1 / 24

Data Management and Dissemination Tools and Systems

Data Management and Dissemination Tools and Systems. UNECE Training Workshop on Dissemination of MDG Indicators and Statistical Information Astana, Kazakhstan 23 – 25 November 2009 Steven Vale, UNECE. Contents. Data management in the statistical production process

raap
Download Presentation

Data Management and Dissemination Tools and Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Management and Dissemination Tools and Systems UNECE Training Workshop on Dissemination ofMDG Indicators and Statistical Information Astana, Kazakhstan 23 – 25 November 2009 Steven Vale, UNECE

  2. Contents • Data management in the statistical production process • How are data currently disseminated? • Advantages and disadvantages of different approaches • Good practices

  3. Statistical Production Process • Modelling production processes in statistical organisations started at least 10 years ago • “Statistical value chain” • “Survey life-cycle” • “Statistical process cycle” Generic Statistical Business Process Model Developed by the UNECE Group on Statistical Metadata

  4. Structure of the Model (1) Process Phases Sub-processes (Descriptions)

  5. Structure of the Model (2) • National implementations may need additional levels • Over-arching processes • Quality management • Metadata management • Statistical framework management • Statistical programme management • ........

  6. Why do we Need a Model? To define and describe statistical processes in a coherent way To standardize process terminology To compare / benchmark processes within and between organisations To facilitate statistical software sharing To help manage process quality

  7. Standardized process descriptions Harmonised processes Rationalization of software Use of open source and shared components SDMX between components Convergence of business architectures

  8. Key features Not a linear model Sub-processes do not have to be followed in a strict order It is a matrix, through which there are many possible paths, including iterative loops within and between phases Some iterations of a regular process may skip certain sub-processes

  9. Applicability (1) • All activities undertaken by producers of official statistics which result in data outputs • National and international statistical organisations • Independent of data source, can be used for: • Surveys / censuses • Administrative sources / register-based statistics • Mixed sources

  10. Applicability (2) Producing statistics from raw data(micro or macro-data) Revision of existing data / re-calculation of time-series Development and maintenance of statistical registers

  11. Dissemination Practices • Web sites of statistical agencies for all 56 UNECE member countries checked during spring 2008. • Data dissemination systems and formats recorded. • Not possible to check all national language versions of websites.

  12. Results

  13. Static html / pdf / word Pages

  14. Static html / pdf / word Pages • Advantages • Quick, easy and cheap to prepare • Data at a glance • Possible to combine tables, graphics and text • Html and pdf viewers are free • Disadvantages • Only a picture - users can not easily download or manipulate data • Manual updates

  15. Excel Spreadsheets

  16. Excel Spreadsheets • Advantages • Users can download and customize data • Most common format for basic data analysis • Disadvantages • Excel software is not cheap! • Manual updates • User has to download the whole file

  17. Output Databases

  18. Output Databases • Advantages • Interactive with flexible outputs • User friendly (usually!) • Can be tailored to national requirements • Some generic systems available • Disadvantages • Can be expensive to develop and maintain, particularly if you develop your own system

  19. What Do Users Want? • Depends on the type of user • Quick access to key figures • Options to select and manipulate data • Easy export to own analysis packages • Graphic visualizations (maps, charts, ..) • Appropriate metadata • Multiple languages

  20. How Do We Know This? • Ask the users! • User surveys (more about this tomorrow) • User feedback forms • User forums • …

  21. Good Practices • Static tables can be useful for key figures • For detailed or large datasets, allow users to create and manipulate their own tables • Store data as multi-dimensional cubes • Offer graphic visualizations • Allow users to download data in a range of formats (including SDMX)

  22. Good Practices (2) • Link data and metadata • Share development in an open-source environment or network, with an electronic forum for discussions and questions • Don’t try to re-invent the wheel!

  23. Thank you for listeningQuestions?

More Related