CONTEXT The capabilities of artificial intelligence (AI) have made a significant leap forward in the last few years with the advance of large language models (LLM) that can process natural language and generate texts, and there is a growing recognition of the transformative potential of LLMs in the statistical community. Responding to the increasing interest, HLG-MOS modernization groups – the Blue Skies Thinking Network and the Applying Data Science and Modern Methods Group – started an initiative draft a white paper on LLMs in the context of official statistics which was completed in a relatively short period of time of 4-month. The paper (https://unece.org/sites/default/files/2023-11/HLG2023%20LLM%20Paper.pdf) explored the opportunities and implications of LLMs for official statistics, associated risks, and provided recommendations and strategic considerations. Building on the LLM white paper, the project aims to further investigate the potential of generative AI, a broader category of advanced AI system that encompass LLMs (e.g., image generation), strategic considerations arising when statistical organizations want to use generative AI effectively and responsibly (e.g., governance, open models), as well as identify opportunities to actually co-develop concrete solutions. PROJECT OBJECTIVES The project will start with initial scoping, after which, the following three main activities are planned:
Participation is open to staff from statistical organisations and others interested in Official Statistics. Please contact the UNECE secretariat if you wish to participate in the project. |
CONTEXT Given the increasing need to become more open, transparent and efficient, many statistical organizations are undergoing a transition from traditional propriety software to open-source software. This transition, however, has challenges concerning support, maintenance, training, sharing conditions and legal aspects. The topic of open-source was discussed at the 71st CES Plenary Session and the CES Bureau has asked HLG-MOS to work on the topic. The purpose of the Statistical Open-Source Software (SOSS) project is to develop a better common understanding of the pros and cons, as well as the dos and don’ts of moving forwards to a more comprehensive use of open-source software for official statistics production, with an aim to make it a cornerstone of said production. PROJECT OBJECTIVES After a preliminary activity on scoping, the project aims to work on:
Participation is open to staff from statistical organisations and others interested in Official Statistics. Please contact the UNECE secretariat if you wish to participate in the project. |
|
|
|
|
|
|
|
|
|
|
|
STRATEGIC COMMUNICATION PROJECT OVERVIEW CONTEXT Official statistics are operating in a competitive and challenging environment – one that has changed significantly over the last twenty years. For traditional users of official statistics their values and importance is undisputed. Yet for the average citizen the digital and social media revolutions have meant that more and more people have instantaneous access to various data sources, outside official statistics. The 24/7 news cycle is reality, trust in government is decreasing and the fake news phenomenon is growing. Now more than ever, timely and relevant data and stories produced by statistical organizations are essential to healthy democratic societies as they remain the only independent, impartial, trusted and reliable source of official statistics. For official statistics to be beneficial to society, policy debate, and decision-making they must be known, understood, communicated and used. PROJECT OBJECTIVES The objectives for the project are to provide statistical offices with:
The outputs of the project will focus on enabling statistical offices to modernize their communications at the strategic level and help organizations look at communications strategies in a broader risk management and business continuity context. They include:
|
|
CONTEXT This project is considered as a fundamental step to enable efficient data and metadata management and governance in the context of CSPA. It supports and builds on ideas from the Modernisation Committee on Production and Methods about “Next Generation Data Management”. It has been defined by the need to satisfy new and more sophisticated demands of information products and services, where this only can be achieved making use of all kinds of data sources, traditional and emerging. PROJECT OBJECTIVES The project consists of the development of a reference framework, to describe a standardized data platform to support the design, integration, production and dissemination of official statistics. • A description of the structure and interaction of the major types and sources of data. |
DATA ARCHITECTURE PHASE 2 PROJECT OVERVIEW CONTEXT Statistical organisations deal with many different data sources – each with their own set of characteristics. Statistical organisations need to find, acquire and integrate data from both traditional and new types of data sources in an ever increasing pace and under ever stricter budget constraints, while taking care of security and data ownership. The 2017 HLG-MOS Data Architecture project developed the first version of the Common Statistical Data Architecture (CSDA). This Reference Architecture is a template for NSOs in the development of their own Enterprise Data Architectures. The project will focus on providing a more robust version of the Common Statistical Data Architecture as a result of validation against a number of use-cases and integration with the outcomes from other related groups. It will also provide guidance on implementing the architecture. PROJECT OBJECTIVES The objectives of this project are:
|
