Documentation and support
Work Session on Statistical Metadata (METIS), Geneva Switzerland, 10-12 March 2010 - "Applying the GSBPM within an NSI: Experiences and Examples from Australia"
Australian Bureau of Statistics (ABS) has managed to place GSBPM within its enterprise architecture and it was applied for various purposes by the Statistical Methods and ICT divisions. This presentation describes the ABS experience with GSBPM as being very positive, although there are still unresolved issues that need to be addressed in order to further promote its application and maximise its values. These include a better understanding of the model by the staff and of the relationship between GSBP and DDI, as well as the establishment of an informal community around it.
METIS- wiki, Metadata Case Studies - "Complete case study (New Zealand)"
In 2004 Statistics New Zealand has developed its Business model Transformation Strategy (BmTS) and, in this context, it also developed the generic Business Process Model (gBPM). In this document it is described in its seven phases: 1. Need, 2. Develop and Design, 3. Build, 4. Collect, 5. Process, 6. Analyse and 7. Disseminate. The GSBPM grew from this model, to which two phases were added. High level benefits are detected as well costs which have not been calculated but should be taken into consideration.
Work Session on Statistical Metadata (METIS), Geneva Switzerland, 5-7 October 2011 - "Establishment of the Korea Statistical Business Process Model and Quality Management"
Through quality assessment projects, the Statistics Korea (KOSTAT) realized that to assure the quality of statistics a Standard Business Process needed to be developed. The standard process was decided to be reorganized on the basis of GSBPM. Some of its phases and processes were grouped while some new ones were added in order to cope with the Korean environment. The result is a draft of the Korea Statistical Business Process Model (KSBPM) where each mega process (level 0) is then described with 3 levels of specification. With regards to the mega process of Statistical Business Process, level 1 phases are: 1. Plan and specify needs, 2. Design, 3. Build, 4. Collect, 5. Process, 6. Analyse, 7. Disseminate, 8. Archive, 9. Evaluate. In each phase a number of sub processes can be defined (level 2) as well as descriptions for them (level 3).
High Level Seminar for Eastern Europe, Caucasus and Central Asia Countries (EECCA) on "Quality in Statistics: Metadata" - 27-29 June 2012, Tbilisi, Georgia - "Streamlining the Statistical Production in Turkish Statistical Institute (TurkStat)"
This document highlights, with regards to TurkStat, the need to define and standardize statistical processes in a coherent way which was satisfied by the adoption of GSBPM. In particular, pilot studies led to the drafting of a national version, the TurkStat Draft Statistical Business Process Model. The model comprises four levels. Level 1 is made up of seven phases: 1. Specify needs, 2. Design, 3. Build, 4. Collect, 5. Process, 6. Analyse, 7. Disseminate. Within each phase there are sub processes (level 2), within sub proceses there are sub sub processes (level 3) and within them the activities/job steps (level 4). While the first two levels are mostly generic, level 3 and 4 were defined specifically for TurkStat.
*"Prices System Improvement Project
Early adopter of GSBPM in ABS was the Prices System Improvement Project. The project aims to design an end-to-end system for 5 Price Indexes and the GSBPM was successfully used as a guide to harmonise processes across the 5 Price Indexes. The document shows the nine phases (level 1) of the GSBPM where each sub-process (level 2) is defined with its descriptions (level 3).
"SDMX in the Vietnam Ministry of Planning and Investment - A Data Model to Manage Metadata and Data"
In the context of the European Technical Assistance Programme for Vietnam, an SDMX based solution was chosen in order to assist the Ministry of Planning and Investment. The document presents the data related issues to be solved, regarding especially the inconsistency of formats and definitions between different agencies as well as poor data and metadata management and the absence of central storage. The reason for choosing SDMX based solutions is to create an environment for standardising and harmonising the code sets and classifications, create a storage environment to hold datasets and link data and metadata.
"Overview of the Philippine Implementation Plan of SDMX Model and Technology"
This presentation outlines the Philippine Statistical System's (PSS) plan for implementing SDMX to improve accessibility and timeliness. Furthermore this plan would facilitate data sharing and exchange between other Philippine government statistical offices and agencies and between government national offices and their regional offices. The dissemination and exchange of data will thus use the SDMX-ML format and the regional offices will be coordinated through the development of a Philippine Statistical System (PSS) SDMX Registry.
"SDMX at ABS: Why and How we use SDMX"
Australian Bureau of Statistics uses SDMX as it is an efficient solution that aligns perfectly with its principles: timely and relatable statistics, integrity of data, statistics accessible by everyone, quality and trust of providers. Two case studies are presented and discussed with the highlighting of the potential of SDMX to provide integrated data exchange. As a result ABS identifies the need to keep back-end systems independent of version changes and to use it as a common framework for guiding each statistical domain.
Supporting effective use of information and communication technology in population census operations - Practical Advisory workshop, Moskow April 2012 - "UNSD/DFID project on improving the collation, availability and dissemination of national development indicators, including mdgs"
As part of the UNSD/DFID pilot project, Cambodia was found to lack in data coordination, in harmonization of statistical standards and was found to have discrepancies between international and national data for some indicators due to the use of different definitions and methods. This document describes such challenges as well as the Strategic Plan that was adapted to face them. The plan revolves around the establishment of the National Data Repository to store database and disseminate national indicators, and that of the National Development Indicator Registry for publishing and transferring of national development indicators to UNdata portal. In order to facilitate the data flow between these organisms the SDMX-ML format was used. The data flow includes the flow from line ministries to the National Repository, from National Repository National SDMX Registry and then to UN data.