Results are scattered
Prototype and engineering test results can be stored in different folders, databases, files, notebooks or rig-specific systems. This makes it difficult to compare anomalies across runs, products and time periods.
Test Data Management
Engineering Test Data Analysis
Arc helps hardware teams turn fragmented prototype, engineering, field and early production test data into faster analysis, anomaly investigations and reports.
What is test data management?
Engineering test data analysis is the process of collecting, structuring, analysing and reporting on prototype, validation, field and early production test results. For hardware start-ups and scale-ups, it connects test runs, measured values, configurations, anomalies and engineering notes so teams can make decisions faster.
What is test data management?
Test data management is the process of collecting, structuring, analysing and reusing the results produced by engineering, validation and production test systems.
For hardware teams, test data often sits across prototype rigs, LabVIEW applications, TestStand sequences, Python scripts, CSV files, SQL exports, spreadsheets, local machines and manual reports. Each system may capture part of the picture, but teams still struggle to answer basic operational questions quickly:
- Which prototype or engineering test runs changed most?
- Which measurements, configurations or operating conditions look abnormal?
- Which limits changed, when and why?
- Which runs need to be compared before the next design decision?
- Which failures are linked to a batch, supplier, fixture or software version?
- What evidence do we need for design reviews, customer updates or internal engineering reports?
Arc helps teams turn fragmented test outputs into a usable engineering evidence layer for analysis, anomaly investigation and reporting.
Why test data becomes difficult to manage
Test systems are usually built to run tests first and analyse data second. That creates problems as prototype, validation and early production work scales.
Formats are inconsistent
One test station may produce CSV files, another may write to a database, and another may generate PDF reports. Even when the tests are similar, the data structure may not be.
Context is missing
A result without product, prototype, configuration, limit, fixture, software version and run history is hard to use for root cause analysis.
Field and reliability teams need evidence
Engineering, reliability and customer-facing teams often need evidence, not just summary dashboards. They need to know what happened, when, where and under which conditions.
What Arc helps teams do
Connect engineering test data
Bring together prototype results, engineering runs, field logs and early production data so teams can see what changed across tests.
Analyse runs and anomalies
Identify abnormal measurements, failed runs, repeated symptoms, drift and configuration-specific patterns.
Compare run history
Connect test results to versions, configurations, limits, fixtures, timestamps, notes and report outputs.
Prepare data for AI analysis
Structure test data so engineering teams can query results, investigate anomalies and generate reports more effectively.
Common test data sources
Arc is designed for teams with test data spread across systems such as:
- LabVIEW applications
- TestStand sequences
- Custom automated test systems
- Prototype rigs and validation benches
- SQL exports and local databases
- CSV, XML, JSON and TDMS files
- Field-test logs and early production exports
- Manual engineering reports
- Customer update packs
- Design review summaries
Who is this for?
Hardware engineering leaders
Teams responsible for prototype learning, design validation, anomaly investigation and engineering decisions.
Test engineering teams
Teams building and maintaining test rigs, scripts, data pipelines and repeatable engineering test workflows.
Field and reliability teams
Teams connecting field observations, returned-unit evidence, reliability runs and investigation notes.
Hardware engineering teams
Teams that need test results to understand design issues, supplier problems, product reliability, anomalies and field failures.
Explore engineering test data use cases
Resource
Production test data management
Manage production test results across stations, lines, products and retests.
ResourceManufacturing test data analytics
Turn test results into insight across yield, failures, trends and quality issues.
ResourceTest data traceability
Connect test records to products, serial numbers, limits, stations and quality evidence.
ResourceTestStand data management
Use TestStand result data more effectively across production and quality workflows.
ResourceLabVIEW test data management
Structure LabVIEW outputs so teams can analyse, compare and reuse test results.
Secondary manufacturing and industry resources
Resource
Electronics manufacturing
Manage PCBA, box-build, functional test, inspection and production quality data.
ResourceAutomotive electronics
Connect test data to traceability, supplier quality, warranty risk and high-volume production.
ResourceAerospace and defence
Support high-reliability test evidence, auditability, configuration control and qualification workflows.
ResourceMedical devices
Compare run history and quality evidence across regulated device manufacturing and test.
ResourceBattery manufacturing
Track test results across cells, modules, packs, BMS, end-of-line testing and quality workflows.
ResourceIndustrial electronics
Manage test data for sensors, controls, instrumentation, drives and rugged electronic products.
ResourceSemiconductor test
Connect test data to yield, binning, reliability, failure analysis and production quality workflows.
How test data management differs from MES and QMS
MES systems help manage manufacturing execution. QMS systems help manage quality processes. Engineering test data analysis focuses on the detailed results, configurations, anomalies, run history and evidence generated by test workflows. Manufacturing systems can remain useful later, but Arc starts with the engineering analysis layer teams need before production-line systems are mature.
FAQ
What is test data management?
Engineering test data analysis is the process of collecting, structuring, analysing and reporting on prototype, validation, field and early production test results.
Why do hardware engineering teams need test data management?
Hardware engineering teams need test data management because prototype results, run logs, scripts, spreadsheets and reports are often scattered, making it difficult to compare runs, investigate anomalies and generate reports quickly.
Is test data management the same as MES?
No. MES manages manufacturing execution. Engineering test data analysis focuses on the detailed results, configurations, anomalies, run history and evidence produced by test workflows.
Is test data management only for production?
No. It can support production test, engineering validation, reliability testing, incoming inspection, supplier quality and customer evidence workflows.
Can Arc work with LabVIEW and TestStand environments?
Arc is designed for teams that generate test data from systems such as LabVIEW, TestStand and custom automated test environments.
What is the first step?
The first step is to map where test data is created, how it is stored, what context is missing and which engineering questions the team cannot answer quickly today.
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Bring a prototype or engineering test workflow and we’ll map where test results, scripts, spreadsheets and manual reports are slowing engineering decisions.
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