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UNECE ModernStats World Workshop 2024

UNECE ModernStats World Workshop 2024

21 - 22 October 2024
Geneva Switzerland

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Further details about this workshop can be found here


Topic 1: Improving interoperability using standards

Interoperability refers here to the ability of organizations to use more than one implementation standard (such as SDMX and DDI) together without information loss for producing a given statistical output. A recent initiative related to interoperability in this context includes the Data Governance Framework for Statistical Interoperability. Of particular interest would be contributions covering the following questions:

  • How have you approached achieving interoperability using implementation standards in your organization?
  • If you implemented more than one standard, how did you achieve interoperability across them?
  • Were reference ModernStats models useful for guiding this endeavour?
  • Where are the key opportunities (and pain points) for realizing interoperability?
Topic 2: Enhancing transparency with standards

The use of standards can improve transparency, making it easier to follow and understand the processes used to produce a given statistical output. Of particular interest would be contributions addressing the following questions:

  • Do you have insights to share on how to approach the use of metadata (and ModernStats standards) with the aim of improving transparency?
  • Were reference conceptual ModernStats models useful for guiding this endeavour?
  • Where are the key opportunities (and pain points) for realizing transparency?
  • How do models like GSBPM and GSIM help to clarify the processes in a way that is easy to follow and understand by everyone, improving transparency?
  • Have you implemented Linked Open Data (LOD) solutions, or have used LOD standards to model or disseminate (meta)data within or outside of your agency?
Topic 3: Tools for automating metadata-driven processes

Process automation is a key aim for those wishing to improve the efficiency and reproducibility of statistical production. Of particular interest are contributions addressing questions such as the following:

  • Have you automated parts of your production workflows using ModernStats models? What worked and what didn’t? Were ModernStats used? How were they used? Were there any limitations to their usefulness?
  • Have you implemented solutions using microservices, containers, cloud architecture, and automated pipelines for either data science or statistical production?
  • How have you approached workflow design and pipeline construction?
Topic 4: What should modern statistical production look like in 2025 and beyond?

This topic aims to take an overview of where we have got to (using conceptual and implementation standards), and where we now need to go addressing questions such as the following:

  • How has modernization worked for you and how do standards address your business needs? What needs remain?
  • What should the main objectives of modernization be? What are your plans for the future?
  • How can developments in the following areas influence the modernization of statistical production?:
    • Data science environments
    • Cloud computing environments
    • Developments in open source
  • Does AI have a role in shaping our production systems? How can we use standards to enhance the performance of AI?
  • How to foster collaboration between communities using different standards.


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