Data Portals in National Statistics Offices: Case of Developing Countries
2 likes168 views
The document discusses the deployment, design, and technology of data portals in National Statistics Offices (NSOs) as part of the country-led data revolution. It highlights the increasing demand for data, the challenges faced by NSOs in managing data processes, and the necessity for improved capacity, technology use, and design integration to meet the evolving information needs. The conclusion outlines a transformation roadmap to enhance data dissemination through better practices and novel approaches, emphasizing user-centered design and automation.
![Demand for data
Not new & on the rise
Marrakech Action Plan for Statistics (2004)
Monterrey Consensus (2002) [MfDR]
Paris Declaration (2005)
Efforts to meet the demand
African Charter on Statistics & Strategy for
Harmonization of Statistics in Africa (2009)
Dakar Declaration on the Development of
Statistics (2009)
Busan Action Plan for Statistics (2011)
Traditional key drivers](https://image.slidesharecdn.com/n2g13ducq263zyhyuhq4-signature-ffc5b50490f922e94d506f87a2e2a213b2bf3fd81eb94b6040732b04e10ee8e5-poli-170201152520/85/Data-Portals-in-National-Statistics-Offices-Case-of-Developing-Countries-7-320.jpg)
Data Portals in National Statistics Offices: Case of Developing Countries
- 1. Data Portals in National Statistics Offices (NSOs) - A view on deployment, design and technology - @rajiv_r_in
- 2. Context Country-led data revolution NSO processes Focusing on dissemination Data portals Operating environment Emerging themes Conclusion AGENDA
- 5. Demand for data Not new & on the rise 2015: MDG report: In sub-Saharan Africa, where poverty is most severe, 61 per cent of countries have no adequate data to monitor poverty trends. - The Millennium Development Goals Report 2015 2000: Setting out the Millennium Development Goals (MDGs): Large gaps in data at the forefront. http://www.un.org/millenniumgoals/2015_MDG_Report/pdf/MDG%202015%20Summary%20web_english.pdf
- 6. Demand for data Not new & on the rise Aid effectiveness MDGs National development plans Traditional key drivers
- 7. Demand for data Not new & on the rise Marrakech Action Plan for Statistics (2004) Monterrey Consensus (2002) [MfDR] Paris Declaration (2005) Efforts to meet the demand African Charter on Statistics & Strategy for Harmonization of Statistics in Africa (2009) Dakar Declaration on the Development of Statistics (2009) Busan Action Plan for Statistics (2011) Traditional key drivers
- 8. Demand for data Not new & on the rise Efforts to meet the demand New demand from civil society National Open Data Initiatives Open Government Partnership Traditional key drivers http://ckan.org/
- 9. Demand for data Not new & on the rise Efforts to meet the demand New demand from civil society New sources and new players Big Data Private sector Traditional key drivers https://www.premise.com/
- 11. Sustainable Development Goals No excuse not to have data “No one must be left behind” http://sdgcompass.org/sdgs/
- 12. Sustainable Development Goals No excuse not to have data Expression Sustainable Development Goals Data revolution http://www.uwtsd.ac.uk/ba-ethical-political-studies/
- 13. Sustainable Development Goals Expression No excuse not to have data Data revolution Call for improved availability and accessibility of data http://www.undatarevolution.org/report/
- 14. Country-led data revolution 14 Context Country-led data rev NSO Processes Focusing on dissemination Data Portals Operating Environment Emerging Themes Conclusion
- 15. National Statistics Offices (NSOs)
- 16. National Statistics Offices (NSOs) Any effort to implement the data revolution at the country level will need to address the role of the NSO… - Rachel Quint, Program Fellow, Global Development and Population Programme, Hewlett Foundation. http://post2015.org/2015/05/01/toward-an-african-data-revolution/ As stewards of official data, NSOs should be at the heart of each country’s data revolution. - Shannon Kindornay , adjunct research professor at Carleton University, Canada http://www.scidev.net/global/data/opinion/flashy-innovation-fuel-data-revolution-post-2015.html Center stage
- 17. National Statistics Offices (NSOs) Center stage Fit for the purpose? http://www.oecd-ilibrary.org/development/a-road-map-for-a-country-led-data-revolution_9789264234703-en
- 18. National Statistics Offices (NSOs) Center stage Fit for the purpose? There is too little investment in people and skills. National statistical offices have only limited powers and status within national statistical systems. Data are not adequately disseminated and used. The design and management of statistical processes is not satisfactory. The potential of Information Technology is not fully harnessed. Technical and financial aid is not well-aligned with national priorities. Countries face significant costs in managing aid projects. The overall coordination of national statistical systems is a concern. Challenges: cross-country survey and in-depth country studies
- 19. National Statistics Offices (NSOs) Center stage Fit for the purpose? Cross-country survey and in-depth country studies Going forward Risks in execution without (well) capacitated NSOs. http://demonstrations.wolfram.com/TheMysticRose/
- 20. NSO processes 20 Context Country-led data rev NSO Processes Focusing on dissemination Data Portals Operating Environment Emerging Themes Conclusion
- 21. GSBPM* Lot going on Specify needs Design Build Collect Process Analyse Disseminate Evaluate Feedback *Generic Statistical Business Process Model
- 22. Lot going on Cross-country survey and in-depth country studies Specify needs Design Build Collect Process Analyse Disseminate Evaluate Study focus: Dissemination Feedback GSBPM
- 23. Lot going on Cross-country survey and in-depth country studies Study focus: Dissemination High visibility from outside Signs of the preceding processes Much technology infusion GSBPM
- 24. Focusing on dissemination 24 Context Country-led data rev NSO Processes Focusing on dissemination Data Portals Operating Environment Emerging Themes Conclusion
- 25. Going digital
