Draft Chapter 9: Data Dissemination 7 th Oslo Working Group, Helsinki 2012 Maluta Robert Kwinda Deputy Director: Energy Data and Integrity Department: Energy South Africa

Topics to be covered Overview Data (Coverage, Periodicity and Timeliness) Reference Period Dissemination Schedule Confidentiality Revision policy Dissemination format and access Meeting users´ needs

Overview Provides information on background, objectives and content of the Chapter Touches on 7 key dimensions of an effective data dissemination system: - Data (coverage, periodicity and timeliness); - Reference period; - Dissemination Schedule; - Confidentiality; - Revision policy; - Dissemination Format and Access; and - Meeting User needs. These dimensions are explained in detail and are basically the content of this Chapter More content may stilll be added on receipt of further inputs.

Data Coverage Coverage measures the extent to which statistical units account for the entire population. Information on coverage promotes interpretability of statistical findings as well as correct usage of micro data. Often readily available in metadata documentation Disseminated as part of metadata accompanying statistical releases

Data Periodicity Refers to the frequency of compilation of the data. Is determined by various factors, including but not limited to: - needs for analysis. - ease of compilation. Periodicity should as much as possible aligned to availability of data in recognition of longer compilation periods. Periodicity is usually expressed in terms of divisions in the calender and the divisions can be represented by year, semester, quarter, month and even a day. In many systems, priodicity can be the same whereas timeliness may vary according to data availability and economic sensitivity attached to various data elements.

Data Timeliness Refers to the lapse of time between reference peiod and dissemination time. Critical for usability/relevance of the data ( old data are useful for projection but more recent data are more useful and reflective of current situation) It has implications on various intenal actvities. Including preparation of accompanying commentatry, printing and preparation of CD-ROM´s. Timeliness can be affected by many factor including delays in the provision of data due to varying reporting peiod (calender vs. fiscal year). In such nstnces. Reasonable time stretch should be worked out. Improvements in timeliness should not compromise data quality, rather incremental improvements over time syncronised with improvement on data quality management systems should be the objectice.

Reference period Referes to the time period for which statistical data are collected or calculated. May be a calender year, fiscal year, semester, quarter, a month or even a day. Should be distinguished from publication time: the publication year may actually be much later than the reference year.

Dissemination schedule Ideally, a dissemination schedule should be released to the media well in advance (even a year prior dissemination). Must be made accessible to all users through release to general media and internet sites. Prior to embargo/scheduled time, statistics should be kept confidential. Advance access should be confined to certain individuals on the basis of genuine needs: for preparation of commentaries on social and economic conditions as may be reflected by the statistics. Advance access should be limited to a very short period ( 3 to 4 days)

Confidentiality Confidentiality covers aspects ranging from IT security to disclosure restrictions both within and between agencies. Information about confidentiality should be documented and disseminated. Statistical releases may include information on how confidentiality is managed. Publications on website can be used to describe how confidentiality is assured, including statements on the legal basis for data dissemination. Employees and end users of micro-data should enter into formal confidentiality agreements with the statistical agency. Data should be at a level of disaggregation which does not compromise confidentiality of individual information or records.

Revision policy Revisions may be as a result changes in the undelying data. They should be aimed at improving the quality and integrity of the statistical information Commonly made to incorporate improvements in the statistical framework: Concepts, definition, classifications, conversions, data sources and compilation methodology. Information about revisions should be communicated and well in advance through publication of revision schedule. The possible impact of major methodological or framework changes on key statistical figures should be clearly articulated in the revision schedule. Metadata which normally accompany staistical releases should describe revision policy for each data category.

Dissemination format and access Sustainable flow of comprehensive, reliable, accessible and timely energy statistics is vital for energy planning and policy formulation. Statistics of common public interest can be disseminated through press releases, posting of the statistics on the websites, Release of hardcopies, Production of CD-ROM products for key users and subscribers, online interactive systems which enable users to build statistical tables to thier own requirements and provision of data enquiry service, including customised data tabulation service. Data quality statements and explanatory notes for key finding should be should for part of the publication. Agencies must ensure that there are no barriers of access, i.e costs, internet(not everyone has access to internet), etc

Meeting user needs Users vary from politicians, govenment agencies private sectors and research institutes and data prodivers themselves. Users need to be properly identified, synthesised, undestood and prioritied. User needs are diverse and constantly changing and require proper management. Consultations and workshops with users should be carried out for the purposes of establishing, among other things, - How they use the statistics in their operation - Availability of the statistics - Adequecy of the statistics in terms of relevance, accuracy, consistency, timeliness, level of disaggregation, accessibility etc.) Its improtant to identify those who are interrested in particular data sets and try to manage users in groups. This can be achieved though web-based registration forms and conventional mailing lists.

Questions? Contact data: Maluta Robert Kwinda Department of Energy Tel: