Difference between revisions of "Reporting Tool"
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=== Use cases === | === Use cases === | ||
| + | The use cases are grouped in the three phases. | ||
| + | * analysis of specific messages and the actual situation | ||
| + | * follow up on errors, incidents and technical issues | ||
| + | * Trigger for DQ assurance cycle | ||
| + | * publish KPI indicators per service | ||
| + | * Dashboard per service | ||
| + | * Analysis of development | ||
| + | * Strategic decision | ||
| + | * Management Reporting | ||
| + | * trigger for performance analysis and measurements | ||
| + | * observe and alarm for abnormal states | ||
| + | * trigger for performance analysis and measurements | ||
| + | * Smart services based on aggregated data | ||
=== Enablers === | === Enablers === | ||
| + | * Message | ||
| + | * Performance Indicators | ||
| + | * KPI | ||
| + | * Forecasts | ||
| + | * Benchmarks | ||
| + | * Content Based Reporting | ||
| + | * Tickets | ||
=== Objects === | === Objects === | ||
| + | * drill down to individual message and its state (new) | ||
| + | * aggregate data and hand over | ||
| + | * drill down to a time slot (h/d/w/m/y) per state (aggregate) | ||
| + | * message removal at end of lifecycle | ||
| + | * drill down to specific message and its state (not all) | ||
| + | * removal at end of lifecycle | ||
| + | * store eternally | ||
=== Systems === | === Systems === | ||
| + | * Power BI for phase 2 and 3 at Xrail | ||
| + | * Elastic stack for phase 1 at Xrail | ||
| + | * Influx/Grafana at RailData | ||
=== Principles === | === Principles === | ||
| + | * Sensor/Measurement beside the service | ||
| + | * Aggregation per step from operative data to reporting data | ||
| + | * One reporting tool to RUs / stakeholders | ||
| + | * Data Lake? | ||
| + | * Easy access for all and reports can be found for all services | ||
Revision as of 14:01, 30 April 2021
Contents
Analysis
Report phases
The analysis detected three phases with specific requirements.
- Operative Support & Monitoring
The first phase focus on supporting the operation of a service. It includes near real time reports and monitoring of characteristic figures per Service.
- Short term reports & DataQuality analysis
The second phase delivers the necessary data for data quality analysis and checks for data improvements. This happens on the tactical level.
- Long term Analysis
The third and last phase tackles long term reports from one or several sources. The reports are used for strategic decisions and potentially for smart data based services.
Use cases
The use cases are grouped in the three phases.
- analysis of specific messages and the actual situation
- follow up on errors, incidents and technical issues
- Trigger for DQ assurance cycle
- publish KPI indicators per service
- Dashboard per service
- Analysis of development
- Strategic decision
- Management Reporting
- trigger for performance analysis and measurements
- observe and alarm for abnormal states
- trigger for performance analysis and measurements
- Smart services based on aggregated data
Enablers
- Message
- Performance Indicators
- KPI
- Forecasts
- Benchmarks
- Content Based Reporting
- Tickets
Objects
- drill down to individual message and its state (new)
- aggregate data and hand over
- drill down to a time slot (h/d/w/m/y) per state (aggregate)
- message removal at end of lifecycle
- drill down to specific message and its state (not all)
- removal at end of lifecycle
- store eternally
Systems
- Power BI for phase 2 and 3 at Xrail
- Elastic stack for phase 1 at Xrail
- Influx/Grafana at RailData
Principles
- Sensor/Measurement beside the service
- Aggregation per step from operative data to reporting data
- One reporting tool to RUs / stakeholders
- Data Lake?
- Easy access for all and reports can be found for all services
