Data mapping defines how raw machine signals are interpreted and used within MachineMetrics. When a machine is connected, it transmits data items consisting of keys and values from the PLC or control system. These values do not inherently describe what they represent or how they should be used by the platform.
Data mapping assigns meaning, structure, and behavior to these data items so they can be used consistently across diagnostics, dashboards, analytics, and automation features. Accurate data mapping is foundational to reliable reporting and correct platform behavior.
This feature is intended for customers responsible for managing machine connectivity, data quality, and operational reporting across one or more assets.
Feature Availability
| Category | Details |
|---|---|
| MachineMetrics Module | Core Platform |
| Real Time Requirements | Machine connectivity required |
| Access Level | Executive, Manager, IT Admin |
How Data Mapping Works
Data mapping functions as a translation layer between data collection and data consumption within the MachineMetrics platform.
After a machine is integrated and communication is established, the adapter script defines which data items are collected from the machine and how frequently they are sampled. Data mapping then assigns purpose to those collected items by defining what each data item represents and how the platform should interpret changes in its value.
Once mapped, data becomes available throughout MachineMetrics. Diagnostics rely on mappings to group and organize signals. Dashboards and reports rely on mappings to calculate metrics accurately. Platform features such as execution state tracking and part counting depend on mapped data behaving consistently.
Understanding Data Items
Each data item represents a single signal or value coming from a machine. While the underlying identifier is defined by the machine or adapter script, data mapping determines how that identifier is interpreted by MachineMetrics.
Key Name
The key name is the unique identifier assigned by the machine or adapter script. Because the key name is directly tied to data collection, it cannot be edited through data mapping without corresponding changes to the adapter script.
Component and Component Type
The component describes the physical or logical part of the machine associated with the data item, such as a spindle, axis, controller, or sensor. Component type determines how that component is categorized within the platform and how related data items are grouped, particularly within Diagnostics.
Type and Subtype
The type defines the general category of the data, such as status, count, or measurement. The subtype provides additional context that determines how the data item behaves within the platform, including how values are interpreted and how they influence calculations and visualizations.
Display Name
This field that allows for users to designate a custom name for items that may have the same Component and Component Type so that they can be more easily differentiated in reporting.
Why Data Mapping Matters
When data mapping is configured correctly, platform behavior is more consistent and reporting is more reliable. Part count signals increment as expected, execution states more accurately reflect actual machine behavior, diagnostics are easier to interpret, and downstream reporting is accurate.
When mappings are incomplete or inconsistent, outcomes may include misleading metrics, incorrect state classification, or reduced visibility into machine behavior.
Managing Data Mappings
Data mappings may be added, edited, removed, refreshed, or reverted over time as machine configurations change or as additional data becomes available. Changes to mappings affect how incoming data is interpreted by the platform and may have immediate impact on diagnostics and reporting.
Mappings are managed using the Data Mapping Editor, which provides tools for viewing live values, editing mappings, managing versions, and reverting changes. Detailed instructions for using the editor are covered in the Using the Data Mapping Editor article.
Tips and Best Practices
Confirm that mapped data behaves as expected using live values before relying on dashboards or reports.
Avoid making mapping changes during active production unless the impact is clearly understood.
Use version history to recover from unintended changes rather than recreating mappings manually.
FAQs
What happens if a data item is not mapped?
Unmapped data is still collected, but it may not be usable by platform features that depend on mapped behavior.
Do mapping changes affect historical data?
Mapping changes primarily affect how incoming data is interpreted. Historical data behavior may vary depending on the feature and reporting context.
Can data mappings be reverted?
Yes. Previous mapping versions can be restored using the Data Mapping Editor.
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