- 26. Going digital Centralized Digital Distributed Non-digital Delivery Form The shift From print (non-digital and centralised) to digital (digital and distributed)
- 27. Going digital The shift Crowded space at the NSOs Typical user interfaces NSO website NADA Redatam/IMIS Dataportal (Prognoz) OpenDataForAfrica (Knoema) DevInfo CensusInfo CountryStat SDMX Registry SMS/Mobile Social Media Dataplatforms
- 28. Going digital The shift Data dissemination is prejudicially becoming ‘IT department’ centric Influence on NSO structure Putting pressure to acquire new skills and more staff; traditional roles are changing at the NSOsTypical user interfaces
- 29. Data portals 29 Context Country-led data rev NSO Processes Focusing on dissemination Data Portals Operating Environment Emerging Themes Conclusion
- 30. Data portals in NSOs Data Portals Data demands Internet Machinereadability/Dbdriven DataentrybyNSOs Positioning
- 31. Data portals in NSOs Positioning Definition Data portals are NSO specific adaptations of generic but distinct web-based interactive data (and metadata) platforms - directly populated by the NSOs. • Data portals vs data platforms • Tanzania NSO specific: TNADA; Generic: NADA Specificity of data platforms for statistical data and conceptual modeling of statistical data (‘cube’) Distinct from ckan, dkan and Socarata etc. • Data portals vs data platforms • E.g. Tanzania NSO specific: TNADA; Generic: NADA
- 32. Data portals in NSOs Positioning Definition Drawing attention UNECA hand book Link http://documents.worldbank.org/curated/en/2014/07/20467305/open-data-challenges-opportunities-national-statistical-offices http://documents.worldbank.org/curated/en/2014/10/20451797/technical-assessment-open-data-platforms-national-statistical-organisations
- 33. Operating environment 33 Context Country-led data rev NSO Processes Focusing on dissemination Data Portals Operating Environment Emerging Themes Conclusion
- 34. Insights and challenges Capacity (skills and numbers of personnel) Infrastructure (hardware and connectivity) Large numbers of deployments Role of development partners
- 35. Insights and challenges Viability, feasibility, usability and desirability Business process alignment Context oblivious deployments Large numbers of deployments
- 36. Insights and challenges Multiplicity of data portals in same data types in same NSOs Data types and platforms Redundancy Context oblivious deployments Large numbers of deployments Delivery Micro data Aggregate data Geo-spatial data Geo-spatial data NADA IMIS DevInfo Prognoz AGOL* *ArcGISOnline
- 37. Insights and challenges Search functionality NSO website (look and feel + navigation) Terms of use and copyrights Redundancy Lack of design integration Context oblivious deployments Large numbers of deployments
- 38. Insights and challenges Delayed data wrt print (and other platforms) Few and selected data Inconsistent data across platforms and medium Redundancy Lack of design integration Context oblivious deployment Manual data entry as a result of tech limits Large numbers of deployments
- 39. Insights and challenges Redundancy Lack of design integration Context oblivious deployment Manual data entry as a result of tech limits Large numbers of deployments Ineffectively facilitated consumption Inadequate mechanisms of data sharing with national open data portals - which are gradually becoming windows of national data. Distinct reporting to international institutions continues; burden not eased by the data portals
- 40. Emerging themes 40 Context Country-led data rev NSO Processes Focusing on dissemination Data Portals Operating Environment Emerging Themes Conclusion
- 41. Themes Governance Stakeholder's roles range & decision making process and criteria (preventing chaos) NSO People Feasibility (Can we do this?) https://dschool.stanford.edu/our-point-of-view/
- 42. Themes Alignment Governance Making ‘business analysis’ part of the deployment approach for IT in general; may lead to process adaptations too! Processes Systems
- 43. Themes Alignment Governance Implementation keeping the needs of both the data managers (at the NSOs) and end-users Users centered design User experience https://dschool.stanford.edu/our-point-of-view/
- 44. Themes Alignment Governance Linking production system to the data portals through automated data-entry & OD sharing Users centered design Automation http://rajivranjan.org/what-will-it-take-to-improve-statistical-data-dissemination-in-the-digital-realm/
- 45. Conclusion 45 Context Country-led data rev NSO Processes Focusing on dissemination Data Portals Operating Environment Emerging Themes Conclusion
- 46. Transformation roadmap Building on ‘better’ practices
- 47. SDMX -Statistical Data and Metadata eXchange Examples UNSD-DfID supported project Used SDMX standards for data sharing Used SDMX Registry for automation http://data.un.org/countryData/ Transformation roadmap Building on ‘better’ practices http://rajivranjan.org/disseminating-aggregate-data-and-associated-metadata-using-sdmx/
- 48. API - Application Program Interface Examples UNICEF using DevInfo API for dataviz Positive impact on data quality User enabled interface http://dashboards.devinfo.org/ Transformation roadmap Building on ‘better’ practices
- 49. DDI-CKAN bridge module Examples World Bank initiative Bridging the Open Data divide Import data in DDI format directly into a CKAN http://ckan-ddi.clients.liip.ch/dataset Transformation roadmap Building on ‘better’ practices
- 50. Transformation roadmap Building on ‘better’ practices Conscious of information structure Distinct considerations for different data types Data workflow at NSOs driven by data types Modular (but interoperable) solutions are the way forward
- 51. Transformation roadmap Building on ‘better’ practices Data portals maturity model Conscious of information structure
- 52. Datadisseminationefficiency Time Fragmented Interoperable Integrated User-focused User-enabled Automation Transformation Data portals maturity model
- 53. Transformation roadmap Building on ‘better’ practices Data portals maturity model Conscious of information structure A complex system that works, is invariably found to have evolved from a simple system that worked - John Gall A complex system?
- 54. Thank you!