1、Enhancing Data Management Through the Statistical Data and Metadata eXchange StandardA Special Supplement of the Key Indicators for Asia and the Pacific 2024In todays data-driven world,the efficient exchange,analysis,and dissemination of statistical data is vital for informed decision-making and pol

2、icy formulation across both government and the private sector.However,the entry of new data producers and a surge in available data poses challenges for traditional statistical entities,compelling them to innovate while maintaining data quality.In response,the Statistical Data and Metadata eXchange(

3、SDMX)standard has emerged as a solution,offering a comprehensive framework to streamline data activities and promote interoperability.This report explains the benefits of adopting the SDMX standard in developing economies to enhance efficiency in data collection,production,and exchange.About the Asi

4、an Development BankADB is committed to achieving a prosperous,inclusive,resilient,and sustainable Asia and the Pacific,while sustaining its efforts to eradicate extreme poverty.Established in 1966,it is owned by 68 members 49 from the region.Its main instruments for helping its developing member cou

5、ntries are policy dialogue,loans,equity investments,guarantees,grants,and technical assistance.ENHANCING DATA MANAGEMENT THROUGH THE STATISTICAL DATA AND METADATA EXCHANGE STANDARDA SPECIAL SUPPLEMENT OF THE KEY INDICATORSFOR ASIA AND THE PACIFIC 2024AUGUST 2024ASIAN DEVELOPMENT BANKASIAN DEVELOPMEN

6、T BANK6 ADB Avenue,Mandaluyong City1550 Metro Manila,Philippineswww.adb.orgASIAN DEVELOPMENT BANKENHANCING DATA MANAGEMENT THROUGH THE STATISTICAL DATA AND METADATA EXCHANGE STANDARDA SPECIAL SUPPLEMENT OF THE KEY INDICATORSFOR ASIA AND THE PACIFIC 2024AUGUST 2024Creative Commons Attribution 3.0 IGO

7、 license(CC BY 3.0 IGO)2024 Asian Development Bank6 ADB Avenue,Mandaluyong City,1550 Metro Manila,PhilippinesTel+63 2 8632 4444;Fax+63 2 8636 2444www.adb.orgSome rights reserved.Published in 2024.ISBN 978-92-9270-815-3(print);978-92-9270-816-0(PDF);978-92-9270-817-7(ebook)Publication Stock No.FLS240

8、373-2DOI:http:/dx.doi.org/10.22617/FLS240373-2The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank(ADB)or its Board of Governors or the governments they represent.ADB does not guarantee the accuracy of the

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14、corrigenda.Note:In this publication,“$”refers to United States dollars.ADB recognizes the Kingdom of Bhutan as Bhutan,the Republic of Maldives as Maldives,the Independent State of Samoa as Samoa,and the Kingdom of Thailand as Thailand.Cover design by Claudette Rodrigo.Printed on recycled paperiiiCON

15、TENTSTables,Figures,and Boxes vForeword viiAbbreviations ixHighlights x1 Introduction 11.1 Origins and Evolution of Statistical Data and Metadata eXchange.11.2 Structure,Applications,and Benefits of Statistical Data and Metadata eXchange.21.3 Ongoing Adoption and Uptake of Statistical Data and Metad

16、ata eXchange.42 Core Uses of Statistical Data and Metadata eXchange 52.1 Data Collection.52.2 Data Validation and Statistics Production.52.3 Data Reporting.52.4 Data Dissemination.62.5 The Role of Application Programming in Data Dissemination.62.6 Leading Warehouses in High-Tech Data Retrieval.73 Ke

17、y Benefits of Statistical Data and Metadata eXchange 103.1 Improved Data Quality and Timeliness.103.2 Enhanced Data Consistency and Comparability.113.3 Minimized Burden of Data Reporting.113.4 Availability of Free and Open-Source Tools.123.5 Affordable Implementation and Easier Access to Data.123.6

18、Streamlined Integration and Improved Statistical Practices.133.7 Stability Through Strong Commitment and Governance.133.8 Support from a Global Community of Statistical Practitioners.144 Issues and Challenges in National Statistical Systems 164.1 Multiple Reporting Organizations.164.2 Different Data

19、 Collection Methods.164.3 Lack of Data and Metadata Harmonization.174.4 Manual Data Handling and Validation.174.5 Unstructured Data.17Contentsiv5 Components of the Statistical Data and Metadata eXchange Framework 185.1 Information Model.185.2 Content-Oriented Guidelines.255.3 Technical Standard.296

20、Steps to Implementing Statistical Data and Metadata eXchange 316.1 Identifying Organizational Priorities and Applications of the Standard.316.2 Developing a Strategy for Organizational Upskilling.316.3 Understanding the Technology Needs Under Implementation.326.4 Other Factors for Consideration.327

21、Important Applications of Statistical Data and Metadata eXchange 347.1 National Summary Data Page.347.2 Data Portal.358 ADB Economy Assessment Survey 438.1 Overview of the Survey.438.2 Results of the ADB Survey.438.3 Key Takeaways of the Survey.479 Electronic Courses Supporting Statistical Data and

22、Metadata eXchange 499.1 Foundation Course.499.2 Tools Course.5510 Conclusion 64References 66vTABLES,FIGURES,AND BOXESTABLES1 Summary of the Economic and Financial Statistics Data Structure Definition.202 Sample of Cross-Domain Codelists.273 Interest in Using Statistical Data and Metadata eXchange,by

23、 Subregion .444 Implementation Status of Statistical Data and Metadata eXchange,by Statistical Domain.445 Interest in Using Statistical Data and Metadata eXchange,by Type of Usage.456 Structure of the Foundation Course .507 Completion Rate of the Foundation Course.518 Age Distribution of Foundation

24、Course Graduates.529 Affiliation or Organization of Foundation Course Graduates.5310 Job Classification of Foundation Course Graduates.5311 Graduate Evaluation of the Foundation Course.5412 Structure of the Tools Course .5613 Completion Rate of the Tools Course.5814 Age Distribution of Tools Course

25、Graduates.5915 Affiliation or Organization of Tools Course Graduates.6016 Job Classification of Tools Course Graduates.6017 Graduate Evaluation of the Tools Course.61FIGURES1 Key Milestones for Statistical Data and Metadata eXchange.32 Data Reporting Workflow Using Statistical Data and Metadata eXch

26、ange.63 Data Retrieval via UNICEF Indicator Data Warehouse.74 Main Portal of Thailands Statistics Sharing Hub.85 Application Programming Interface of Thailands Statistics Sharing Hub.96 Governance Structure for the Statistical Data and Metadata eXchange Initiative.147 Components of Data Structure De

27、finition .198 The Four Quadrants of Structural Data Modeling .219 Sample Message Using Extensible Markup Language.2310 Sample Message Using JavaScript Object Notation.2411 Sample Message Using Comma-Separated Values.2512 Example of Cross-Domain Concepts.2613 Cross-Domain Concept in the Glossary.2914

28、 National Summary Data Page for Bhutan.3515 Perceived Benefits of Statistical Data and Metadata eXchange.4616 Perceived Challenges of Implementing Statistical Data and Metadata eXchange.4717 Timeline of Developing the Foundation e-Learning Course.51Tables,Figures,and Boxesvi18 Origins of Foundation

29、Course Graduates.5219 Timeline of Developing the Tools e-Learning Course.5720 Gender of Tools Course Graduates.5821 Origins of Tools Course Graduates.5922 Completion Rate of Tools Course,by Activity.62BOXES1 Streamlining Statistics Sharing in Thailand.362 Enhancing Data Dissemination in Maldives.393

30、 Unlocking Data Access Across the Pacific.41viiFOREWORD In todays data-driven world,the ability to efficiently analyze,exchange,and disseminate statistical information is paramount for informed decision-making and effective policy formulation.From government agencies to private enterprises,organizat

31、ions are overwhelmed with vast amounts of data collected from various sources.This abundance of data presents both opportunities and challenges that may need robust mechanisms to manage information effectively.The wealth of available data and the entry of new data producers are combining to place pr

32、essure on official statisticians.To remain relevant,producers of official statistics are increasingly required to innovate and respond to change,without sacrificing data quality or statistical accuracy.For statisticians,innovation must consider more than just methods of data collection and handling:

33、it must also encompass how organizations and the broader statistical system are functioning.In response,the Statistical Data and Metadata eXchange(SDMX)standard has emerged as a means to streamline data activities and facilitate interoperability across the world.Developed and sponsored by eight majo

34、r international organizations,SDMX provides a comprehensive framework for structuring,collecting,producing,exchanging,and managing statistical data and metadata.By doing so,SDMX enables seamless integration and sharing of data across different systems and domains.The Asian Development Bank(ADB),thro

35、ugh the Data Division of the Economic Research and Development Impact Department,has positioned itself to play a key role in the adoption of SDMX in its developing member countries(DMCs)across Asia and the Pacific.ADB is committed to supporting the ongoing development of the national statistical sys

36、tems within DMCs,and has consistently advocated for high-quality statistical information using modern technology,innovative data,advanced methods,and best practices.In relation to SDMX,we have enhanced the capacity of DMCs and conducted SDMX training for staff from national statistics offices and ot

37、her data-producing agencies.This includes the creation(in collaboration with development partners)of e-learning courses on SDMX foundations and key SDMX tools.More than 600 participants from over 65 countries around the world have successfully completed these online training courses.This years speci

38、al supplement to Key Indicators for Asia and the Pacific highlights the advantages of adopting the SDMX standard,particularly in developing economies.It presents the results of an ADB technical assistance project that was launched in 2018 to promote the adoption of the SDMX standard in our region.Un

39、der the project,and in collaboration with development partners,ADB has supported the National Statistical Office of Thailand to implement SDMX within their decentralized reporting system.The offices staff were trained in SDMX concepts,data modeling using globally defined data structures,and customiz

40、ing data structures according to national requirements.Foreword viiiThis special supplement is a product of close collaboration among a team of experts drawn from a variety of disciplines.The supplement team was led by Stefan Schipper,under the overall guidance of Elaine S.Tan.Stefan was joined by B

41、rian Buffett and Jeffrey Napoles as coauthors of the report,while significant contributions in research and technical support were made by Pamela Lapitan and Thomas JohnBallatore.The supplement team extends its appreciation to the participating national statistics offices and other data-producing ag

