Understanding Data Management
This module becomes useful after the basic project path is already working. If your first monitoring page still does not run, fix that first and come back later.
Start Here
Use this page when:
- the project can already display live tag values
- you need row-based records instead of only live values
- you want dashboards, reports, shift summaries, or event-based archiving
Before You Continue
Make sure:
- you already understand the difference between tags and views
- you know which event should create a record
- you can explain why a simple historian trend is not enough for this task
If you want copy-ready historian groups, table fields, and aggregation examples, use Configuration Sample Data.
Choose Between Historian and Data Management
Use the right storage model for the right job:
| Need | Best choice | Why |
|---|---|---|
| Time-series trends for one or more tags | Historian | Simple tag history with time on the x-axis |
| One row per event, batch, shift, or business action | Data Management | Custom fields, triggers, and table-style storage |
| KPI dashboards built from custom fields | Data Management | Supports dimensions, metrics, and table-driven charts |
| Fast first trend for analog values | Historian | Usually less setup than a custom table |
What You Should Be Able to Do After This Page
After reading this page, you should be able to:
- explain the role of Tag Archiving, Data Aggregation, Base Data, and Business Data tables
- decide whether your requirement belongs in Historian or Data Management
- identify the next page to create a first practical table
What is Data Management?
Data Management is the core module for archiving and managing runtime data generated during project execution. It provides a centralized system to collect, process, store, and analyze industrial data, ensuring data integrity and availability for production analysis, quality tracking, and compliance reporting.
When to Use Data Management?
Data Management is essential for:
- Production Tracking: Record output, defects, cycle times, and batch information
- Equipment Monitoring: Track equipment status, runtime, downtime, and fault history
- Energy Analysis: Monitor energy consumption by equipment, line, or shift
- Material Traceability: Record material usage, lot numbers, and process parameters
- Quality Analysis: Collect inspection data, defect rates, and process deviations
- Compliance Reporting: Archive data for regulatory requirements and audits
- Performance Metrics: Calculate OEE, yield, throughput, and other KPIs
Data Flow Architecture
How It Works:
- Tag Values Change: Tags continuously update with real-time data from devices
- Trigger Evaluation: The system checks if trigger conditions are met
- Data Recording: When triggered, data is recorded to a Tag Archiving table
- Aggregation: Data Aggregation tables summarize raw data (optional)
- Visualization: Archived data is displayed on views and reports
Data Table Types
Tag Archiving Tables
Purpose: Collect data from tags based on trigger conditions.
Use Cases:
- Record temperature every minute
- Log production data when part completes
- Capture fault snapshots when alarms trigger
Data Flow: Tags → Trigger → Archive
Data Aggregation Tables
Purpose: Summarize data from another table (the "source table").
Use Cases:
- Calculate hourly production from per-second data
- Summarize daily energy consumption
- Group defects by product code
Data Flow: Source Table → Grouping/Aggregation → Summary Table
Base Data Tables
Purpose: Store custom business data not directly linked to tags.
Use Cases:
- Work order management
- Personnel skills database
- Material inventory tracking
Data Flow: Manual entry or Script automation → Base Data Table
Business Data Tables
Purpose: System-generated tables for internal use.
Examples:
- Alarm Management - Real-time Table
- Alarm Management - Historical Table
- Audit logs from plugins
Data Flow: System modules → Business Data Table
Key Concepts
| Concept | Description | Example |
|---|---|---|
| Record | A single row in a data table | One production cycle |
| Field | A single column in a data table | Temperature, Product Code |
| Trigger Condition | Event that causes data recording | Part completes, every 5 minutes |
| Calculation Formula | Expression to process data | [Good] / [Total] * 100 |
| Aggregation | Grouping and summarizing data | Sum production by hour |
| Storage Policy | Data retention rules | Keep data for 6 months |
Data Management Interface
📷 [UG-DATA-UNDER-01] Data Management interface showing three main areas
Interface Areas
| Area | Description |
|---|---|
| Table List (Left) | All data tables in the project. Organize with folders. |
| Configuration Panel (Center) | Configure fields, triggers, and storage for selected table. |
| Data Preview (Right) | View the most recent 500 archived records. |
Common Operations
| Operation | How to Access | Purpose |
|---|---|---|
| Create Table | Click Add in toolbar | Create new data table |
| Enable/Disable | Right-click table → Enable/Disable | Control data archiving |
| Export Data | Right-click table → Export Data | Backup to CSV |
| Clear Data | Right-click table → Clear Data | Remove test data |
| Delete Table | Right-click table → Delete | Remove table and data |
⚠️ Caution: Deleting a table also deletes all archived data. Export data before deletion.
Next Steps
- Creating Data Tables - Start with one simple Tag Archiving table before using aggregation or base data
- Configuring Historian - Use this instead if you only need time-series trends for tags
- Configuring Triggers and Formulas - Set up data collection rules after the table structure is clear
- Data Management Examples - Return here later for larger production scenarios