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Data governance best practices for the AI-ready airport

Author

Listed:
  • Strome, Trevor

    (Winnipeg Airports Authority, Canada)

Abstract
Airports generate vast amounts of data across various systems that are crucial for operational efficiency, safety and enhanced passenger experience. As airports increasingly rely on this data in order to adapt to evolving passenger experience demands, business environments and regulatory requirements, data governance becomes essential for managing, safeguarding and leveraging data effectively. A robust data governance framework provides the structure for ensuring data quality, security and compliance while enabling airports to harness data for artificial intelligence (AI) applications such as predictive maintenance and passenger flow management. By starting with a clear scope, objectives and policies, airports can build a data governance framework that addresses both current needs and future challenges. This paper explores the role of data governance in making airports AI-ready, outlining best practices for implementing a governance programme. It highlights the importance of tools such as data lineage tracking and the need for strong data security measures to comply with regulations such as General Data Protection Regulation (GDPR). The paper also emphasises the need for a culture of accountability, outlining key roles such as data stewards and chief data officers (CDOs) to ensure consistent, robust data management. As AI adoption grows, airports must focus on maintaining data integrity, fostering transparency and ensuring regulatory compliance to unlock the full potential of their data assets while safeguarding privacy and building stakeholder trust.

Suggested Citation

  • Strome, Trevor, 2024. "Data governance best practices for the AI-ready airport," Journal of Airport Management, Henry Stewart Publications, vol. 19(1), pages 57-70, December.
  • Handle: RePEc:aza:jam000:y:2024:v:19:i:1:p:57-70
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    More about this item

    Keywords

    data governance; artificial intelligence; AI; data privacy and security; regulatory compliance;
    All these keywords.

    JEL classification:

    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

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