42、encies,with special recognition to the National Statistical Office of Thailand,the Samoa Bureau of Statistics,and the National Statistics Bureau of Bhutan.Their invaluable contributions have been instrumental in driving the implementation of SDMX and strengthening the capabilities of statistical sys

43、tems in other DMCs.The team would also like to acknowledge Denis Grofils from the Pacific Community for his invaluable insights and experiences regarding SDMX implementation,as well as Dayyan Shayani from the United Nations Economic and Social Commission for Asia and the Pacific for his input on SDM

44、X support in Maldives.Additionally,the team is grateful to Abdulla Gozalov,Hernan Hernandez Martinez,and Markie Muryawan of the United Nations Statistics Division,as well as Pinar Ucar from the United Nations Statistical Institute for Asia and the Pacific,for their invaluable contributions and suppo

45、rt for the SDMX e-Learning courses.We hope this publication can play a role in promoting the adoption of SDMX throughout Asia and the Pacific,serving as a catalyst for standardization and modernization in the management and exchange of statistical data and metadataleading ultimately to more accurate

46、 policy development and more equitable allocation of development resources.Albert F.ParkChief Economist and Director GeneralEconomic Research and Development Impact Department Asian Development BankixABBREVIATIONSADB Asian Development BankAPI application programming interfaceCOGs content-oriented gu

47、idelinesCSV comma-separated valuesDMC developing member countryDSD data structure definitione-GDDS Enhanced General Data Dissemination SystemEurostat Statistical Office of the European UnionIMF International Monetary FundISO International Organization for StandardizationJSON JavaScript Object Notati

48、onNSO national statistics officeNSS national statistical systemSDDS Special Data Dissemination Standard SDG Sustainable Development GoalSDMX Statistical Data and Metadata eXchangeSIS-CC Statistical Information System Collaboration CommunitySPC The Pacific CommunityTNSO National Statistical Office of

49、 ThailandUNSC United Nations Statistical CommissionVTL Validation and Transformation LanguageXML Extensible Markup LanguageNote:In this report,the term“economy”is widely used to represent“country”or“nation”.Any use of the terms“country”,“nation”,or“national”is not intended to make any judgment as to

50、 the legal or other status of any territory or area.x Statistical Data and Metadata eXchange(SDMX)is an international standard for improving the efficiency,quality,and accessibility of statistical data.The main objectives of SDMX are to standardize and modernize the collection,processing,and exchang

51、e of statistical data and metadata.These factors can contribute significantly to more informed decision-making and policy formulation at both national and international levels.SDMX is a global initiative sponsored by eight major international organizations:the Bank for International Settlements,the

52、European Central Bank,the International Labour Organization,the International Monetary Fund,the Organisation for Economic Co-operation and Development,the Statistical Office of the European Union(Eurostat),the United Nations,and the World Bank.These sponsor organizations have instituted and maintain

53、ed common SDMX technical and statistical standards and guidelines,and they continue to collaborate to enhance statistical data and metadata management through SDMX.Together,they are working to streamline data and metadata exchange processes,offer capacity development programs,provide information tec

54、hnology infrastructure for efficient data sharing,and explore the need for future evolvement of the standards.SDMX is the preferred standard of the United Nations Statistical Commission(UNSC)for the exchange of data and metadata.In 2008,the UNSC endorsed SDMX as the preferred standard for the exchan

55、ge and sharing of both data and metadata,with members of the UNSC collectively agreeing that SDMX provides a robust and effective solution for standardizing practices related to data and metadata exchange.SDMX is an International Organization for Standardization(ISO)standard(ISO 17369:2013).It is sp

56、ecifically designed to foster interoperable implementations within and between systems,and be applicable to any organization responsible for the collection,processing,and exchange of statistical data and associated metadata.SDMX supports modernization of statistical data management practices by offe

57、ring standardized and interoperable solutions,adopting new technologies,facilitating efficient tool development,encouraging worldwide collaboration,and aligning with contemporary data-sharing initiatives and global development goals.HIGHLIGHTSHighlightsxi SDMX offers numerous benefits for the manage

58、ment of statistical data,including enhanced efficiency,interoperability,and standardization.By providing a common framework for data and metadata exchange and sharing,the SDMX standard facilitates seamless communication between different systems and organizations.This standardized approach also impr

59、oves data quality and timeliness,reduces duplication of effort,and enhances the comparability of statistical information across various domains.Key issues and challenges remain within national statistical systems.Issues and challenges within national statistical systems include obstacles faced in co

60、llecting,integrating,and disseminating statistical data.These include ensuring data quality,addressing resource limitations,navigating legal and ethical considerations,and adopting modern technology.Free,high-quality,open-source software tools are available to facilitate SDMX adoption.There is an ar

61、ray of freely accessible tools available to aid in the implementation of SDMX.These tools serve different purposes within the SDMX framework,such as data modeling,data collection,data conversion,data validation,metadata management,and data dissemination.Economies of Asia and the Pacific emphasize a

62、growing need for SDMX.Survey results from Asian Development Bank(ADB)members across Asia and the Pacific highlight a notable emphasis on adopting and utilizing the SDMX standard,especially for enhancing data and metadata dissemination,as well as improving metadata management and standardizing statis

63、tical business processes.SDMX Implementation in Thailand demonstrates the benefits of data harmonization.The National Statistical Office of Thailand(TNSO)has undertaken a series of initiatives to implement SDMX within their decentralized reporting system.The TNSOs journey began with the development

64、of a data exchange system and progressed to more robust SDMX-based infrastructure,such as the TNSO Statistical Sharing Hub.ADB supports upskilling via SDMX e-learning courses.In collaboration with three development partners,ADB has developed two vital e-learning courses on SDMX.The SDMX foundation c

65、ourse provides an introduction to SDMX and its most important components,such as the information model,content-oriented guidelines,and available SDMX infrastructure and information technology.The SDMX tools course is a follow-up to the foundational course and looks in depth at three tools:the SDMX C

66、onstructor,the Fusion Metadata Registry,and the SDMX Converter.Both e-learning courses have achieved high completion rates and positive ratings.The SDMX foundations course was first conducted from 28 March to 15 April 2022.Its completion rate was 91.9%and its approval rating(graded as good or excell

67、ent by participants)was 94.9%.The SDMX tools course was first conducted from 15 November to 15 December 2023.Given the significantly more demanding nature of the content,the completion rate was still exceptionally high for an online course,at 39.4%.The approval rating was also very high,with 94%of p

68、articipants grading it as good or excellent.1INTRODUCTION1Statistical Data and Metadata eXchange(SDMX)is an international standard established to improve the efficiency,quality,and accessibility of statistical data.The SDMX framework provides guidance on standardizing and modernizing the collection,

69、processing,and exchange of statistical data and metadata(SDMX 2023).SDMX is driven by the need for consistency in interpreting and presenting statistical information.The standard provides a common language and a set of rules for describing,exchanging,and validating data and metadata across different

70、 statistical domains,such as national accounts,balance of payments,and foreign direct investments.As an illustrative example,when datasets are released only in spreadsheet or digital file format,and are not stored in a centralized database,users may struggle to find the specific data they need.Moreo

71、ver,reporting to international organizations also becomes challenging since national statistics offices are required to convert the data into acceptable formats,increasing workload and potential inconsistencies.SDMX addresses these challenges by using standardized structures that ensure consistency

72、across datasets and eliminate the need for multiple format conversions.Additionally,the standard allows for a centralized repository where all data are published,streamlining access for users.This commitment to standardized practices not only streamlines the data exchange process but also enhances t

73、he overall utility and accessibility of statistical information on a global scale.Importantly,the SDMX standard enables the automation and integration of data flows and processes,reducing the resource and cost burden of data reporting and enhancing the timeliness and quality of the data collected.It

74、 also supports the interoperability and accessibility of data,which allows statisticians and relevant stakeholders to easily access,analyze,and apply the data for their own specific purposes.11 Origins and Evolution of Statistical Data and Metadata eXchangeThe SDMX global initiative is sponsored by

75、eight major international organizations:the Bank for International Settlements,the European Central Bank,the International Labour Organization,the International Monetary Fund,the Organisation for Economic Co-operation and Development,the Statistical Office of the European Union(Eurostat),the United

76、Nations,and the World Bank.As well as collaborating on the SDMX framework itself,the sponsor organizations have instituted and maintained common SDMX technical and statistical standards and guidelines.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard2Since its est

77、ablishment in 2001,the SDMX initiative has made remarkable progress,with the collective efforts of the eight sponsor organizations playing a pivotal role in its development.In 2008,the United Nations Statistical Commission(UNSC)officially acknowledged and endorsed SDMX as the preferred standard for

78、the exchange and sharing of both data and metadata(UNSC 2008).This endorsement implies a collective agreement among the members of the UNSC that SDMX provides a robust and effective solution for standardizing practices related to data and metadata exchange.As a preferred standard,SDMX is encouraged

79、for adoption across United Nations agencies and affiliated organizations to ensure a harmonized approach to statistical data management and dissemination(UNSC 2008).In 2013,the International Organization for Standardization(ISO)confirmed SDMX as an official international standard(ISO 17369:2013).Und

80、er the ISO definition,SDMX is specifically applied to foster interoperable implementations within and between systems,and is applicable to any organization with a mandate to manage the reporting,exchange,and dissemination of statistical data and associated metadata.The information model of ISO 17369

81、:2013 was established to support statistics in alignment with the practices of national governments and supranational statistical organizations.However,use of the information model extends beyond these organizations,proving applicable to diverse organizational contexts involving the management of st

82、atistical data and metadata(ISO 2023).Figure 1 provides an overview of the evolution of SDMX,showcasing key milestones achieved through the collaborative efforts of SDMX community practitioners from both the public and private sectors.12 Structure,Applications,and Benefits of Statistical Data and Me

83、tadata eXchangeThere are three main components of the SDMX standard:the information model,content-oriented guidelines,and the technical standard.The information model defines the conceptual framework for structuring statistical data and metadata,ensuring a standardized understanding of information.T

84、he content-oriented guidelines provide procedures and best practices for creating and managing the content of statistical data and metadata within the SDMX framework.For instance,the SDMX list of economy codes can be used universally because each economy is assigned a unique,language-independent cod

