Page 84 - Proceedings of the 2017 ITU Kaleidoscope

2017 ITU Kaleidoscope Academic Conference

• Process Quality Frameworks – the framework for • Readiness Assessments – are used to determine the
process quality in national statistical institutes [15] existing environment and the preparedness for change.
proposes a structured framework for the quality of the UNDP has developed a prototype tool – the Rapid
statistical processes used to produce official statistics. Integrated Assessment (RIA) – to support countries in
• Quality Management Frameworks – for example, the assessing their readiness for SDG implementation. RIA
one implemented in the Central Statistics Office in reviews the current national development plans and
Ireland [16] is an extensive and long-term program of relevant sector strategies, and provides an indicative
activities aiming at ensuring that statistical production overview of the level of alignment with the SDG
meets the highest standards as regards quality and targets.
efficiency. • Common Assessments – useful for assessing and
• Quality Frameworks – provide a systematic mechanism promoting common approaches towards objectives
for ongoing identification and resolution of quality involving multiple stakeholders. The Common Country
problems and increased transparency to the processes Assessment (CCA) prepared by UNDP informs the
used to assure quality. An example is the Quality design of UN policies and programs at the country
Framework and Guidelines for Economic Co-operation level based on the review of context-specific data that
and Development (OECD) Statistical Activities, correspond to the SDGs and targets of the 2030
developed by the OECD in 2012 [17]. Agenda [25]. The CCA assists in identifying links
• Data Quality Assessment Framework – evaluates the among goals and targets in order to effectively
data quality of statistics. For example, the International determine mutually reinforcing priorities and catalytic
Monetary Fund created a data quality assessment opportunities for implementation of the new agenda as
framework [18] for comprehensive assessments of a whole.
countries' data quality. It defines five dimensions and it • Data Readiness – a tool to assess an organization’s
covers institutional environments, statistical processes, ability to produce and report data. In [26], a design-
and characteristics of the statistical products. reality gap model is applied for the assessment of big-
• Statistical Quality Management Framework – aims at data-for-development readiness, barriers and risks. This
setting out clearly and succinctly an organization’s kind of tools could similarly be applied to assess
commitment to quality in respect of particular statistical readiness for monitoring the progress towards the
outputs, and to describe the steps that it will take to achievement of the SDGs.
meet its quality aims [19].
Processes and standards. A statistical process is defined as
Enterprise Architectures (EA) are formal descriptions of the collection, processing, compilation and dissemination of
the structure and function of organizational components, the statistics for the same area and with the same periodicity
relationships between such components as well as the [27]. A statistical standard provides a comprehensive set of
principles and recommendations for their creation and guidelines for surveys and administrative sources collecting
development over time [20]. Some EA applications to information on a particular topic [28]. The following are
official statistics include: some processes and standards for statistics:

• Enterprise Architecture Reference Frameworks • Quality Assessment Process – their purpose is to define
(EARF) – aim at helping countries (in particular, EU the steps to process data in such a way that quality is
member states) with the production of statistics that preserved. The quality assessment process for Big Data
respond more quickly and cost-effectively to new developed by the OECD [29] presents a data quality
statistical business needs [21]. assessment process which includes a dynamic feedback
• Common Statistical Production Architecture (CSPA) – mechanism to adapt to the characteristics of big data,
provides support for the whole span of statistical and define the tasks that should be conducted at early
production process and gives a framework for stages to improve quality.
collaborating and sharing effectively [22]. • Codes of Practice (CoP) – the European Statistics
Code of Practice aims to ensure that statistics produced
Koskimäki and Koskinen [23] discuss Statistical Enterprise are not only relevant, timely and accurate but also
Architectures as tools for modernizing the national comply with principles of professional independence,
statistical systems by identifying the gaps and overlaps impartiality and objectivity [15]. Similarly, the UK
between CSPA and EARF from the point of view of the National Statistics Code of Practice sets out conditions
National Statistics Institutes. and procedures which govern access to data, including
access to data for research purposes, and appropriate
Readiness studies analyze the conditions in a country, city actions for unauthorized data disclosure [30].
or sector to see if data initiatives are likely to be successful
and, at the same time, they seek out suitable areas and
identify challenges that may exist when implementing such
policies [24]. Some readiness studies in the domain include:

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