Many central banks have recently taken important steps to strengthen the communication of their statistics, aiming at increasing their outreach to a broader audience and enhancing their value to better support information users. These initiatives have underlined three main points. First, statistical communication is a key element for maximising the value of the data produced by central banks. Second, it is also an important ingredient supporting the effectiveness of their policies, with due consideration for the fact that data collected for statistical purposes should be protected from other needs. And third, communication effectiveness depends crucially on the degree of statistical literacy in the public; central banks can play an important role in addressing this point, for instance by making statistical content easy to understand, practising plain language, avoiding over-simplification and motivating the diverse user groups to engage with statistical information (data and metadata) and expand their knowledge.
Despite ongoing efforts, the communication of statistics remains a constant challenge, reflecting in particular the difficulties posed by new information sources, the increasing need for granular insights and the competition of alternative, sometimes poor-quality data. Fortunately, central banks appear well positioned, building on well-established credibility, visibility and trusted independence. They are also making substantial progress, especially in establishing a dedicated statistical communication function, identifying and selecting specific audience targets, crafting tailored content, using various (traditional and new) dissemination channels and leveraging technical innovation. A key objective is to foster user engagement with, and understanding of, central bank statistics.
First, a statistical communication function is typically set up to address both internal and external users. Success will often depend on defining clear priorities and objectives and on ensuring good cooperation with subject matter experts as well as with the main communication department of the central bank. Moreover, there is merit in following a structured approach to designing a comprehensive framework comprising all the various building blocks that constitute the “communication ecosystem” – eg the related information sources, audiences, channels and required multidisciplinary skills.
Second, a one-size-fits-all approach has clear limitations. To be effective, communication should reach out to and engage with different groups, consider their distinct needs and be tailored to their various levels of sophistication and knowledge. Central bank statisticians are indeed already following different approaches when communicating statistical information, depending on the specific user groups considered (such as the general public, researchers, students, journalists). Moreover, central banks are also developing new communication channels in order to reach out to the broadest possible audience. This calls for making the most of the wealth of data they have available, including granular data, and for using all media opportunities, especially social media.
Lastly, central banks are leveraging innovation to enhance their statistical dissemination methods and address the most pressing challenges. For instance, they are developing single “open source” data portals to strengthen their communication by making data more accessible and enhancing users’ experience. They are also exploring new ways to share very granular information without compromising confidentiality. And they are mobilising techniques based on artificial intelligence (AI) to support a wide range of tasks, from identifying user types to offering customised solutions that are tailored to specific user needs and degrees of literacy. In particular, the recent progress observed in the field of natural language processing (NLP) and large language models (LLMs) is providing important and promising new opportunities to strengthen central banks’ statistical communication.