85、e,e.g.,“FR”for France,“JP”for Japan,and“US”for the United States.These codes remain consistent regardless of the language used in the dataset.So,whether the dataset is presented in French,Japanese,English,or any other language,the economy codes in the SDMX codelist remain standardized.The technical

86、standard refers to the software architecture definitions that enable the production of computing tools and online data services in accordance with the information model and the content-oriented guidelines.These definitions work together to establish a common language and framework for the exchange o

87、f statistical information(SDMX 2020a).Introduction3One of the standout features of the technical standard for SDMX is the Application Programming Interface(API),which empowers users to efficiently retrieve structured datasets and seamlessly integrate them into statistical systems.Various internation

88、al organizations have collaborated to produce and share SDMX global data structures that can be used for different statistical domains and purposes.Global data structures exist for national accounts,balance of payments,consumer price indices,labor force statistics,Sustainable Development Goal(SDG)in

89、dicators,and more.These data structures are based on common concepts and classifications widely accepted and used by the statistics community.For instance,the SDG data structure includes disaggregation factors such as sex,age,and urbanization as integral components that can be recognized globally.In

90、 the compilation and dissemination of data for the SDG indicators,the means of officially measuring progress toward SDG targets,the SDMX standard plays a key role.A predetermined format and structure for SDG data has been defined in the SDMX standard,which assists in harmonizing,validating,aggregati

91、ng,and disseminating a wide range of SDG datasets across different sources and platforms.Figure 1:Key Milestones for Statistical Data and Metadata eXchangeDSD=data structure definition,ISO=International Organization for Standardization,SDMX=Statistical Data and Metadata eXchange,UN=United Nations,VT

92、L=Validation and Transformation Language.Source:Asian Development Bank visualization using data from SDMX e-learning courses and https:/sdmx.org.2001SDMX Timeline2007200420082011201320152018202120222023Birth oftheSDMXinitiativeReleaseof thefirstversion of SDMXNinth SDMXGlobalConferenceLaunch ofSDMX

93、Toolse-LearningcourseLaunch ofSDMX UserForumLaunch ofSDMXFoundatione-LearningcourseLaunch ofSDMX 3.0SDMXRoadmap2021-2025Preparation ofSDMX version3.0Release of VTLversion 2.0Introduction ofValidation andTransformationLanguage(VTL)version 1.0SDMXpublished asan ISOstandardFirst releaseof globalDSDs fo

94、rNationalAccounts andBalance ofPaymentsSDMXversion2.1 wasreleasedSDMX aspreferredstandard byUNStatisticalCommissionFirst SDMXGlobalConferenceEnhancing Data Management Through the Statistical Data and Metadata eXchange Standard4The use of SDMX global data structures benefits data-producing agencies b

95、y making them part of a larger statistical community.It also benefits data users by providing them with high-quality and consistent datasets that observe common definitions and formats,facilitating clearer interpretations and more accurate application of statistical information.Importantly,using SDM

96、X global data structures also benefits both data producers and users through reduced costs for data collection,processing,analysis,and dissemination.SDMX improves the efficiency and quality of data generation and dissemination by reducing manual work,errors,and inconsistencies and by ensuring compli

97、ance with international standards and definitions.13 Ongoing Adoption and Uptake of Statistical Data and Metadata eXchangeThe eight SDMX sponsor organizations have been collaborating to conduct capacity development programs,training courses,and workshops for national agencies involved in the product

98、ion and exchange of data,aiding these agencies in the modernization of their statistical systems.In addition,the sponsor organizations have leveraged the SDMX technical standard to develop numerous free,open-source software tools to promote more efficient definition,collection,production,exchange,an

99、d management of statistical data and metadata(SDMX 2023).As the SDMX standard has gained traction,other entities such as national statistics offices,central banks,and private firms have developed open-source and commercial SDMX tools and connectors.52CORE USES OF STATISTICAL DATA AND METADATA EXCHAN

100、GEStatistical Data and Metadata eXchange(SDMX)plays a crucial role in standardizing the exchange of information across different stages of the statistical process,including data collection,production,reporting,and dissemination.Use of the standard promotes interoperability and consistency in the way

101、 data is represented,making it more efficient for organizations to share and use readily understood statistical information at national and international levels.21 Data CollectionData collection refers to the process of gathering statistical source information by various means,such as surveys,census

102、es,polls,or administrative records.SDMX standardizes the collection of data by providing a common framework for defining data structures,codes,and classifications.It ensures that collected data conform to a specified format,making it easier to aggregate,compare,and analyze information from diverse s

103、ources.For example,under the SDMX standard,geographic locations or individual economies are each identified by a unique and universally recognized code,such as“PH”for the Philippines and“TH”for Thailand.22 Data Validation and Statistics ProductionStatistics production is where the complex and highly

104、 specialized tasks of validating and aggregating data take place,along with the analysis of high-quality statistics.SDMX supports statistics production with the introduction of metadata-driven processes and tools.In this context,a metadata-driven process is a systematic approach to modeling,structur

105、ing,and organizing the information about data.This benefit increases even further if validation,transformation,and aggregation processes are driven by metadata that identify and describe data.23 Data ReportingData reporting involves the process of submitting statistical data to a central authority o

106、r repository.This could be a national statistics office,an international organization,or any other entity responsible for collecting and managing statistical information.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard6SDMX provides a standardized format for stru

107、cturing and transmitting data,ensuring that data reported by different entities are compatible and can be easily integrated.The SDMX standard specifies how data should be organized.Figure 2 illustrates an SDMX workflow from a national statistics office to an international organization for reporting

108、Sustainable Development Goal(SDG)data.Figure 2:Data Reporting Workflow Using Statistical Data and Metadata eXchangeCSV=comma-separated values,DOC=word document,PDF=portable document format,SDG=Sustainable Development Goal,SDMX=Statistical Data and Metadata eXchange,XLS=Excel file.Source:Asian Develo

109、pment Bank visualization,with logos and icons sourced from https:/www.sdglab.ch and https:/www.un.org/sustainabledevelopment/news/communications-material/.DATA REPORTINGSDG LABSDMX FileNational Statistics OfceData Source FormatsCSVXLSPDFDOC24 Data DisseminationData dissemination involves making stat

110、istical information available to users,including the general public,policymakers,researchers,and other stakeholders.SDMX supports data dissemination by defining standardized structures for the metadata used to describe the content and quality of statistical data,such as their sources,methodologies,a

111、nd quality indicators.This metadata helps users understand the context and characteristics of the data being disseminated.Additionally,SDMX enables the publication of data in a format that is easily accessible and understandable.25 The Role of Application Programming in Data DisseminationUtilizing a

112、n application programming interface(API)is an effective means of disseminating data.An SDMX-API offers a standardized framework of protocols and methods made for efficiently requesting statistical data.By using an SDMX-API,data retrieval processes can be streamlined through automation,empowering app

113、lications and systems to interact with SDMX-enabled data sources.This capability facilitates scheduled,repeatable,and automated tasks for retrieving data.Core Uses of Statistical Data and Metadata eXchange7SDMX-API not only simplifies the process but also enables direct access to statistical databas

114、es and repositories.This supports real-time data updates,allowing applications to retrieve the most recent information directly from the data source.This is particularly important where up-to-date data are critical,such as data used for economic indicators,financial modeling,or other time-sensitive

115、statistical activities.In essence,an SDMX-API serves as a powerful tool to enhance the efficiency and timeliness of data dissemination and retrieval processes.26 Leading Warehouses in High-Tech Data RetrievalSeveral data warehouses around the world are equipped with an SDMX-API for data retrieval.1

116、The Key Indicators Database-ADBs central statistical database housing economic,social,and environmental indicators from across Asia and the Pacific-has integrated the use of SDMX-API for efficient data retrieval and dissemination.The United Nations Childrens Fund(UNICEF)Indicator Data Warehouse has

117、implemented SDMX Fusion Metadata Registry,which serves as a structural metadata repository for a wide range of indicators on health,education,nutrition,child protection,and more(Figure 3).The data warehouse incorporates a user-friendly interface using an SDMX-compliant representational state transfe

118、r(REST)API.It offers an intuitive interface for users to query,download,and export desired data and metadata by either selecting from the options or entering specific terms in the search field.21 Access the Key Indicators Database via https:/kidb.adb.org.2 Access the UNICEF REST Web Service via http

119、s:/sdmx.data.unicef.org/webservice/data.html.Figure 3:Data Retrieval via UNICEF Indicator Data WarehouseREST=representational state transfer,UNICEF=United Nations Childrens Fund.Source:Asian Development Bank screenshot taken from https:/sdmx.data.unicef.org/webservice/data.html.Enhancing Data Manage

120、ment Through the Statistical Data and Metadata eXchange Standard8Figure 4:Main Portal of Thailands Statistics Sharing Hub Source:Asian Development Bank screenshot taken from https:/stathub.nso.go.th/?lc=en&pg=0.The Statistics Sharing Hub maintained by the National Statistical Office of Thailand is a

121、 comprehensive data portal containing a diverse array of international and national indicators(Figure 4).This hub uses the.Stat Suite data dissemination platform developed by the Statistical Information System Collaboration Community(SIS-CC).In addition to making data available via a rich tabular pr

122、esentation tool,one of the key features of the hub is the availability of an API,enabling data portal users to access their desired data in a variety of ways(Figure 5).33 Access the National Statistical Office of Thailand Statistics Sharing Hub via https:/stathub.nso.go.th/?lc=en&pg=0.Core Uses of S

123、tatistical Data and Metadata eXchange9Figure 5:Application Programming Interface of Thailands Statistics Sharing HubSource:Asian Development Bank screenshot taken from https:/stathub.nso.go.th/vis?lc=en&pg=0&fs0=International%20Indicators%2C0%7CSustainable%20Development%20Goals%23SDG%23&fc=Internati

124、onal%20Indicators&bp=true&snb=1&vw=o-v&dfds=ds-stathub-release&dfid=DF_SDG_GLC&dfag=IAEG-SDGs&dfvs=1.16&pd=2015%2C&dq=A._T._T._T._T._T._T._T.&lycl=TIME_PERIOD&lyrw=SERIES&toTIME_PERIOD=false.103KEY BENEFITS OF STATISTICAL DATA AND METADATA EXCHANGEIn this era of information abundance,understanding a

