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INSTITUTO NACIONAL DE ESTATÍSTICA

AN INFORMATION ARCHITECTURE MODEL FOR THE NATIONAL STATISTICAL SYSTEM OF MOZAMBIQUE

Tomás BernardoNational Institute of Statistics (INE) – Mozambique

June, 2012



OUTLINE

•INTRODUCTION•INFORMATION ARCHITECTURE – A PROPOSED MODEL•ISSUES•CHALLENGES•WAY FORWARD•FINAL REMARKS

An information architecture model for the national statistical system of Mozambique



INTRODUCTION


•INE created by a presidential decree 1996•Executive body of the SEN (NSS)•Independent body reporting to the Council of Ministers•In charge of data collection and dissemination of country oficial stats

•Central Bank responsible for monetary stats and balance of payments•Law alloes INE to delegate authority to government ministries for data collection under stated conditions•SEN organs

•High Council on Statistics (chaired by the Prime Minister)•Population and Housing Census Council•Representatives from line ministries•President of INE•Representatives from Central Bank, private sector and Academia)



INTRODUCTION


•INFORMATION ARCHITECTURE – DEFINITION“The art and science of organizing information to help people effectively fulfill their information needs” (Kat Hagedorn, 2000: The Information Architecture Glossary).

Information architecture involves • investigation• analysis and • implementation.




STATISTICAL INFORMATION SYSTEM

OF A NSO

Taken by Bo Sundgren, 1995.(Statistical information systemsin a modern Society: Roles, functions, and system designs




PROPOSED INFORMATION ARCHITECTURE

Publications

Documentation database Classification database

DATAWAREHOUSE

General databases(e.g. ESDEM/DevInfo)

INTERNET DATABASE

COMMON DOCUMENTATION

Documentation accessible to all staff at INE. Part of the documentation accessible from the Internet

INE

ODINE

OUTPUT DATABASE

Statistical areas

Adapted from Lars Thygesen




• The common access to non confidential data is ensured by building a data warehouse containing data to be shared across subject matter areas.

• The storing of documentation and classifications in common databases will contribute to coordination among various producers of the production of statistics. The standards for documentation and storing of documentation in common databases also make it easy to publish documentation on the Internet.

PROPOSED INFORMATION

ARCHITECTURE (cont.)




• The central data storage is crucial for the integration of Internet related procedures at INE. If implemented before dissemination it gives the opportunity for implementing common computer aided approval procedures for Internet data (Content management systems).

PROPOSED INFORMATION

ARCHITECTURE (cont.)



ISSUES


• No data and metadata sharing systems in place

• No standards for data management including consistency checks and imputation rules.

• Very limited use of administrative data and available data may not be stored electronically.

• No data center where all available data belonging to the SEN could be stored is available.

• Report burden on INE and SEN




• Small number of IT personnel, yet highly in need of computer skills

• Heterogeneous level of expertise among producers of statistics as well as availability of resources, which makes difficult to solve quality issues such as under coverage, missing values as well as discrepancies among datasets.

ISSUES (cont.)



CHALLENGES

• Database technology can be challenging because of capacity limitations and concurrent on-line and real time access.• Integration of databases made difficult by the multiplicity of database systems (Devinfo, PcAxis, CountryStat, etc).• Revision of the statistical legislation cannot be seen only as a legal act.• Adoption of standards, classifications and best practices is not an easy task due to harmonization issues at various levels (national, regional and international).




WAY FORWARD


• Speed-up data processing and reduce delays in dissemination and providing more disaggregated and geo-referenced data.

• The centralization of all data and metadata (including classifications and methods) in a central repository and make it available on the Web.




• The adoption of standards and take full advantage of advances in information technologies and standards.

• The harmonization (or “integration”) of information systems, complying with the SDMX standard.

• The development of a data recovery plan (“disaster management”) to guarantee the preservation of all digital information.

WAY FORWARD (cont.)




WAY FORWARD (cont.)

Adoption of the GSBMP to ensure data quality




WAY FORWARD (cont.)

The GSBMP can be used for:• Harmonizing statistical computing

architectures • Facilitating the sharing of statistical software • Providing a basis for explaining the use of DDI

and SDMX within SEN. • Providing a framework for process quality

assessment and improvement • Better integrating work on statistical

metadata and quality




• Providing a structure for storage of documents

• Providing a framework to assess the knowledge and capability that already exists and to identify the gaps that need to be filled to improve operational efficiency.

• Providing an input to high-level corporate work planning.

WAY FORWARD (cont.)



• Some progress has been made for the implementation of the proposed architecture. These include:• Revision of the statistical legislation• Approval and ratification of the African

Charter on Statisitcs• Decentralization of statistical

production to district level• Adoption of UN classifications (e.g.

economic activities, products, etc)


FINAL REMARKS



• Implementation of a National Data Archive (MOZDATA) is in progress

• Survey documentation is being carried out by the use of data documentation standards (Microdata Management Toolkit).

• The launch of a new website based on Business Intelligence (sponsored by AfDB).

• Intention to adhere to the SDMX• Strategies to tackle the described issues

and other are included in the next Strategic Plan (2013 – 2017).


FINAL REMARKS



Obrigado

Thank you


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