Open Data, Official Statistics and Statistics Education-Threats, and Opportunities for Collaboration (original) (raw)
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Althought Big Data (BD) provides new methods and ideas for knowledge communities, becoming agents of economic change and of innovation, there is a problem of a new dimension of digital devise between the people who “haves” and “have-nots” skills and competencies to gain new knowledge from handling BD which affects National Statistical Institutes (NSIs). In details, as the private sector collect much of the BD, NSIs have by no means a monopoly on producing statistics while who are in a position of accessing and analyzing data and have the processing power, have the “scientific evidence” to guide environmental policy development. For this reason, nowdays NSIs are under pressure; to stay relevant they have more attention paid to the computer science skills of their statisticians training their staff in this fast-developing field for different aspects of BD related work. In an effort to accommodate these widespread needs, the collaboration of NSIs with the private sector, academia, and civil society could be the way of working to determine the right mix of statisticians and any of the new categories of jobs (i.e. data scientist , DSst). In fact statisticians have proven to be very good in developing and maintaining their data quality assurance frameworks, while relevant data need to be delivered frequently, in a timely manner, and with much detail as DSsts could be available to do. So, is Dsts the next generation actors into this data-driven statistical systems? The answer is not. NSIs should try to keep them in the spotlight with the trusted smart statistics and experimental statistics focusing on these open-ended research topics: a) the border between official statistics, experimental and trusted smart statistics (in not yet easy to feel); b) the external data sources (not always “open”); c) the BD quality (in a basic processing framework). In this fast-moving data landscape, NSIs could play a central role both in making more trust information with expanding the traditional role of statisticians in emerging fields, like DSst. In this respect, the adoption of a data stewardship (DSship) approach enables NSIs to evolve from being a statistics producer to becoming a public service provider that facilitates a joined-up approach to trust in statistical products and statistics across different data and statistics communities.
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