125、nd harnessing the benefits of Statistical Data and Metadata eXchange(SDMX)is crucial for organizations navigating the complexities of data management and for policymakers seeking to make informed decisions based on a foundation of standardized and harmonized statistical data.As the demand for timely

126、,accurate,and harmonized data continues to grow,SDMX provides a robust framework that facilitates the efficient exchange,integration,and dissemination of statistical data.The Business Case for SDMX delves into the multifaceted advantages that SDMX can bring,shaping a scenario whereby data becomes no

127、t just a resource but a powerful tool for informed decision-making and evidence-based policies(SDMX 2020a).Some of the key benefits outlined in the SDMX business case report are detailed below.31 Improved Data Quality and TimelinessAdoption of the SDMX standard facilitates the production of more tim

128、ely and better-quality data.It does so by reducing manual efforts,automating checks and workflows,enhancing accessibility,and minimizing the risk of errors throughout the data-exchange process.SDMX promotes timeliness by minimizing the need for manual conversion of data.The use of automated checks i

129、n SDMX leads to swift validation of data.Automated validation processes can quickly identify errors or inconsistencies,reducing the time needed for manual verification and validation.For example,when reporting on the percentage of women in Parliament,an automated check ensures that the value for the

130、 sex field is correctly set to female and prevents any erroneous entry of male”or“other”.Such automation results in validated data being available more quickly and lets users access this high-quality data in a shorter time span.SDMX also enables automated processing of data,reducing the likelihood o

131、f human errors associated with manual handling.Automated workflows ensure consistency and accuracy in data processing,contributing to better data quality overall.Furthermore,adoption of the SDMX standard triggers increased automation through the implementation of automated workflows for the exchange

132、 of statistics(SDMX 2020a).Significantly,the Validation and Transformation Language(VTL)initiative for SDMX is intended to enhance data quality further.VTL provides standardized instructions for expressing the validation and transformation rules applied to statistical data,improving both accuracy an

133、d consistency in the datasets(SDMX 2020b).The development of tools supporting VTL is ongoing.Key Benefits of Statistical Data and Metadata eXchange1132 Enhanced Data Consistency and ComparabilitySDMXs standardized approach to organizing and exchanging data and metadata enables interoperable function

134、ality within and between systems dedicated to exchanging,reporting,and disseminating statistical information.The result is improved data consistency and comparability across different statistical applications and organizations.The power behind SDMX lies in its information model,which allows for the

135、development of processes and functions around the data model,rather than being constrained by specific data syntaxes or formats.The SDMX information model establishes a common language and structure for expressing statistical concepts to ensure consistency and uniformity in the representation of dat

136、a.SDMX supports a common terminology for describing statistical data,harmonizing concepts and codelists.This strong terminological foundation standardizes data description across various statistical domains.In section 5.2.1,cross-domain concepts are explained in more detail,demonstrating how codelis

137、ts and data description can be applied in more than one statistical domain in a similar form.Harmonizing statistical data content and structure offers numerous advantages,including a shared language among implementers and users.This is achieved through the use of uniform codes,with names and descrip

138、tions that can be expressed in different languages.This approach saves time and resources through reduced mapping and data processing,wider availability of tools based on a commonly agreed format,and the existence of SDMX registries that facilitate reuse.SDMX also enhances interpretability by using

139、a standardized terminology to harmonize structural and reference metadata.This contributes to the development of a global statistical language and improves coherence through cross-domain concepts,shared codelists,harmonized statistical guidelines,and extensive reuse of SDMX objects across domains an

140、d agencies(SDMX 2020a).The adoption of the SDMX standard therefore promotes collaboration,consistency,and comparability in data sharing,underpinning the success of global initiatives to advance statistical data.33 Minimized Burden of Data Reporting The core responsibilities of national statistics of

141、fices(NSOs)include collecting,processing,and disseminating statistical data.Each organization that requests data from,or provides data to,an NSO may have its own unique data format requirements.In these instances,the NSO may find itself having to adapt and transform the same dataset into multiple fo

142、rmats,(e.g.,spreadsheet,text file,digital file)significantly increasing its workload and resource requirements.Adoption of the SDMX standard proves highly beneficial in resolving this scenario.Because SDMX promotes the use of standardized data structures and exchange formats,organizations can align

143、their reporting systems with these standards,reducing the need for maintaining multiple,diverse reporting systems.Standardization across organizations and data domains further minimizes the complexity associated with managing various reporting formats and exchange agreements.This streamlining effect

144、 contributes to a significant reduction in the overall data reporting burden.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard12SDMX ensures data accuracy through prevalidation,automating the reporting process,standardizing data structures,and enabling efficient d

145、ata collection methods.These attributes of the standard enhance the overall efficiency of the data reporting workflow,minimize manual interventions,and lead to a more streamlined and cost-effective reporting process.The SDMX standard allows for the prevalidation of data messages against predefined d

146、ata structures,also known as data structure definitions.This ensures that the data messages are aligned with the structure,reducing errors during data exchange and minimizing the need for manual intervention.As a result,data producers can check the accuracy and adherence of their dataset to the spec

147、ified structure before sending it.By avoiding errors and ensuring compliance at the source,this prevalidation feature minimizes the need for multiple rounds of communication between data producers and consumers to rectify data issues.It streamlines the reporting process and reduces the back-and-fort

148、h exchanges that often occur during data validation(SDMX 2020a).34 Availability of Free and Open-Source ToolsMost of the SDMX information technology(IT)tools developed by various organizations are free and open-source.SDMX follows an open-source approach,meaning that the standards and tools associat

149、ed with SDMX are freely available for anyone to use.This fosters a large community of developers and users who contribute to the improvement and evolution of the SDMX ecosystem.This open-source collaboration not only reduces the cost of acquiring tools but also allows organizations to benefit from a

150、 wide range of SDMX solutions created and maintained by the community.Additionally,the open-source community shares expertise and best practices,providing valuable resources at no cost4(SDMX 2020a).35 Affordable Implementation and Easier Access to DataSDMX is a versatile and accessible framework for

151、 organizations involved in statistical data management.It provides flexibility for customization and promotes cost-efficiencies via access to freely available development tools and codes.These factors go a long way to removing barriers to implementation and data accessibility.The flexibility of the

152、SDMX toolkit approach,which provides a set of standardized tools and resources that organizations can adapt to their specific needs,allows flexibility in the SDMX implementation process.Organizations are able to tailor SDMX implementation based on their unique requirements,infrastructure,and data-pr

153、oduction processes.SDMX implementation can have a wide range of applications.It can resolve specific tasks such as data reporting,as shown in the National Data Summary Page(Section 7.1),or it can be used in comprehensive solutions for the entire data life cycle,exemplified by the Statistical Sharing

154、 Hub developed and maintained by the National Statistical Office of Thailand(Section 7.2).4 Consult sdmx.org(https:/sdmx.org)and sdmx.io(https:/sdmx.io/tools/ecosystem)for more information on available tools.Key Benefits of Statistical Data and Metadata eXchange13Meanwhile,the open-source tools and

155、codes that support SDMX eliminate the need for organizations to invest in proprietary software,often necessary via licensing fees.These open-source solutions contribute to cost-efficiencies,making SDMX more accessible to a broader range of entities,especially those with limited budgets(SDMX 2020a).3

156、6 Streamlined Integration and Improved Statistical PracticesSDMX is designed to be flexible and interoperable with modern technologies.This adaptability allows NSOs and other data-producing agencies to integrate SDMX with contemporary IT infrastructures,databases,and data-processing tools,promoting

157、a more modern and efficient data ecosystem.The adoption of SDMX also contributes to the modernization of statistical data management practices on a worldwide scale.Because the standard is globally endorsed and adopted,it encourages collaboration among international organizations and national economi

158、es and fosters the exchange of modern practices,methodologies,and technologies,creating a shared and regularly updated vision for statistical data management(SDMX 2020a).37 Stability Through Strong Commitment and GovernanceThe SDMX initiative is sponsored by eight major international organizations.T

159、he SDMX sponsors,together with other development partners,collaborate closely with NSOs and other data-producing agencies worldwide.The involvement of these reputable entities lends credibility to SDMX as a global standard.Moreover,the commitment of the sponsor organizations spans more than 20 years

160、,indicating a long-term dedication to the success and continuity of SDMX.This commitment ensures stability and reliability in the implementation and development of the standard(SDMX 2020a).The SDMX initiative also benefits from a well-established governance model,as shown in Figure 6.Key entities wi

161、thin the governance structure include the Sponsors Committee,which makes strategic decisions,and the Secretariat,which is responsible for the operational management of the initiative.This hierarchical structure allows for effective decision-making and coordination among the SDMX sponsor organization

162、s.Meanwhile,the Technical Working Group and the Statistical Working Group operate with a proactive approach that ensures the SDMX standard evolves according to the changing needs of users and remains relevant and adaptable.The initiatives governance model also offers a proven approach for managing t

163、he life cycle of a statistical domains SDMX objects and versions.This structured approach ensures that SDMX evolves systematically and updates are managed efficiently,contributing to the long-term reliability and stability of the standard.Enhancing Data Management Through the Statistical Data and Me

164、tadata eXchange Standard14Figure 6:Governance Structure for the Statistical Data and Metadata eXchange InitiativeBIS=Bank for International Settlements,ECB=European Central Bank,Eurostat=Statistical Office of the European Union,ILO=International Labour Organization,IMF=International Monetary Fund,OE

165、CD=Organisation for Economic Co-operation and Development,SDMX=Statistical Data and Metadata eXchange,UN=United Nations.Source:Asian Development Bank visualization based on https:/sdmx.org/?page_id=2561.SPONSOR ORGANIZATIONSSDMX Sponsors CommitteeSDMX SecretariatInformationModelSDMXGlobalRegistrySDM

166、X OfcialWebsiteTechnical Working GroupStatistical Working GroupBIS,ECB,EUROSTAT,IMF,OECD,UN,WORLD BANK,ILO38 Support from a Global Community of Statistical PractitionersThe adoption of SDMX provides NSOs and other data-producing agencies with access to a global community of individuals and entities

