STRATEGIC COMMUNICATION PROJECT OVERVIEW 

CONTEXT

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.

PROJECT OBJECTIVES

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

CONTEXT

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.

PROJECT OBJECTIVES

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