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Summary of workshop Workshop on Writing Metadata for Development Indicators Lusaka, Zambia 30 July – 1 August 2012.

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Presentation on theme: "Summary of workshop Workshop on Writing Metadata for Development Indicators Lusaka, Zambia 30 July – 1 August 2012."— Presentation transcript:

1 Summary of workshop Workshop on Writing Metadata for Development Indicators Lusaka, Zambia 30 July – 1 August 2012

2 Workshop objective Build capacity of countries in writing metadata for development indicators with a view to improving data quality and resolve discrepancies between national and international sources



5 Source: International Labour Organization (ILO), Guide to the new Millennium Development Goals Employment Indicators including the full set of Decent Work Indicators, 2009.

6 UNDP Guide to Measuring Human Development “… a reference tool that provides guidance on statistical principles…”

7 Types of metadata

8 Uses of metadata Data discovery Define and describe data resources Drive statistical production Capture information about sources Integral to the IT environment Describe quality of outputs Source : Graeme Oakley, Australian Bureau of Statistics www. unescap. org / stat / apex /2/ APEX 2_ S.4_ conference _ Statistical %20 Metadata %20 Standards. pdf

9 Existing guidance

10 Metadata and development indicators indicators often have multiple data sources users may have limited knowledge of statistics discrepancies in MDG estimates

11 Improving MDG metadata is a global priority ‘ National statistical systems to improve the compilation of metadata in countries and their accessibility by users ’ Recommendation from the International Conference on MDGs Statistics, Manila, October 2011

12 Challenges you face Within the NSO Need a metadata champion Lack of consistent practices Lack of awareness about metadata Capacity gaps in metadata management Limited documentation on business processes Not mandatory to produce metadata Data influenced by operating partners Limited legislation / accountability Reluctance to share knowledge Lack of dissemination policies and systems

13 Challenges you face Outside the NSO Other organizations require training Limited resources to provide guidance Inconsistent standards Practices not harmonized NSO needs to lead in this area / set standards One - off data collections create problems International estimation difference

14 On their own, statistics are just numbers

15 Who are you talking to?

16 Metadata for presentation with data

17 Metadata presented with data Mandatory metadata1. Title describing data being presented 2. Data provider 3. Statistical concepts and definitions Conditional metadata4. Comparability (geographical / over time) 5. Source data 6. Symbols or abbreviations Optional metadata7. Accuracy 8. Contact information 9. References / Relevant links

18 Metadata about an overall data series 1.Name of data series 2.Goal and target addressed 3.Method of computation 4.Definition 5.Rationale 6.Sources and data collection 7.Gender issues 8.Comments and limitations 9.Availability

19 National activities 1. Convince management about need for action 2. Create awareness about importance of metadata 3. Establish a technical working group on metadata 4. Develop metadata framework and strategy 5. Create a metadata - friendly culture 6. Develop standards and produce metadata tools ( e. g. national compendiums, guidelines, templates ) 7. Produce a training manual

20 National activities 8. Train statisticians / other data producers in writing metadata 9. Collect, edit and compile national MDG - related metadata from across the NSS 10. Disseminate metadata ( hard and soft copy ) 11. User - producer workshops to get feedback 12. Create a data quality assessment framework 13. Ensure AU Handbook is used by the NSS

21 Regional activities 1. Campaign for action / convince heads of NSOs 2. Agree on a minimum standard for writing metadata 3. Translate metadata guide into other languages 4. In - depth training on SDMX ( train the trainer ) 5. Further training on metadata 6. Collate national metadata at the regional level 7. Create technical committee to review metadata and provide feedback to NSOs 8. Compile and publish regional metadata

22 Regional activities 9. Needs assessment to identify gaps in statistical capacity 10. Standard set of indicators, AU handbook and legislation 11. Build adequate capacity in implementing a quality framework statistics

23 Expectations of the workshop Better understanding / knowledge produce and disseminate better metadata Tools to apply what we learn Develop skills of other data producers Get managerial support : resources and will Guidance on how to customize MDG metadata to reflect national practices How to build on what NSOs have already done

24 Expectations of the workshop SDMX : what is it and how to implement ? Standard format for writing metadata How to narrow the gap between different data sources ? How to build a data warehouse ? Resolve international and national discrepancies

25 What are metadata ? Provide a short definition. Answer : metadata are data that defines or describes other data.

26 What metadata would you expect to be included with data presented in a table ? Answer : a clear title, column / row labels, footnotes, data provider, source of the data…

27 Why is metadata particularly important when reporting on development indicators, such as the MDGs ? Answer : indicators often have multiple data sources users of MDG reports may have limited experience in interpreting statistics differences in MDG estimates

28 What benefits can be gained by improving the way national statistical offices produce and manage metadata ?

29 Benefits of managing metadata Use up - to - date classifications and definitions Gain resources Increase morale and productivity Capitalising on lessons learned Make it available to users Easier for data users to understand Increased trust in official statistics

30 What are some tips / advice for writing good metadata ?

31 Answers include : Be aware of the target audience Use clear and simple language Keep sentences and paragraphs short Avoid technical terms, jargon and acronyms Ask colleagues to review the data and metadata – do they make sense ? Use a standard glossary of terms for consistency Develop publication or style manuals for how data / metadata are presented

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