167、actively involved in the development and use of the standard.This opens the way for networking,knowledge exchange,access to wider resources,involvement in standardization efforts,and ongoing professional development.Members of the SDMX community interact frequently to share insights,seek advice,and

168、work together on common challenges related to statistical data management.Key Benefits of Statistical Data and Metadata eXchange15In addition,SDMX users have the opportunity to participate in events such as SDMX global conferences,expert meetings organized by the SDMX sponsors,and workshops and webi

169、nars hosted by various organizations.These events create a platform for learning and networking on first-hand experiences and best practices in statistical management(SDMX 2020a).Another channel for connecting with the SDMX community of practitioners and experts at any time is the online SDMX User F

170、orum,which was launched in 2022.5 5 Access the SDMX User Forum via https:/ more information on SDMX e-learning courses and events,consult the ADB website via https:/adb.org or go to any of the following three providers:https:/sdmx.org,https:/sdmx.io,and https:/academy.siscc.org/.164ISSUES AND CHALLE

171、NGES IN NATIONAL STATISTICAL SYSTEMS Data exchange and dissemination are integral parts of any national statistical system(NSS).Within the NSS,standardized frameworks play a pivotal role in ensuring the reliability,comparability,and coherence of statistical data.While Statistical Data and Metadata e

172、Xchange(SDMX)offers a standardized approach for data exchange and dissemination,its integration into the NSS is sometimes not without challenges.An understanding of these challenges is crucial for statisticians,policymakers,and data practitioners aiming to harness the full potential of SDMX within t

173、he complex environment of an NSS(Ward 2015).41 Multiple Reporting OrganizationsAn NSS often involves multiple data-source organizations,each responsible for collecting and reporting specific sets of data.Each organization may have its own data-collection processes and reporting formats(e.g.,spreadsh

174、eet,text file,digital file),leading to potential inconsistencies and delays in reporting when datasets need to be merged.The diversity of data-collection methods among agencies often creates resistance to and complexity in standardizing these practices,entrenching difficulties faced in consolidating

175、 and harmonizing data from various sources.Moreover,the need to manage and adapt to multiple reporting formats can be a complex and time-consuming task for NSOs and other data-producing agencies.The need to tailor reports to different formats may require additional resources in terms of time,personn

176、el,and technology.42 Different Data Collection MethodsIntegrating diverse data sources is critical for ensuring data quality and comparability.However,transforming data collected through different methods into a cohesive and meaningful dataset can be complex.An NSS may employ different methods of da

177、ta collection,including censuses,surveys,administrative records,and other sources,and variability in these collection methods can lead to inconsistencies and challenges of integrating data seamlessly.Issues and Challenges in National Statistical Systems 17Managing multiple data-collection methods ca

178、n also introduce operational complexity for NSOs and other data-producing agencies.Different collection formats,structures,and methodologies may require distinct skill sets,resources,and tools,making full coordination and oversight challenging and expensive.43 Lack of Data and Metadata Harmonization

179、Harmonizing data and metadata from diverse sources involves aligning concepts,classifications,and structures to ensure consistency.Lack of harmonization can result in difficulties in comparing and aggregating data across different statistical domains.It may hinder the interoperability and consistenc

180、y in the way data are represented and limit the usefulness of statistical information.In addition,the sharing of statistical data becomes more challenging and less efficient.44 Manual Data Handling and ValidationManual data entry,review,and validation processes can be time-consuming and prone to err

181、ors.Manual data entry and/or conversion exposes the possibility of inconsistencies in data interpretation and the chance of transcription errors occurring.Similarly,manual data review and validation processes introduce the risk of overlooking inconsistencies and,being slow by nature,they can impact

182、the timeliness of data dissemination.For instance,when data are sourced from various providers or systems that use different formats(e.g.,spreadsheet,text file,digital file),integrating this data manually becomes complex and heightens the chances of errors and inconsistencies being introduced into t

183、he dataset.Identifying and rectifying errors can be challenging,especially in large datasets,leading to the potential misinterpretation of data.Manual data handling and validation therefore have significant potential to compromise the quality and reliability of statistical information.45 Unstructure

184、d Data Because unstructured data lack a predefined data model or format,it can be difficult to upload such data into a computerized database.When unstructured data need to be integrated into an SDMX-enabled database,the process of converting the unstructured data into a structured format becomes cha

185、llenging.Unstructured data may not adhere to a standardized schema,making it difficult to map the predefined structure expected by the SDMX standard.Moreover,unstructured data may lack clear metadata,requiring additional efforts to define and assign relevant metadata during the mapping process.185CO

186、MPONENTS OF THE STATISTICAL DATA AND METADATA EXCHANGE FRAMEWORKThe Statistical Data and Metadata eXchange(SDMX)standard has three components:a robust information model,content-oriented guidelines,and a detailed technical standard.The SDMX Information Model describes the key concepts around statisti

187、cal data,metadata,and data exchange processes.The model can be used to describe any multidimensional dataset,regardless of domain,and is the area of primary focus for statisticians.The SDMX Content-Oriented Guidelines provide procedures and best practices for creating and managing the content of sta

188、tistical data and metadata within the SDMX framework.The guidelines offer recommended practices that can be implemented consistently,ensuring interoperability and standardization regardless of the specific focus or subject matter of the statistical data.The SDMX Content-Oriented Guidelines further s

189、upport the creation of international good practices and shared standards,such as domain-specific data models and cross-domain codelists(sdmx.io n.d.).The SDMX Technical Standard defines how to create IT tools that fully support the SDMX process throughout the data life cycle.Transforming the SDMX In

190、formation Model and the SDMX Content-Oriented Guidelines into tools and databases that respond to the needs of the entire statistical data lifecycle,in accordance with the SDMX Technical Standard,is the focus of IT experts.51 Information ModelThe SDMX Information Model serves as the central and fund

191、amental component of the SDMX framework.It defines the structure of statistical data and metadata.More specifically,the model harmonizes the representation and exchange of statistical information across different systems and organizations.It also establishes a common language and structure for expre

192、ssing statistical concepts to ensure consistency and uniformity in the representation of data.The model identifies objects within the statistical domain and defines their relationships.This includes essential elements such as core concepts,their roles,and the codelists that enable a clear understand

193、ing of the interconnections between various statistical agencies.Moreover,the model provides a standardized approach to organizing and accessing statistical data.This contributes to interoperability,allows for centralized management,and simplifies the process involved in data exchange across differe

194、nt platforms and systems(Eurostat SDMX Infospace).Components of the Statistical Data and Metadata eXchange Framework195.1.1 Key Elements of the Information ModelThe SDMX Information Model comprises,among others,the following key elements:(i)descriptor concepts(i.e.,concepts associated with the stati

195、stical data)as well as the nature of these concepts(dimension,attribute,or measure);(ii)the packaging structure(i.e.,observation level,series level,dataset level);(iii)the keys(grouping the various dimensions for a particular set of data);and(iv)the codelist(defining the possible values for a dimens

196、ion).As shown in Figure 7,all the information for the key elements is contained in a specific data structure definition(DSD)or“key family”.Figure 7:Components of Data Structure DefinitionSource:Asian Development Bank visualization.DataStructureDefinitionCodelistsKeysGrouping ofdimensionsDimensionsAt

197、tributesMeasuresDescriptorConceptsObservation levelSeries levelDataset levelPackagingStructureThe DSD specifies a set of conceptsalso referred to as“structural metadata”to describe and identify a set of data.A statistical concept in SDMX refers to a unit of thought created by a unique combination of

198、 characteristics(SDMX 2020c).It provides essential information about the data,such as the location or economy represented(e.g.,reference area),the specific time period to which observation refers(e.g.,time period),and the statistical aspect to which the data pertain(e.g.,indicator).Enhancing Data Ma

199、nagement Through the Statistical Data and Metadata eXchange Standard20The concept,codelist,and packaging structure are essential components of any DSD.The keys shown in Figure 7 refer to the combination of dimension values that uniquely identifies an observation or series within a dataset(SDMX 2020c

200、).In the case of a time series,the keys include all dimensions except time period.Every dataset is defined using three concepts from the SDMX Information Model.The first concept is dimension,which is used for defining the data:dimensions are always categorical,meaning most of the dimensions have a c

201、odelist except for time period which follows a structured format such as“YYYY”for yearly data,“YYYY-QQ”for quarterly data,“YYYY-MM”for monthly,and more.6 The second concept is attribute,which is used only for describing additional aspects of the data:common examples of attributes are footnotes and o

202、ther descriptive text.The final concept is measure,which is numerical and represents an actual value.In other words,a DSD is a container for metadata that describe the structure of related datasets in terms of their dimensions,attributes,and measures(sdmx.io n.d.).Additional explanatory information

203、is called“reference metadata”,with this information describing the content,methodology,and quality of the data.In SDMX terminology,the data and metadata structure definitions are made available in the SDMX Global Registry(Tissot 2018).As an example,Table 1 provides summary of the Economic and Financ

204、ial Statistics Data Structure Definition developed and maintained by the International Monetary Fund.6 For example,data for the year 2024 is formatted as YYYY:2024;data for the first quarter of 2024 is formatted as YYYY-QQ:2024-Q1;data for May 2024 is formatted as YYYY-MM:2024-05.Table 1:Summary of

205、the Economic and Financial Statistics Data Structure DefinitionCONCEPTCODELISTPACKAGING STRUCTUREIDNAMEROLEDATA_DOMAINData domainDimensionCL_DATADOMAINREF_AREAReference country or areaDimensionCL_REF_AREAINDICATOREconomic indicatorDimensionCL_INDICATORCOUNTERPART_AREACounterpart areaDimensionCL_REF_

206、AREAFREQFrequencyDimensionCL_FREQTIME_PERIODTime periodDimensionBASE_PERBase periodAttributeSeries levelUNIT_MULTUnit multiplierAttributeCL_UNIT_MULTSeries levelTIME_FORMATTime formatAttributeCL_TIME_FORMATSeries levelCOMMENTCommentAttributeDataset levelOBS_STATUSObservation statusAttributeCL_OBS_ST

