Adoption of Statistical Data Metadata Exchange (SDMX) in India and Its Way Forward
Data dissemination in India plays a crucial role in making statistics accessible and understandable to the public. The usage of SDMX for exchanging statistical data metadata aims to enhance comparability and interpretation, enabling collaboration with major data agencies. Challenges lie in extending the platform to encompass socio-economic surveys. Initiatives like setting up a Data Warehouse by NSO India demonstrate efforts towards meeting national and international data needs, aligning with SDMX requirements.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
E N D
Presentation Transcript
Adoption of Statistical Data- Metadata Exchange (SDMX) in India and its way forward Dr. Mamta Saxena, ADG, DSDD Mr. Kumar Sundaram, JD, NAD Dr. Sudeepta Ghosh, DD, NAD NSO,Ministry of Statistics & PI INDIA
Outline of the Presentation Introduction SDMX India and SDMX Discussions Challenges Way Forward
Introduction Data Dissemination ensures that the statistics produced are made public for the use of the audience at large. Production of statistics does not carry enough weight unless it is made palatable for public consumption with comparability and potential for concrete interpretation. The dissemination of statistics nowadays is not only confined within the boundaries of the nation but also across the nation.
India and SDMX Under the MoU , EuroStat offered assistance to SDMX project in India under framework of EU Project on ICT Standardization Initiated SDMX in the domain of National Accounts-started with Quarterly NA. Intended to be extended to Annual NA. Further the same has been extended to the NSDP, India. India for SDMX intends to onboard and thereby collaborate with major data provider/generator agencies namely Central Bank, M/o Finance, other Central/State agencies which are routinely generating official Statistics Data. RBI already shares information in SDMX format with BIS.
Data Warehouse The NSO India is also in the process of setting up a Data Warehouse with the objective to address the various national and international data requirements. It would also act as central repository of all the statistical data collected by various ministries/ other Agencies generating Administrative data. It further aims at leveraging big data analytical tools to improve the quality of macro-economic aggregates. It is pragmatic that the data Warehouse is compatible with SDMX requirement.
India and SDMX India s economy has a large chunk of the informal sector; the data source of which is primarily the NSS surveys under NSO India. The NSO conducts large scale sample surveys on routine and as per requirements. The surveys generate large scale data related to various socio-economic phenomenon. In most of the developing nations, estimation procedure rely to a large extent on the socio-economic surveys. Until SDMX platform is extended to data/results of socio- economic surveys the objectives behind envisaging SDMX platform would remain unfulfilled.
Challenges and its remedies Awareness: SDMX being a new domain, more awareness emphasizing its importance needs to be spread. Capacity Building: Regular trainings of officials at various levels are required for proper understanding its concepts Shifting of officials : In order to cope up with the issue more and more officials needs to be thoroughly trained in this area.
Way Forward Expand the domain of the SDMX to be at par with other developed nations. Expand SDMX on socio-economic surveys Requires onboarding of more agencies within the implementing countries to expedite the conversion to SDMX compatible format. A large amount of data is already on web which may be attempted for SDMX conversion so as to have larger time series of data for better analysis. Regular trainings for capacity building.
