Started Now: 2019 Modernisation Projects
STRATEGIC COMMUNICATION PROJECT OVERVIEW
Within the context of today’s ever-changing data environment, many statistical organizations are in the process of developing or reviewing their strategic objectives and their business models – leading to the articulation or a review of their mission and/or vision statements. More and more statistical organizations are involved in government-wide data strategy formulation. For statistical organizations to become strategic partners in the development of a national data strategy and for the successful development of a solid business model or the transition to a new business model, the vision must resonate with staff at all levels. For mission and vision statements to resonate with employees, staff need to be engaged.
The objective of the Strategic Communication Framework Project is to guide statistical offices in the development of a strategic approach to protect, enhance and promote the organization’s reputation and brand. Phase 2 of the Project will build on the experience and momentum gained in Phase 1 and will focus on developing a strategic approach to internal communications and stakeholder management/analysis in support of two priority topics for 2019 identified by HLG-MOS - Communicating our value and Setting the vision. It will also explore the experience of national statistical organizations in the development of government-wide data strategies in support of a third HLG priority – National Data Strategies.
The project will focus on:
- Developing organizational vision and strategic staff engagement strategies
- Developing effective stakeholder engagement management strategies
- Statistical organizations engagement in Government-wide data strategies
MACHINE LEARNING PROJECT OVERVIEW
The interest in the use of Machine Learning (ML) for official statistics is rapidly growing. For the processing of some secondary data sources (including administrative sources, big data and Internet of Things) it seems essential to look into opportunities offered by modern ML techniques, while also for primary data ML techniques might offer added value, as illustrated in the ML position paper mentioned above. Although ML seems promising there is only limited experience with concrete applications in the UNECE statistical community, and some issues relating to e.g. quality and transparency of results obtained from ML still have to be solved.
Based on mutual interest and building on existing national developments, the objective of the project is to advance the research, development and application of machine learning techniques to add value to the production of official statistics. To achieve this objective the Machine Learning (ML) will aim to:
Investigate and demonstrate the value added of ML in the production of official statistics, where "value added" is increase in relevance, better overall quality or reduction in costs.
Advance the capability of ML to add value to the production of official statistics.
Advance the capability of national statistical organisations to use ML in the production of official statistics.
Enhance collaboration between statistical organisations in the development and application of ML.
The objectives will be attained by:
Conducting pilot studies in ML solutions in: (a) common statistical processes (classification and coding; edit and imputation); and (b) the use of alternate data sources (imagery or big data; sentiment and web).
Researching and experimenting approaches to inform users on the quality of ML solutions, notably on accuracy
Identifying best practices in the development and application of ML solutions, including organisational aspects
Conducting the activities in groups of national organisations
2018 Modernisation Projects
STRATEGIC COMMUNICATION PROJECT OVERVIEW
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.
The objectives for the project are to provide statistical offices with:
- support in the development a strategic approach to communication and increase their capacity to review and renew their communication approach, methods and processes;
- with tools to increase their visibility, relevance and brand recognition; and
- tools to take a proactive approach to managing issues and reputation.
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:
- Defining skillsets of a professional communication programme and organizational options for the strategic communication function within the statistical organization;
- Developing a Communication Maturity Model, including metrics and a description of how to use the model and examples of how the model can be used;
- Developing guidelines to create a communications strategy and its implementation plan (including examples);
- Developing the branding options that are most relevant for statistical organizations; and
- Establishing an issues management process including guidance and tools to support statistical organizations in times of issues or crisis management.
DATA ARCHITECTURE PHASE 2 PROJECT OVERVIEW
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.
The objectives of this project are:
- To complete the development of the Common Statistical Data Architecture, testing the reference architecture defined in 2017 against other use-cases
- To apply and validate the Data Architecture against the outcomes from other groups like UN-GWG, Data Integration project and groups working on statistical ontologies.
- To provide guidelines to support statistical organisations in using the Common Statistical Data Architecture.