207、ATUSObservation levelOBS_VALUEObservation valueMeasureCL=codelist,FREQ=frequency,ID=unique identification of the concept,MULT=multiplier,OBS=observation,PER=period,REF=reference.Note:The Economic and Financial Statistics Data Structure Definition was developed and is maintained by the International

208、Monetary Fund.Source:Asian Development Bank construction based on the Economic and Financial Statistics Data Structure Definition taken from https:/sdmxcentral.imf.org/data/datastructure.html.Components of the Statistical Data and Metadata eXchange Framework215.1.2 The Importance of Structural Data

209、ModelingStatistics are generally concerned with particular details about objects or events.A few examples are persons,households,geographic areas,loans,jobs,marriages,and births.Objects and events have a variety of characteristics that help define or describe them.A few characteristics of a person,f

210、or example,are age,weight,height,eye color,country of birth,education status,employment status,nationality,mother tongue,and languages spoken.Data modeling is a general methodology for deciding on and defining all the objects and events to be used in a dataset,determining the characteristics needed

211、to accurately define and describe the statistical data,and specifying the relationships between the data and the processes that act upon them.Structural data modeling,more particularly,is a systematic approach to conceptualizing and organizing information within a specific context.It involves the cl

212、ear and unambiguous identification and description of concepts,the selection of key properties and specification of their attributes,the definition of the relationships between concepts,and the formal codification of these concepts(Figure 8).This process is fundamental in various fieldsincluding dat

213、abase design,systems analysis,and information managementas it provides a clear and structured representation of the data within a given domain(UNDESA 2019).Figure 8:The Four Quadrants of Structural Data ModelingSource:Asian Development Bank visualization.Selection ofKey PropertiesClear andUnambiguou

214、sIdentificationFormalCodification ofInformationDefinition ofRelationshipsEnhancing Data Management Through the Statistical Data and Metadata eXchange Standard22Clear and unambiguous identification involves carefully identifying and delineating the concepts to describe the data.This involves a precis

215、e definition of what these concepts represent in the context of the data.For example,age disaggregation is not relevant in the definition for international merchandise trade statistics but is pertinent for social indicators.Selecting key properties means accurately identifying the essential properti

216、es that need to be captured to describe the concepts meaningfully.These properties provide crucial information about the characteristics of the identified concepts.Examples of such properties include a unique identifier,a name,and a description.For instance,within the concept of frequency,the identi

217、fier is:FREQ,the name is frequency of observation,and the description is time interval at which observations occur over a given time period.Additionally,name and description can be expressed in multiple languages,with English as the default.The definition of relationships between concepts requires u

218、nderstanding and formalizing connections within the data.For statistics to be of high quality and comparable,the model used to define and describe the data needs to be standardized,with common concepts specified using common terms.In a world with many languages and alphabets,the most effective appro

219、ach to this challenging task is to use codes to represent the concepts.A code is a language-independent set of letters,numbers,or symbols that represent a concept,the meaning of which is described in a natural language.For example,under the concept of sex or gender,the code“F”can represent female(En

220、glish),femme(French),or femenina(Spanish).Formal codification of information involves creating a structured representation of all concept codes by,for example,creating codelists for all categorical variables,e.g.,sex,age,eye color,or employment status,and grouping relevant concepts together into dat

221、a structures.This enables ease of management and dataflows to support processes such as sharing data with other organizations,publishing in a data portal,using data within internal processes,and collecting data from providers.5.1.3 Structural Data Modeling in the Context of the StandardStructural da

222、ta modeling is an essential part of SDMX.It ensures a standardized and consistent representation of statistical information.The structural data model of SDMX serves as the foundation for organizing,describing,and exchanging data and metadata across diverse systems and organizations.It can vary acros

223、s different statistical domains as some data concepts may be essential in one domain but not relevant in another.For instance,while sex may not be relevant in a consumer price index,it holds significance for many Sustainable Development Goal indicators.By identifying and defining statistical concept

224、s,specifying data structures,and allowing interoperability,structural data modeling in SDMX promotes seamless data exchange,consistency,and the efficient implementation of the SDMX standard.This form of data modeling plays a crucial role in quality assurance,metadata description,and the transformati

225、on of datasets,making it an integral aspect of SDMXs goal to provide a universal framework for statistical data exchange.Components of the Statistical Data and Metadata eXchange Framework235.1.4 Message Formats under the StandardMessage formats provide standardized ways to structure and convey stati

226、stical information between systems.The choice of the format depends on factors such as data complexity,interoperability requirements,and the specific needs of data producers and consumers.SDMX supports multiple message formatsincluding Extensible Markup Language(XML),JavaScript Object Notation(JSON)

227、,and comma-separated values(CSV)to cater to diverse user needs and preferences.Extensible Markup LanguageThe default message format of SDMX is in Extensible Markup Language,known as SDMX-XML or SDMX-ML.A sample of the message format is shown in Figure 9.XML is a robust and widely adopted format,offe

228、ring a hierarchical and extensible structure.It provides a standardized way to represent complex relationships within statistical data and metadata,ensuring consistency and ease of interpretation by both humans and machines.XMLs extensibility allows for the inclusion of additional information or cus

229、tomizations,assisting adaptation to specific needs while maintaining compatibility.Figure 9:Sample Message Using Extensible Markup LanguageSource:Asian Development Bank screenshot taken from internal systems.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard24JavaS

230、cript Object NotationJSON is a lightweight data interchange format that is easy for humans to read and write,and easy for machines to parse and generate.SDMX-JSON is a specific message format designed for the exchange of statistical data and metadata in accordance with the SDMX standard.A sample of

231、the format is shown in Figure 10.SDMX-JSON aligns with modern web development practices,making it suitable for web services(e.g.,application programming interfaces)and applications that operate in a JSON-centric environment.Figure 10:Sample Message Using JavaScript Object NotationSource:Asian Develo

232、pment Bank screenshot taken from internal systems.Comma-Separated ValuesCSV is a plain-text format in which values are separated by commas and each line represents a record.The tabular structure of CSV aligns well with the representation of tabular statistical data,making it suitable for datasets or

233、ganized in rows and columns.SDMX-CSV is a particular message format used for the exchange of statistical data and metadata following the SDMX standard.A sample of this format is shown in Figure 11.Components of the Statistical Data and Metadata eXchange Framework2552 Content-Oriented GuidelinesThe S

234、DMX Content-Oriented Guidelines(SDMX-COGs)refer to recommended practices for the creation of interoperable statistical data and metadata sets(SDMX 2016).A primary focus of the SDMX-COGs is harmonizing specific concepts and terminologies that are common to a large number of statistical domains.By ali

235、gning concepts and terminology,the SDMX-COGs create a shared language and understanding.The harmonization promoted by the SDMX-COGs contributes to a more efficient exchange of comparable data and metadata.This is particularly valuable where datasets need to be compared or combined across different s

236、tatistical domains:common terminology and concepts enhance the consistency and reliability of such comparisons.The SDMX-COGs are developed using implementation experiences within the SDMX community.They leverages lessons learned from practical implementations,ensuring that the guidelines are grounde

237、d in real-world scenarios and are reflective of best practices globally.5.2.1 Cross-Domain ConceptsA statistical concept in SDMX describes all relevant characteristics of the data,each assigned to represent as either a dimension,attribute,or measure.The term“cross-domain”is used to indicate that a c

238、oncept can be applied in more than one statistical domain in a materially similar form.In other words,these concepts exhibit a substantial degree of similarity when applied in different domains.Figure 11:Sample Message Using Comma-Separated ValuesSource:Asian Development Bank screenshot taken from i

239、nternal systems.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard26Figure 12 illustrates two distinct statistical domains.In Domain 1,statistical concepts like customs procedure,trade flow,trade system,reference area,time period,and observation value are used.Mean

240、while,Domain 2 uses concepts such as accounting entry and stocks,alongside an activity classification.Notably,both domains share certain concepts that are presented in a materially similar form.These include reference area,time period,and observation value.These shared concepts serve as prime exampl

241、es of cross-domain concepts.Figure 12:Example of Cross-Domain ConceptsOBS=observation,Proc=procedure,Ref=reference,Sto=stock,transactions,and other flows.Notes:Domain 1 represents selected concepts from the International Merchandise Trade Statistics data structure definition while Domain 2 represent

242、s select concepts from the System of National Accounts data structure definition.Source:Asian Development Bank visualization.DOMAIN 1DOMAIN 2CUSTOMS_PROC,TRADE_FLOW,TRADE_SYSTEMREF_AREATIME PERIOD,OBS_VALUEACCOUNTING_ENTRY,STO,ACTIVITYCross-domain concepts in the SDMX framework refer to concepts tha

243、t are relevant to many,if not all,statistical domains(SDMX 2016).These are fundamental elements describing statistical data and are intended for use across different domains wherever possible.The SDMX cross-domain concepts are actively developed and published by the SDMX Statistical Working Group.Th

244、is group is responsible for shaping standardization efforts and ensuring that cross-domain concepts are well-defined,widely applicable,and adhere to best practices.5.2.2 Codes and CodelistsCodes that are language-independent and used globally are crucial for ensuring standardized communication and d

245、ata interchange across diverse systems and languages.For instance,in the aviation and travel industries,three-letter codes provide a standardized and globally recognized way of identifying airports Components of the Statistical Data and Metadata eXchange Framework27(e.g.,MNL for Manila airport,SIN f

246、or Singapore airport).These codes are used on flight information displays at airports and on airline websites.Travelers can quickly locate their departure and arrival gates,check flight status,and navigate airports more efficiently using these codes.Each code within a codelist should be well-describ

247、ed.This involves providing clear and comprehensive information about the meaning,context,and usage of each code.Well-documented codes contribute to the understanding and correct interpretation of statistical data.Codelists are established to organize interrelated concept codes into a meaningful,syst

248、ematic,and standardized format.The aim is to establish a structured and standardized format for organizing terms that represent specific concepts in statistical data and metadata.A codelist functions as a collection of codes maintained as a unit.As part of the SDMX-COGs,codelists refer to predefined

249、 sets of terms from which certain statistically coded concepts derive their values(SDMX 2018).To provide SDMX codelists with a distinct visual identity,it is recommended to prefix their identifiers with CL_.The codelist comprises three essential elements:an identification number,a version number,and

