Statistics Denmark is rethinking its data validation routines to focus resources more optimally. They are looking at data across statistical domains and need special knowledge of large multinationals' international production. Given limited resources, the team is rearranging validation routines to always focus on the errors that matter most, using a key account system. They are also monitoring the performance of validation routines to ensure they identify probable errors at the highest rate. The goal is to spend resources optimally by starting with potential errors of biggest impact.

1. Re-thinking Data Validation
Anette Morgils Hertz – ahz@dst.dk
Katja Overgaard – kao@dst.dk
Statistics Denmark
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2. Motivation
• Look at data across statistical
domains
• Special knowledge on
international production of
large multinationals
necessary
• Complex and time consuming
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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)
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4. Agenda
• About us
• SKV and Validation team
• Validation Routines then and now
• Monitoring the Validation Routines
• Key Account System
• Conclusion
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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
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6. SKV and Validation Team
• SKV team
• SKV → Company critical to our statistics
• Team dedicated to validate the reported data from these companies
• Validation team
• Responsible for all other validation and communication with the
companies
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Team No. companies
validated
Value validated
SKV 21 42,3 Bill.
Validation 82 4,01 Bill.

7. Validation Routines then and now
• Old system: 59 different valdation routines
• Now: Some validation routines are closed, others rearranged
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8. Key Account System
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One person = One company

9. Key Account System
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Changing weights →

10. Monitoring Validation Routines
• Monitoring report
• Measuring the quality of a validation routine:
ℎ𝑖𝑡 𝑟𝑎𝑡𝑒 =
𝑎𝑐𝑡𝑢𝑎𝑙 𝑒𝑟𝑟𝑜𝑟𝑠
𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑 𝑝𝑟𝑜𝑏𝑎𝑏𝑙𝑒 𝑒𝑟𝑟𝑜𝑟𝑠
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11. Monitoring Validation Routines
• Graphs for comparison
• Absolute errors
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Period Value in DKK Amount
2019M05 3.272.563 1

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