1. Expected Outputs & Lessons Learned
Léa Hakim & Neda Jafar
Statistics Coordination Unit, ESCWA
Workshop on Building Country Capacity to Maintain a Central Repository of Data Amman March 2007

2. 2. Action plan for clean-up
Expected Outputs
By the end of today’s session:
1. Preliminary Report
2. Action plan for clean-up

3. 2. Action plan for clean-up
Expected Outputs
1. Preliminary Report
2. Action plan for clean-up

4. Preliminary Report
Consolidation of working session outputs as per templates shared:
Status:
Database Management
DevInfo as a Central Repository of Data (CRD)
Data coverage
Linking producers and users of data
Dissemination

5. Preliminary Report Structure and Content Review Summary:
DevInfo summary report (Excel sheets)
2. MDG indicator I-U-S analysis (template provided)

6. 1. DevInfo summary report: Categories (Current – Proposed)
Indicator
Unit
Subgroup
I-U-S
Time period
Area
Sector
Goal
Framework
Institution
Theme
Convention
Source
Specify Indicator Classification(s) employed

7. 1. DevInfo summary report: Review of subgroups
Source: Report on DevInfo 2004 Technical Support for UNDG Project “Building Capactiy and Statistical Literacy for MDG Monitoring”, Community Systems Foundation p.12

8. 1. DevInfo summary report: Review of sources
Source: Report on DevInfo 2004 Technical Support for UNDG Project “Building Capactiy and Statistical Literacy for MDG Monitoring”, Community Systems Foundation p.9

9. 2. MDG Monitoring Indicator I-U-S Analysis
Source: Report on DevInfo 2004 Technical Support for UNDG Project “Building Capactiy and Statistical Literacy for MDG Monitoring”, Community Systems Foundation p.19

10. 2. MDG Monitoring Indicator I-U-S Analysis
Complete template in reviewing data content
Customize area categories
Include review of whether changes implemented for final report [ Column “Action Taken (Y, N)” in template shared]
Note: Electronic I-U-S Analysis template distributed.

11. Preliminary Report Conclusions for next steps
Outline decisions taken for next steps during this training
Ex. Focus on MDG indicators only for first launch
Ex. Establishing area codes at sub-national level
Ex. Adding proxies/national development indicators

12. Final Report Outline Introduction Objectives Status of CRD
Structure and Content Review
Conclusions
Challenges in clean-up
Lessons learned
Evaluation report (M&E UNICEF)
Annexes
Include Action Plan Timetable
Preliminary Report
Note: Electronic report template distributed.

13. 2. Action plan for clean-up
Expected Outputs
1. Preliminary Report
2. Action plan for clean-up

14. Beyond CRD Training Complete database (DB) Review
Complete DevInfo DB cleanup
UNICEF M&E officer evaluation
Submission of final report
Use and dissemination of DB
Country Roll-out

15. 2. Action plan Main Task Task Components Person(s) responsible March
April
I. Full conversion to DevInfo v5.0
II. Completion of DB review
III. Implementation of cleanup
IV. Evaluation
V. Submission of report
VI. Launch of national DevInfo DB
Note: Detailed electronic action plan template distributed.

16. Lessons Learned

17. Strategy and Planning Exchange of experiences
Should invest in preparation prior to release:
Setup (complete DevInfo team w/ clear functions & responsibilities)
Necessity of Technical/Steering Committee
Establish (feedback between DevInfo team, statistics producers, UN agencies).
Clear strategy and plan of action
Emphasis on quality
Prioritize MDG DB
Raise awareness of decision-makers to make use of available statistics

18. Database Management Start with planning process
Start small, think BIG!
Importance of establishing:
DevInfo team
Committees: Steering and Technical
Formal agreements with line ministries
Involve users from beginning
DB management requires experts for assessing data and metadata

19. DevInfo as CRD DevInfo is user-friendly as a program and interface
Web-enabling option
Institutional link between DBs and DevInfo
Customized national DevInfo
Use of GIS
Mapping facility
Data organized by themes for targeted policy-making
Store other activities’ data such as surveys & censuses
Ready-tailored metadata and indicators of MDG framework
Availability of international sources of data

20. Data coverage Sex disaggregated data Small area disaggregated data
Time series
Different sources
Strengths and weaknesses of DB
Establish processes for data collection
Review of list of indicators in national database relative to global MDG list
Addition of country-specific development indicators
Review of meta-data on indicators, sources
Review of geographical areas/maps

21. Producer – User dialogue
Establish linkages with line ministries
Regular forums
Unification of standards and methods
Unification of classifications and definitions
Data-entry by line ministries through web
Train users to understand statistics, indicators, and analysis of data

22. Dissemination Press releases Web-enabling
Dissemination through workshops
Addition of DevInfo links to NSO sites (including training material)
Provision of DevInfo CD with national MDGR
Knowledge transfer
Roll-out advocacy campaign

23. Structure & Content Spell Check Thematic databases
Always refer to global DB structures - Import MDG indicators and goals from global DB – do not type.
Planning of DB content is essential
Continuous check of data quality
Metadata
Follow standard naming of source
Document all changes made
Assessment of data availability has to be done BEFORE creating a template
Source of data should specify the original producer ex. MOH for health indicators
Naming of source should follow “PAL_MOH_DHS_2003”
Set up different DBs for big amounts of info ex.census & surveys
Remove indicators with no data values to avoid over reporting

24. Mapping Continent and country level: apply ISO coding
Ensure each area ID is connected to area name and vice versa
Start working towards a target for infrastructure maps
Area ID is the key to the mapping

25. Data Quality During data entry preferable to insert value not formula
Review data and consistency – do not rely on automated ways
Do not include indicators with missing values
Avoid duplication
Ensure completeness of data values and time series

26. Thank you