Central bank statistics are undergoing profound transformations as the information ecosystem continually evolves. On the supply side, innovation, especially through digitalisation and novel analytical techniques, is making data more available, usable and reusable. This has been accompanied, on the demand side, by the need for better evidence on which to base policymaking when addressing growingly complex issues. As a result, data have increasingly become a core strategic asset underpinning central banks’ operations and functions, including their statistical responsibilities. Such an evolution has certainly improved the quality of the reference statistics central banks produce, especially in terms of accuracy, granularity and timeliness. But it has also highlighted the importance of safeguarding the integrity of the information central banks manage and use, through shared principles, robust methodologies as well as adequate governance and collaboration with the stakeholders populating the broad data ecosystem.
Fortunately, central banks’ long-standing expertise with data has enabled them to embrace these transformative changes effectively, with widespread benefits spilling over to their various functions. Their statistical role and related activities, both within the organisation and in the broader economic and financial system, have equipped them with the tools to sustain the provision of trustworthy information for policymakers and society at large. In turn, their extensive data knowledge has increasingly become instrumental in supporting their various operations, reflecting the fact that data have become ubiquitous in today’s world.
After various decades of profound transformations, the central bank statistical function has matured significantly, characterised by a relentless commitment to robust ethical standards, strong professional independence and sound international cooperation. Yet it also needs to keep pace with the growing complexity of policy issues, the uncertainty arising from evolving global and more interlinked challenges, as well as the rapid but often unpredictable advances in technology. Quite ironically, the more innovation opens new avenues for using and producing data, the greater the need for strengthening the fundamental principles, practices and methodologies developed by central bank statisticians over time.
Addressing these issues calls for developing an ambitious roadmap to further enhance the central banks’ statistical role. Drawing on the IFC members’ experiences, six main priorities have emerged thus far:
- To make sense of the expanding data universe, which provides multiple opportunities for improving central banks’ statistical offerings, especially in terms of the breadth, speed and timeliness as well as the depth and length of the information produced.
- To leverage innovative techniques to transform the abundance of available data into meaningful and policy-relevant insights. In particular, the emergence of generative artificial intelligence (AI) is offering entirely new prospects, especially to support statistical and analytical processes.
- To continue to deal with important priorities in terms of “traditional” statistical work, calling for continuous refinements in methodologies, data collection and management processes. A key aspect relates to the implementation and ongoing maintenance of international statistical standards to secure the coherence, consistency, comparability and relevance of official statistics.
- To expand the availability of central bank data and statistics to broader audiences. Greater and better data dissemination can improve transparency and public trust, ultimately underpinning policy effectiveness. Reflecting this, important efforts are being made to make data adequately accessible, usable and shareable, with communication and engagement with users playing an increasingly important role.
- To develop agile, integrated and scalable information technology (IT) architectures supported by adequate governance, not least to advance innovation effectively and responsibly. Migration to novel solutions has to be carefully managed as an evolutive process rather than a disruptive shift, allowing for a gradual phasing out of legacy systems. Other key requirements are to develop integration capabilities, both to combine novel techniques with traditional ones and to link data from disparate sources and with different formats.
- To enhance cooperation with the various stakeholders in the data ecosystem, especially to support the international exchange of data and best practices that play an essential role in central bank statistics. To be effective, collaboration should be mindful of preserving the well-established ethical standards and professional independence of central bank statisticians.