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Motivation

Learn how to establish a new validation routine to ensure optimal data accuracy across statistical domains. Gain insights on international production, complex validation processes, and monitoring performance.

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Motivation

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  1. Re-thinking Data ValidationAnette Morgils Hertz – ahz@dst.dkKatja Overgaard – kao@dst.dkStatistics Denmark

  2. Motivation • Look at data across statistical domains • Special knowledge on international production of large multinationals necessary • Complex and time consuming

  3. Motivation • Given the same resources available – how to establish this new validation routine? • Rearranging our validation routines • Always focus on the errors that matter the most (Key account system) • Monitoring the performance of the validation routines, to ensure they perform optimally (monitoring report)

  4. Agenda • About us • SKV and Validation team • Validation Routines then and now • Monitoring the Validation Routines • Key Account System • Conclusion

  5. About us • What is International Trade in Goods (ITGS)? • Import/Export of goods • Around 9000 commodity codes and 250 country codes • Published monthly • What is International Trade in Services (ITSS)? • Import/Export of services • Around 70 service codes and 250 country codes • Published aggregated monthly and detailed quarterly • What is Balance of Payments • Use data from different sources incl. ITGS and ITSS

  6. SKV and Validation Team • SKV team • SKV  Company critical to ourstatistics • Team dedicated to validate the reported data from these companies • Validation team • Responsible for all other validation and communication with the companies

  7. Validation Routines then and now • Old system: 59 different valdation routines • Now: Some validation routines are closed, others rearranged

  8. Key Account System One person = One company

  9. Key Account System Changing weights 

  10. Monitoring Validation Routines • Monitoring report • Measuring the quality of a validation routine:

  11. Monitoring Validation Routines • Graphs for comparison • Absolute errors

  12. Conclusion • To spend our resources optimally we must monitor every validation routine • A change of focus is necessary • Start with the potential errors with the biggest impact

  13. Thank you! Anette Morgils Hertz, email: ahz@dst.dk Katja Overgaard, email: kao@dst.dk

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