Train Performance Management

Data Quality Management

RNE’s activities in data quality have made the association a reliable and respected provider of international reports based on its European train running data warehouse. RNE’s activities in this area are based on commonly agreed principles and procedures, not relying on additional manual effort, and using clear topology, descriptions, and rules.

High data quality in the Train Information System (TIS) is crucial for ensuring the reliability of the system itself, and when providing these data to IMs’ and RUs’ IT systems. Beyond that, TIS data stored in the data warehouse is used to create various reports presenting national and international train performance, and helps detect data quality problems that may occur at different stages, such as:

  • Delivery of incorrect data by legacy systems
  • Incomplete data transferred from legacy systems to TIS
  • Incompatible data provided by different legacy systems concerning the same train
  • Incorrect data processing or data loss within TIS
  • Incorrect data transfer to sub-systems (such as OBI)
  • Incorrect data processing or data loss in sub-systems

To provide reliable information at all these stages data quality checks must be performed.

The Members’ commitment related to the unique centralised Train Information System is expressed in the TIS Declaration.

To manage the whole process, the Data Quality Expert Working Group is composed of data quality experts tasked with the in-depth analysis of TIS data and the relationships between these data. The working group analyses problems and forwards the resulting tasks/corrective actions to the responsible partner.

RNE has created a clear and structured framework defining the requirements for data delivery described in the Guideline - Basic TIS Requirements on Data Delivery for Reporting Purposes.

To measure the quality of delivered data the Handbook on Management of Data Quality for Train Performance Analysis describes processes, performance indicators and related tools to reach data quality goals and overall strategical goals.