragmented data sources operate in isolation, creating silos that hinder meaningful analysis.
Without a solid data infrastructure, To truly unlock the value of AI in business, the first step isn’t model training or algorithm design. It’s integrating, cleaning, transforming, and restructuring data from diverse sources—turning it into high-quality, well-structured assets enriched with consistent business context.
Integrate Diverse Data Sources and Eliminate System Silos
To enable efficient data integration, TDengine supports a wide range of mainstream industrial protocols and data sources, including but not limited to:
- Modern industrial protocols such as MQTT and OPC (both UA and DA)
- Data collection agents like Telegraf and collectd
- Traditional real-time databases such as PI System and AVEVA Historian
- Relational databases including MySQL, Oracle, SQL Server, and PostgreSQL
- Apache Kafka
- CSV files
With flexible connectors and a unified data ingestion framework, TDengine allows enterprises to collect and centralize distributed, heterogeneous data—without writing a single line of code.
Unlike traditional data aggregation tools, TDengine is built with data governance in mind right from the ingestion layer. It comes with a complete built-in ETL engine that supports field mapping, unit conversion, expression-based transformation, and data type standardization—ensuring that data isn’t just aggregated, but also aligned and meaningful.
Build a Structured View with Hierarchical Data Modeling
TDengine organizes industrial data in a hierarchical tree structure, clearly representing the relationships from enterprise and factory down to production lines, equipment, and sensors. Each node represents a physical device or logical entity and can host data, define attributes, link to visualization dashboards, apply analytical logic, and manage events—becoming a complete carrier of business context.
The system supports multiple tree structures built from different business perspectives. You can organize data by organizational hierarchy (e.g., Group → Factory → Equipment) or by equipment type (e.g., Turbine → Inverter → Sensor), enabling consistent presentation and multi-dimensional analysis of the same data. This structured approach allows enterprises to build a clear, manageable data asset directory, aligning scattered data with business meaning and laying the groundwork for standardization and contextualization.
Align Data Structures and Definitions to Achieve Standardization
In real-world scenarios, even data of the same type often varies across systems—differing in naming conventions, units, or structural formats. For example, one system might record temperature as “WD,” another as “temp”; some devices report in Fahrenheit, others in Celsius. For business analysis and AI algorithms, this kind of inconsistency renders the data unusable without preprocessing.
TDengine IDMP allows users to define standard field names, target units, and conversion formulas for each data point—automatically transforming and standardizing data at the source. With its data reference mechanism, TDengine can also map data from different databases and schemas into a unified set of business attributes, enabling consistent modeling across heterogeneous sources with no manual intervention.
Add Business Semantics to Enable Data Contextualization
Building on a clear hierarchical structure, TDengine IDMP allows each element and attribute to be enriched with business semantics, creating a data system rooted in context.
Descriptions can be added to define business meaning, and flexible tagging supports fast classification and filtering. Static properties such as device model, installation location, and specification parameters enhance asset identification. At the attribute level, physical units, thresholds, and target values can be defined to establish reference baselines for analysis and alerting. The system also supports additional metadata—such as whether an attribute is constant, visible, or included in calculations—to further expand data expressiveness and application value.
This mechanism transforms raw values into contextualized, business-relevant information—laying a solid foundation for intelligent analytics and automated decision-making.
AI-Ready, From the Ground Up
Built on a unified data structure enriched with context, TDengine IDMP delivers a fully AI-native foundation—capable of automatically detecting scenarios, recommending analyses, generating dashboards, and defining alert rules. With this approach, AI no longer depends on specialized teams for configuration or requires users to “ask questions first, then look for data.”
What makes proactive insight possible is a standardized, structured, and contextualized data system. TDengine doesn’t just offer a tool or model—it provides the entire data foundation needed to make AI truly operational. On top of this foundation, everything from traditional reporting systems and BI tools to external AI services and foundation models can run efficiently and respond in real time—enabling your data to speak for itself.