250、 a reference to a maintenance agency.Table 2 provides a sample of cross-domain SDMX codelists.Table 2:Sample of Cross-Domain CodelistsIdNameDescriptionAgency IdVersionCL_FREQFrequencyThis codelist provides a set of values indicating the“frequency”of the data(e.g.weekly,monthly,quarterly).The concept

251、“frequency”may refer to various stages in the production process,e.g.data collection or data dissemination.For example,a time series could be disseminated at annual frequency but the underlying data are compiled monthly.The codelist is applicable for all different uses of“frequency”.SDMX2.1CL_UNIT_M

252、ULTUnit multiplierThis codelist provides code values for indicating the magnitude in the units of measurement.More information about this codelist and SDMX codelists in general(e.g.list of generic codes for expressing general concepts like“Total”,“Unknown”,etc.;syntaxes for the creation of further c

253、odes;general guidelines for the creation of SDMX codelists)can be found at this address:https:/sdmx.org/?page_id=4345.SDMX1.1CL_SERIESSDG series codelistIAEG-SDGs1.6CL_EDUCATION_LEVSDG educational level codelistIAEG-SDGs1.0CL_CURRENCYCurrency of issuance or invoicing codelistIMF1.6CL=codelist,Freq=f

254、requency,IAEG-SDGs=Inter-agency and Expert Group on Sustainable Development Goal Indicators,IMF=International Monetary Fund,Lev=level,Mult=multiplier,SDMX=Statistical Data and Metadata eXchange.Note:Name and Description can be expressed in multiple languages.Source:Asian Development Bank screenshot

255、taken from SDMX foundation course material.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard285.2.3 Statistical Subject-Matter DomainThe statistical subject-matter domain is a fundamental concept within the SDMX-COGs,emphasizing the common characteristics and stan

256、dardized approaches associated with specific statistical activities(SDMX 2016).The SDMX framework provides structured classifications for organizing data based on these subject-matter domains such as national accounts,balance of payments,international merchandise trade,consumer price indices,and Sus

257、tainable Development Goals(SDGs),among others.Classifications under statistical subject-matter domains provide a high-level scheme for organizing statistical data and metadata in various types of applications.These classifications are based on the United Nations Economic Commission for Europes Class

258、ification of International Statistical Activities.The classifications are often expressed as data structure definitions(DSDs).The SDMX sponsor organizations have defined global DSDs for various statistical domains,including consumer price indices,national accounts,balance of payments,international m

259、erchandise trade statistics,the SDGs,the System of Environmental-Economic Accounting,government finance statistics,and foreign direct investment.Individual economies have the flexibility to customize existing DSDs to align with their national data needs,with the scope to expand the codes to accommod

260、ate their particular statistical objectives.The established global DSDs can be found in the SDMX Global Registry.7 When reporting SDG data,national statistics offices use the SDG global DSD to report to international organizations.5.2.4 GlossaryThe SDMX Glossary contains concepts and related definit

261、ions used in structural and reference metadata across international organizations and national data-producing agencies(SDMX 2020c).It focuses on terms essential for constructing and understanding metadata systems and facilitating SDMX data exchange arrangements.Rather than imposing specific concepts

262、 and codelists for SDMX structures,the glossary recommends a common terminology.This recommendation aims to foster communication and understanding by suggesting a shared vocabulary while allowing flexibility in the use of concepts and codes.Figure 13 provides an overview of the cross-domain concept

263、incorporated in the SDMX Glossary.7 Access the global DSDs in the SDMX Global Registry via https:/registry.sdmx.org/data/datastructure.html.Components of the Statistical Data and Metadata eXchange Framework2953 Technical StandardFor some time,statistics offices have used information technology(IT)to

264、 improve the efficiency of statistical processes.However,such innovations have remained confined only to specialized areas,creating islands where IT tools are less likely to interoperate,share data,and work together as a cohesive system.This fragmented environment of individual and disparate IT syst

265、ems created what many researchers called“silos”within statistics offices.A data flow analysis offers active measures that statistics offices can take to break the silos by harnessing data in digital format to realize the gains of using advanced technologies(Paris21 2021).Data flow analyses can also

266、pave the way for statistics producers to follow the SDMX Technical Standard,which defines the configuration of IT tools to support the SDMX process through the entire data lifecycle.The technical standard transforms the SDMX Information Model and the SDMX Content-Oriented Guidelines into practical s

267、ystems and databases,including web services application programming interface(API)specification,transmission format specifications,and the SDMX registry specification.The web services API specification provides a standardized interface for interacting with software systems implementing the SDMX stan

268、dard.An organizations SDMX-API web service typically would offer programmatic access to data and metadata published on the organizations data portal.The related open API documentation describes the supported functionality in an interactive way.Data retrieval and discovery are supported in a variety

269、of formats(e.g.,JSON,XML,CSV).Data and structural metadata such as DSDs,codelists,and concept schemes are available via this API service.The latest version of the standard,Figure 13:Cross-Domain Concept in the GlossarySource:Asian Development Bank screenshot taken from https:/sdmx.org/wp-content/upl

270、oads/SDMX_Glossary_Version_2_1_December_2020.htm#_Toc59116750.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard30SDMX3.0,supports transmission formats of data and structural metadata in XML and JSON formats,and of data only in CSV format.The XML and JSON component

271、s also support reference metadata.The SDMX registry is a controlled repository for structural metadata and processes,which organizations can consult for information on how to structure,process,validate,and interpret statistical data.316STEPS TO IMPLEMENTING STATISTICAL DATA AND METADATA EXCHANGEThe

272、adoption of the Statistical Data and Metadata eXchange(SDMX)standard should be thought of as a change project or,depending on the quantity and breadth of statistical data and activities across the organization,many change projects.These projects will typically span multiple years and impact statisti

273、cians,economists,communications,and information technology(IT)staff within an organization.61 Identifying Organizational Priorities and Applications of the Standard Statistical organizations typically use a variety of approaches to decide where to invest scarce financial and human resources.Needs ma

274、y be identified as a result of audits on data quality and/or governance,an organizational assessment of risks and opportunities,or a maturity assessment using a data quality framework.In other instances,there may be a desire to implement a significant new capability,such as a data portal,or the need

275、 to accommodate an international initiative,such as the Sustainable Development Goals.The first step in adopting the SDMX standard is therefore to undertake a rigorous assessment and determine alignment of SDMX capabilities with organizational goals and priorities.This assessment should then be foll

276、owed by the development of a prioritized road map of projects to guide the SDMX adoption efforts.Paris21,which was established to advance statistical capacity in low-and middle-income economies,has defined a data flow assessment framework to assist organizations with this process.The Paris21 guideli

277、nes help statistical organizations assess and document data flows,specifying how data is collected,processed,and disseminated.The goal during this phase is to become familiar with the fundamental concepts of SDMX and to identify and prioritize the primary applications of SDMX within the organization

278、,such as data reporting to international organizations using global data structure definitions(DSDs).In other cases,the organization may already have decided on their priority projects,such as implementing a data portal,and the change effort is focused on specific uses of SDMX.62 Developing a Strate

279、gy for Organizational Upskilling As the depth and scope of data being modeled increase,so must the data governance practices that ensure the quality,coherence,and consistency of the model and the modeled artefacts.The second step in SDMX adoption is therefore to establish a resourcing strategy for u

280、pskilling staff to become SDMX literate.These staff members must have the requisite knowledge and skills to model data in accordance with the SDMX standard and its recommended modeling practices.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard32A governance frame

281、work for SDMX implementation typically would establish roles,responsibilities,and data quality assurance procedures.The goals during this phase are to broaden and deepen the knowledge of SDMX among key staff,especially the statisticians who are responsible for data modeling;and to create the necessa

282、ry SDMX information model artefacts for the project,such as DSDs,including the concept scheme,codelists,and roles.863 Understanding the Technology Needs Under Implementation In parallel with data modeling,a stream of work should be undertaken to assess the IT needs to deliver on the initial projects

283、 as well as the architectural implications for delivering on the full range of projects that have been identified.It is important during this IT and architecture phase to fully understand the organizations vision for SDMX adoption.While changing tools later on in the adoption process is feasible,sel

284、ecting the tools that are the best fit from the very beginning generally results in the most efficient use of resources.Changing tools midway through an SDMX adoption creates data quality and project delivery risks,but this may be the best approach in certain circumstances.These deliberations should

285、 involve discussions with other organizations who have successfully implemented SDMX as well as accessing other educational resources.9The goal for this stream of work is to upskill the core team to be adept SDMX practitioners in the modeling of statistical data and to have produced the necessary SD

286、MX artefacts for the first SDMX application within the organization.64 Other Factors for Consideration As with any change initiative,a communication strategy will be required to engage internal and external stakeholders affected by the SDMX adoption project(or projects)and a robust testing and trans

287、ition processes should be undertaken.It is desirable,and in most cases quite feasible,to realize concrete benefits in data quality and operational efficiency throughout the phases of SDMX implementation.8 There are a number of resources available to assist organizations in this regard,such as the AD

288、B e-learning course on SDMX Foundation(https:/elearn.adb.org/course/view.php?id=486)and SDMX Tools(https:/elearn.adb.org/course/view.php?id=520),the.Stat academy(https:/academy.siscc.org/),and sdmx.io(https:/sdmx.io/resources/elearning).For a current list of all SDMX courses,consult the learning cat

289、alogue on the sdmx.org website.9 These resources include ADBs e-learning course on SDMX tools,the sdmx.io site managed by the Bank for International Settlements,and the sdmx.org tools site.Steps to Implementing Statistical Data and Metadata eXchange33Once the organization has achieved its initial go

290、als in adopting SDMX,more advanced implementations can be considered.This could involve extending the adoption of SDMX to other sectors(domains)within the organization or extending the adoption of SDMX to other statistical processes such as data collection or production.Advanced implementations coul

291、d enable the organization to automate data exchange processes using SDMX-compliant application programming interfaces,or to further improve data quality by implementing advanced data and metadata validations and transformations for via extensions such as the Validation and Transformation Language.34

292、7IMPORTANT APPLICATIONS OF STATISTICAL DATA AND METADATA EXCHANGETwo primary applications of Statistical Data and Metadata eXchange(SDMX)for individual economies are the National Summary Data Page(NSDP)and the more general statistical data portal.71 National Summary Data PageThe NSDP functions as a

