Inconsistent file formats
Different LabVIEW applications may output CSV, TDMS, text files, spreadsheets or custom formats.
LabVIEW
LabVIEW Test Data Management
Turn LabVIEW test outputs into structured data that production, quality and engineering teams can analyse and reuse.
What is LabVIEW test data management?
LabVIEW test data management is the process of structuring and connecting data generated by LabVIEW applications. It helps teams turn LabVIEW data logging, TDMS files, CSV outputs and custom results into reusable evidence for analytics, traceability, failure analysis and quality workflows.
Why LabVIEW test data becomes difficult to manage
LabVIEW is often used to build custom test applications that match specific products, instruments, fixtures and production processes.
That flexibility is valuable, but over time it can create fragmented test data. Different applications may log data differently, use different file structures or capture different metadata.
The result is a familiar problem: the team has the data, but it is difficult to compare, analyse and use across production.
Common LabVIEW data challenges
Local storage
Results may be stored on station PCs or shared drives rather than in a central structure.
Missing metadata
Files may include measurements but not enough context about product, serial number, station, fixture, operator or software version.
Legacy applications
Older LabVIEW applications may still run important production tests but lack modern data workflows.
Manual reporting
Teams may rely on manual exports, screenshots or spreadsheets to share results with quality or customers.
What LabVIEW test data should support
A good LabVIEW test data workflow should help teams:
- Search historical results
- Compare failures across products and stations
- Analyse first pass yield
- Track retests and rework
- Link results to serial numbers and batches
- Preserve test limits
- Support customer and audit evidence
- Identify recurring failures
- Connect engineering and quality workflows
How to improve LabVIEW test data management
Define a common data model
Agree which fields every test output should include, even across different LabVIEW applications.
Capture product context
Include product, variant, serial number, batch, station, fixture and timestamp.
Preserve measurement limits
Store the limits used at the time of test, not just the measured values.
Track test application versions
Record which version of the LabVIEW application produced the result.
Centralise results
Move away from isolated files and toward a structure that can support analysis and traceability.
How Arc helps
Arc helps teams using LabVIEW structure test data so it can be reused beyond the original application.
This helps production, quality and engineering teams analyse results, investigate failures and build better traceability from existing test workflows.
How LabVIEW test data management differs from log files
LabVIEW log files preserve useful test outputs, but they are often hard to compare across products, stations and time periods. LabVIEW test data management adds the shared structure and context needed for analysis and traceability.
FAQ
What is LabVIEW test data management?
It is the process of structuring and managing data generated by LabVIEW test applications so it can be analysed and reused.
Why does LabVIEW test data become fragmented?
LabVIEW applications are often built for specific products or stations, so different systems may output data in different formats.
Can legacy LabVIEW applications support better analytics?
Yes, but the data usually needs to be extracted, standardised and connected to product, station and quality context.
What formats can LabVIEW test systems produce?
LabVIEW systems may produce formats such as CSV, TDMS, text files, spreadsheets, databases or custom outputs.
What context should LabVIEW test data include?
It should include product, serial number, station, fixture, timestamp, software version, measured values, limits and pass/fail results.
Request Access
Bring a LabVIEW test output and we’ll map how it could be structured for analytics, traceability and quality workflows.
Request Access