Based on an analysis of several data portals in a variety of countries, this presentation offers lessons on how the deployment, design and technology considerations can be improved as we enter the implementation phase of the Sustainable Development Goals – which will lead to a further push for data portals.

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

3. Context
3
Context
Country-led
data rev
NSO
Processes
Focusing on
dissemination
Data
Portals
Operating
Environment
Emerging
Themes Conclusion

4. Demand for data

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/

10. Sustainable Development Goals

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!