27 June 2026
Panel discussion during the WCO Council Sessions on “Data as a Strategic Asset: Shaping the WCO’s Role in a Data-Driven Customs Landscape”.
Discussions focused on technical, policy and regulatory arrangements across the full data lifecycle.
Investment, safeguards and capacity building are all elements that need to be strengthened for Customs to leverage data.
In recent decades, as processes and procedures have been digitalised, data has become a key asset for Customs administrations. Strengthening the use of data is considered key by the WCO to enhancing targeting capacities, improving operational processes and engaging meaningfully with trading partners and international organisations. For the WCO, the challenge is no longer instilling the value of data, but ensuring its members build the governance, capabilities and trust needed to use it effectively.
Although much of the attention is usually placed on methods and tools for analysing data, specific care is needed to ensure that the data itself receives the attention it deserves in high-level policy discussions. Access to timely, high-quality, reliable and well-governed data is a prerequisite for effective data use. If issues in the underlying data, including its quality and governance, are not addressed, investments in technologies such as artificial intelligence (AI) will not deliver the expected operational performance.
To enable WCO members to share their practices and experiences regarding data, a panel discussion was held as part of the June 2026 council sessions. Under the title “Data as a Strategic Asset: Shaping the WCO’s Role in a Data-Driven Customs Landscape”, panellists from the United Kingdom’s HM Revenue & Customs, Mauritius Revenue Authority and Japan Customs introduced their administrations’ approach to data governance, explained how they had built internal capacity and discussed how to navigate the challenges associated with data-sharing and the deployment of emerging technologies.
Developing a data governance framework can be complex, requiring careful planning and execution
As data becomes more central to Customs operations, the question is not only how to use it, but also how to protect and manage it properly. The panellists explained their approach to data governance, namely a mix of technical, policy and regulatory arrangements across the full data lifecycle. They highlighted that the objective of a data governance framework was to ensure that the data was accurate, consistent and secure. “Data governance is not about limiting the use of data. It is about enabling its trusted use,” said one of the panellists.
The first step in building such a framework is to evaluate existing policies on data ownership, classification, quality, security and privacy, and align them with regulatory requirements and best practices. Data standards and definitions must also be examined. When doing so, a cross-functional collaboration approach should be adopted to ensure that the development of a data governance framework does not take place in isolation, but instead involves a wide range of experts, including those working in IT, operations and legal, with each role and responsibility clearly defined.
Data management must be grounded in the understanding of the Customs administration’s wider future strategy
Data is a means, not an end. The panellists pointed out that data management should be treated as a strategic discipline with a long-term roadmap, rather than a one-off IT project. The focus should therefore not just be on addressing legal issues, but also on understanding where the Customs administration aims to go in the future.
Key Customs data and analytical functions should be created within the administration
Any data operating model should define roles, responsibilities, processes and standards for managing data across the administration. The roles must be clear and meaningful, with real accountability and capability behind them.
A panellist explained that, in his administration, a team bringing together data governance, design, exploitation and innovation functions had been established to support operational analytical use of data. These professionals work collaboratively as embedded members of multi-functional teams, including policy, operational, project management and digital staff and, in many cases, external suppliers, that plan, manage and deliver key Customs transformation investments.
Alongside data and analytical professionals, these multi-functional teams work through expanding data design, data exploitation and data innovation. They apply user-centred design principles to ensure that data is defined, structured, and applied in ways that better meet operational requirements, analytical policy, and, in many cases, customers’ needs.
An internal governance function is also needed to oversee the deployment of AI tools, avoid fragmented or uncoordinated adoption of AI and ensure transparency
Practice related to the deployment of AI tools was also addressed, with a panellist explaining that his administration had ensured central visibility and oversight of AI use cases by creating a dedicated function within the administration and establishing an internal governance body. This avoids fragmented or uncoordinated adoption, ensuring safe adoption and the sharing of insights and tooling across the administration. It also enables the administration to identify the highest-value initiatives, prioritise them, and take them forward safely.
Implementing a data policy requires investment in technological tools
Panellists also pointed out that robust information and communication technology (ICT) systems and tools must be put in place to maintain confidentiality, integrity, availability, accuracy, legitimacy and accountability during data collection, processing, storage, sharing and disposal. Examples include next-generation firewalls, intrusion prevention and detection systems, endpoint solutions, system-to-system data-sharing without human intervention and access control mechanisms.
Data exchange can only progress as fast as the trust and the safeguards underlying the exchange
Rules governing data should also address data-sharing with foreign administrations, which means bringing data protection experts on board.
“Data should be shared only when the purpose of its use is defined, and the conditions of its use are secure. In practice, this means that data-sharing is based on bilateral or multilateral agreements which stipulate which data can be shared, how it can be used, and how it must be protected,” explained a panellist.
The capacity gap between members will continue to widen unless we invest in capacity building
While acknowledging that technology deployment must remain member-driven and sensitive to operational realities, the panellists recognised the need to close the gap between administrations in data infrastructure development and data governance to enable them to implement AI and machine learning tools efficiently. WCO members have contributed to a comprehensive set of resources under the Smart Customs Project, funded by China Customs. Leveraging these resources, regional and national workshops are regularly held to develop recommendations and roadmaps with participants, including the development of strong data frameworks.
Finally, under the BACUDA Project, the WCO has released an e-learning course on building data governance frameworks within Customs administrations. This course, available to Customs officers on the WCO CLiKC! platform under the title “Data Governance for Customs”, covers core components of data governance, including strategy, policies, roles, data quality and security, as well as practical tools and Customs case studies. The development of the e-learning course was funded by the Customs Cooperation Fund of Korea (CCF/Korea).