The Discom Data Hub is the central platform for managing, integrating, and analyzing measurement and test data. It connects various data sources, standardizes formats, and ensures that all stakeholders can access consistent and valid information at all times. Centralized data storage reduces errors, speeds up analyses, and optimizes collaboration between development, production, and quality assurance. DDH thus lays the foundation for data-driven decisions and supports the use of modern technologies such as AI and big data.
Key Features of the Discom Data Hub (DDH)
Monitoring Services
Intelligent Monitoring:
Background services continuously monitor production metrics such as test bench activity, scrap rates, error codes, and individual value time series.
Notifications:
If there are deviations from defined parameters, push or email notifications are automatically sent to WebPal or other clients. Users can individually specify which events they would like to be notified about.
Data Access & Analysis
Multiple DDH instances can be securely networked (cloud/on-prem), including a gateway API as a single point of access. Edge-close access to local data plus a global overview—reduces latency, minimizes data duplication, and supports data protection regulations.
Python SDK:
Direct access to NVH data for data engineers and analysts, including visualization, analysis, and simulation.
Accelerated Analyses:
Fast data manipulation using familiar Python workflows.
Process Optimization:
Simulations for fine-tuning production parameters before implementation.
Custom Reports:
Flexible export functions for tailored reports.
Integration & Openness
Open APIs:
Easy integration with existing analytics platforms and data pipelines.
Automation:
Support for automated workflows and integration of machine learning/AI services.
DDH Python SDK – Enabler for Data Access, Feature Engineering, and ML Workflows
ㅤ
Direct, high-performance access to measurement and production data
- Unified API for curve/spectral data, individual values, error codes, and metadata (e.g., test conditions, bench IDs).
- Batch reading and lazy streaming for large data sets—ideal for training datasets.
Out-of-the-box feature engineering
- Access to domain-specific features (e.g., NVH parameters, spectral aggregates) facilitates the derivation of robust features.
- Time-series transformations (aggregation, windowing, trend/change-point detection)—suitable for classification, anomaly detection, and forecasting.
Simulations & “What-if” Analyses
- Curve limit simulation: Test the impact of limit value changes on scrap rates before implementation.
- Support for A/B comparisons between production lines/test benches.
Integration with Common ML Stacks
- Seamless integration with NumPy/Pandas/Scikit-learn/TensorFlow/PyTorch for training, evaluation, and deployment.
- Export to Parquet/CSV/JSON or directly to your feature stores/data lakes.
Typical use cases you can accelerate with DDH and the Python SDK
ㅤ
Predictive Maintenance:
Detect early anomalies on test benches; train models using reference-based time series.
Root Cause Analysis:
Consistently evaluate heterogeneous error codes; identify correlations between spectra/individual values and NOK.
Quality Optimization:
“What-if” simulations for threshold values; rapid rollout to multiple lines via the grid.
Production Comparison:
Benchmarking across locations using an identical data model; fast BI dashboards and AI insights.
MLOps/Automation:
Events → trigger pipelines (feature recompute, retraining, deployment), all secured via DDH gateways.
Your benefits
Improved Measurement Analyses
- Early detection of errors and trends
- Increased transparency and quality assurance
- More efficient collaboration between teams
- Flexibility through cloud or on-premises operation
Have we caught your interest?
Do you have any questions?
Would you like a consultation?