Linked Statistical Metadata CONTEXT HLG-MOS has been jointly developing common models and vocabularies to prevent each organization from developing their own using different vocabularies for the same concepts . Linked open metadata provides the next step. Instead of each organization having to maintain and update their individual vocabularies, this would be made available and managed in a centralized way. This not only reduces costs but also prevents discrepancies in structural and reference metadata and semantic heterogeneity. PROJECT OBJECTIVES The main objective of the project is to demonstrate the usefulness of linked metadata for the statistical community and to acquire hands-on experience in that field. It is proposed to fulfil this objective by constructing two concrete examples of linked metadata-based information systems: one aimed at improving the way that we disseminate core structural metadata, the other at supporting the advancement of the HLG vision by creating an harmonized and semantically enhanced information system grouping the main CSPA models and standards in a coherent and machine-actionable form. This will be achieved through three Work Packages:
|
CONTEXT There are many new opportunities created by data sources such as Big Data and Administrative data. These sources have the potential to provide more timely, more disaggregated statistics at higher frequencies than traditional survey and census data. It is clear that NSOs are challenged by the capacities needed to incorporate new data sources in their statistical production process while at the same time companies have appeared exploiting these new sources to provide alternative statistics. If official statistics can't find an answer to this, we are at risk of losing our unique position. We can, however, join forces and keep or even increase our value proposition by providing relevant, reliable and comparable data of high quality. NSOs are particularly well placed to integrate data from various sources and to use them to satisfy the needs of policy makers and other partners for data. It is thus time to intensify our efforts and commence working on it within the framework of an HLG project. PROJECT OBJECTIVES The main objectives of the project are to gain experience in data integration by pooling resources in joint practical activities and to translate experiences into general recommendations for data integration and to provide initial guidance for a quality framework. It will consist of these Work Packages:
|
CONTEXT There are many new opportunities created by data sources such as Big Data and Administrative data. These sources have the potential to provide more timely, more disaggregated statistics at higher frequencies than traditional survey and census data. It is clear that NSOs are challenged by the capacities needed to incorporate new data sources in their statistical production process while at the same time companies have appeared exploiting these new sources to provide alternative statistics. If official statistics can't find an answer to this, we are at risk of losing our unique position. We can, however, join forces and keep or even increase our value proposition by providing relevant, reliable and comparable data of high quality. NSOs are particularly well placed to integrate data from various sources and to use them to satisfy the needs of policy makers and other partners for data. It is thus time to intensify our efforts and commence working on it within the framework of an HLG project. PROJECT OBJECTIVES For 2017, the project proposes to develop an online, adaptive, practical guide to Data Integration for Official Statistics which supports successful data integration projects; using lessons learnt within the project and in related work. Furthermore, to undertake more joint experiments in high priority practical interest areas. The project has identified a number of areas where working together should bring faster results than working alone. The following activities were identified:
|
CONTEXT The importance of the relationship of Big Data to the official statistics industry has been identified at the 2012 High-Level Seminar on Streamlining Statistical Production and Services as well as at the 2013 UNECE Expert Group on the Management of Statistical Information Systems (MSIS). This project is important for the HLG’s broad programme of modernisation of statistical production. As a component of the modernisation programme, it will contribute to the goals of international harmonization and collaborative approaches to new challenges, improved efficiency of statistical production, and the modification of products and production methods to meet changing user needs. PROJECT OBJECTIVES The project has three main objectives:
|
CONTEXT To built on the momentum gained during the 2014 project a common shared Sandbox Computing environment was proposed to engage in collaborative research activities using various Big Data sources. Continuation of the experiments started in 2014 will allow to consolidate the technical skills. It will allow to test the production of multi-national statistics only basing on Big Data sources in a common environment. PROJECT OBJECTIVES The main goals of the 2015 project are:
|
CONTEXT An statistical industry architecture will make it easier for each organization to standardize and combine the components of statistical production, regardless of where the statistical services are built. The Common Statistical Production Architecture (CSPA) is a framework about Statistical Services to create an agreed top level description of the 'system' of producing statistics which is in alignment with the modernization initiative. CSPA provides a template architecture for official statistics, describing:
PROJECT OBJECTIVES To implement CSPA in practice by creating CSPA-compliant services that can be shared between processes and organizations (including resolving any specific licensing issues). To develop the resources necessary to support CSPA implementation, including training materials, and the proposed catalogue of services and other artefacts. To further test the applicability of the GSIM, and, if necessary, to suggest further refinements to that model for a possible future revision. The desired project outcomes are:
|
CONTEXT A review of the 2014 CSPA project has identified that the technical implementation governance and support is a significant area for improvement, the AWG is proposing a HLG project for 2015 which would see the expansion of the role of the governance and support offered by AWG to cover implementation and the establishment of a Technical Coordination Committee to support NSI’s and NSO’s who are developing or implementing CSPA compliant statistical services. PROJECT OBJECTIVES The project has three main objectives:
|
Earlier Projects (no text):