293、data dissemination platform for economies actively engaged in the International Monetary Funds Special Data Dissemination Standard(SDDS),SDDS Plus,or the Enhanced General Data Dissemination System(e-GDDS).As the name suggests,the NSDP is a web page that provides a summary of key macroeconomic and fi

294、nancial data and social indicators for the economy involved in SDDS,SDDS Plus,or e-GDDS(IMF n.d.).10 The NSDP aims to enhance data dissemination and improve the accessibility and visibility of the data for end users.It provides access to a diverse range of online data and metadata across all availab

295、le categories for a given economy.Notably,even where these categories are compiled by multiple statistical agencies(e.g.,national statistics offices,central banks,etc.),the NSDP offers a simple way of disseminating data in SDMX format for various statistical domains(IMF n.d.)where economies are enga

296、ged in SDDS Plus or e-GDDS.The NSDP is hosted and maintained by the national statistics office or other relevant government agency.It serves as a great resource for policymakers,researchers,and the general public to access important statistical information.Figure 14 shows an example of Bhutans NSDP

297、in SDMX format.1110 As of 14 June 2024,36 ADB members in Asia and the Pacific were participating in SDDS,SDDS Plus,and e-GDDS.11 Access the current NSDP of Bhutan via https:/www.nsb.gov.bt/national-summary-data-page-nsdp-bhutan/.Important Applications of Statistical Data and Metadata eXchange3572 Da

298、ta PortalWith an increasing need for efficient data sharing,the online data portal serves as one of the most advanced models for disseminating information.Such platforms are designed to simplify the exploration,retrieval,and visualization of statistical data.The Asian Development Bank(ADB),through t

299、he Data Division of the Economic Research and Development Impact Department,supports National Statistics Offices in the region in adopting the of the SDMX standard.A prime example of a quality online data portal using SDMX is the Statistics Sharing Hub developed and maintained by the National Statis

300、tical Office of Thailand(TNSO).The statistical system in Thailand functions in a decentralized manner,whereby various government agencies independently undertake statistical activities aligned with their respective missions,which lead to dispersion of diverse statistical data in both the public and

301、private sectors.Moreover,the different agencies involved employ diverse formats and definitions for data storage.The challenge therefore lies in integrating these datasets for effective public administration or crisis response.Figure 14:National Summary Data Page for BhutanSource:Asian Development B

302、ank screenshot taken from https:/www.nsb.gov.bt/national-summary-data-page-nsdp-bhutan/.Enhancing Data Management Through the Statistical Data and Metadata eXchange Standard36The solution to this challenge required the establishment of standardized methods for the exchange of statistical data,includ

303、ing the development of a statistical framework that defined common data formats and terminology.Moreover,this framework needed to allow automation of data exchange,thereby streamlining the process of data integration and enhancing the efficiency of data utilization.The TNSO took the initiative to es

304、tablish a robust platform capable of integrating diverse indicators into a centralized dissemination hub featuring advanced functionalities and SDMX compliance.These ambitions led to the creation of the TNSOs Statistics Sharing Hub,powered by the.Stat Suitea potent SDMX tool renowned for its effecti

305、veness in disseminating statistical data and metadata.Moreover,the TNSO also used a combination of tools to implement the SDMX standard.For structural metadata repository,the office used the Fusion Metadata Registry to store,publish,and maintain their SDMX artefacts such as the DSDs,dataflows,concep

306、t schemes,and codelists.For SDMX data preparation,the TNSO used Excel2CSV to convert to SDMX-CSV format and uploaded to.Stat Suite for data dissemination(TNSO 2023).Box 1 provides more information about the TNSOs implementation of SDMX,while Box 2 and Box 3 showcase benefits gained from SDMX impleme

307、ntations in Maldives and across the Pacific.continued on next pageBox 1:Streamlining Statistics Sharing in ThailandSource:Screenshot of the National Statistical Office of Thailands Statistical Sharing Hub taken from https:/stathub.nso.go.th/?lc=en&pg=0.Important Applications of Statistical Data and

308、Metadata eXchange37Background The statistical system in Thailand operates in a decentralized manner,with government agencies conducting various independent statistical activities aligned with their missions.This results in diverse statistical data scattered across the public and private sectors.Whil

309、e there is an abundance of data,integrating diverse datasets for public administration or crisis response is challenging due to variations in data storage formats and definitions among different agencies.To address this issue,the National Statistical Office of Thailand(TNSO)explored the establishmen

310、t of standardized methods for the collaborative exchange of statistical data.This would involve developing a framework that defines common data formats and definitions.Such a framework would enable computers to automatically exchange data,which would streamline data integration and improve the effic

311、iency of data utilization.Data Governance for Government AgenciesIn 2019,the Government of Thailand passed the Digitalization of Public Administration and Services Delivery Act,B.E.2562(2019),which mandates that government agencies establish data governance frameworks.This legislation was enacted to

312、 enhance digital management practices,including provisions concerning the exchange of government data,as per Section 8(3)and Section 12(1)of the act.The TNSO took the lead in developing a data governance framework at the agency level,known as the TNSO Data Governance Framework.This framework was off

313、icially implemented on 2 July 2021.Moreover,the TNSO has embraced international standards,such as the Data Documentation Initiative,the Generic Statistical Business Process Model,and Statistical Data and Metadata eXchange(SDMX),to enhance organizational compatibility in managing statistical data.The

314、se standards help to improve data quality,streamline data processes,and facilitate collaboration between agencies.In addition,the Government of Thailand commissioned the TNSO to develop a central data repository for government agencies.This repository,called the Government Data Catalog,allows agenci

315、es to share data in a standardized format,making it easier for users to integrate and analyze data across agencies.While the Government Data Catalog hosts a wealth of datasets across five categories,including statistics,their diverse file formats(CSV,XLSX,PDF,JSON,API)posed a significant challenge f

316、or data integration.This stemmed from a lack of standardization across agencies,with varying data storage formats,definitions,and statistical methods hindering seamless collaboration.To bridge this gap and foster seamless collaboration,the TNSO strategically embraced the SDMX standard.By establishin

317、g statistical data structures aligned with SDMX,and publishing them through the Statistics Sharing Hub,the TNSO has orchestrated a remarkable transformation.Data exchange between agencies now flows with unprecedented efficiency,convenience,and speed.This has empowered integration of diverse datasets

318、,such as population and economic statistics,for comprehensive reports and insightful data analysis.Technical Aspects of the ImplementationSince 2020,the TNSO has been developing technological infrastructure,promoting SDMX knowledge among its personnel,and raising awareness of the importance of makin

319、g structured statistical data available.The TNSO has chosen to implement two open-source SDMX tools:the.Stat Suite and the Fusion Metadata Registry.Stat Suite is an open-source platform developed by the Statistical Information System Collaboration Community,supported by the Organisation for Economic

320、 Co-operation and Development in collaboration with the European Statistical Office(Eurostat).It is designed for the management and provision of datasets in accordance with the SDMX standard at the Statistics Sharing Hub(https:/stathub.nso.go.th/).Fusion Metadata Registry is an open-source tool supp

321、orted by the Bank for International Settlements.It is designed for the creation,maintenance,storage,and management of SDMX artefacts,such as codelists,data structure definitions,and dataflows.It can be accessed at https:/sdmx.nso.go.th/.Box 1:continuedcontinued on next pageEnhancing Data Management

322、Through the Statistical Data and Metadata eXchange Standard38Box 1:continuedThe TNSO initiated the integration and structured management of all statistical data in accordance with the SDMX standard.It has collected and provided key datasets at the Statistics Sharing Hub,categorized into four groups:

323、International indicator datasets:These datasets cover a wide range of topics,including demographics,economics,and the environment.They are used by a variety of stakeholders,including government agencies,businesses,and researchers.Datasets aligned with national strategic goals:These datasets support

324、the Government of Thailands key priorities,such as economic development and social welfare.They are used to track progress and inform policy decisions.21 statistical sector datasets:These datasets provide information on specific topics such as agriculture,tourism,and health.They are used by governme

325、nt agencies,businesses,and the public.Datasets presented at the provincial level:These datasets provide provincial-level information on topics such as population,income,and education.They are used by government agencies,businesses,and the public.Beyond integrating data,the TNSO actively fosters a da

326、ta-driven ecosystem.They achieve this by:Empowering government agencies:The TNSO regularly organizes SDMX training courses for government agencies,equipping them with the knowledge and tools to utilize and exchange data following international standards.This strengthens interagency collaboration and

327、 facilitates seamless data sharing.Building nationwide understanding:The TNSO disseminates knowledge on SDMX through various events and courses,including meetings,seminars,and workshops.This raises awareness about the importance of developing data in accordance with international standards,ultimatel

328、y promoting data readiness for global use and exchange.The TNSOs progress has been driven by strong partnerships.The office has collaborated with international organizations such as the Asian Development Bank,the Economic and Social Commission for Asia and the Pacific,the International Labour Organi

329、zation,the Organisation for Economic Co-operation and Development,the United Nations Childrens Fund,and the United Nations Statistics Division.Through these collaborations,the TNSO has received invaluable expertise,training,and technical assistance,which have been the catalysts for the offices remar

330、kable achievements in data integration and development.Benefits of Using Statistical Data and Metadata eXchangeThe TNSO cites four key benefits in applying the SDMX standard in its Statistics Sharing Hub.Standardized structure for seamless exchange:Collecting and storing data in accordance with SDMX

331、 standards guarantees a standardized structure,enabling quick and effortless data exchange across agencies and organizations.Enhanced accessibility and value creation:Adhering to a standardized data structure and utilizing common tools,both domestically and globally,expands data usage across diverse

332、 sectors.This promotes convenient and efficient data utilization,reducing time spent on data cleansing.Automated synchronization for accuracy:SDMX programming automates data updates and retrievals,following strict update schedules.This ensures the seamless display of data that are always identical t

333、o the corresponding source data.Clear understanding and interoperability:Metadata,codelists,definitions,and classifications managed in compliance with SDMX guidelines foster clear understanding and seamless interoperability among diverse data systems and sources.Important Applications of Statistical Data and Metadata eXchange39BackgroundIn partnership with the United Nations Economic and Social Co