India's Ministry of Statistics and Programme Implementation (MoSPI) is planning a major overhaul of the national statistical architecture, with officials saying artificial intelligence will be embedded across official data systems. MoSPI secretary Saurabh Garg told the Economic Times that the ministry has constituted a sub-committee to examine implementation of the United Nations System of National Accounts (SNA) 2025 and that "the committee will focus on the technical aspects of the overall framework including treating data as an asset". Garg also told ET the ministry is working on an Index of Service Production, expected to be released by July, and has initiated a Data Innovation Lab and collaborations to develop practical use cases. MoSPI says harmonisation of administrative datasets and integration of alternative data sources will be priorities, with data quality controls and field training emphasised.
What happened
MoSPI has signalled a planned modernisation of India's statistical system, with integration of artificial intelligence into official statistics and steps to treat data as an asset, Saurabh Garg, secretary of the Ministry of Statistics and Programme Implementation, told the Economic Times. The ministry has "constituted a separate sub-committee to examine the implementation of the United Nations System of National Accounts (SNA) 2025," Garg told ET, and he said "the committee will focus on the technical aspects of the overall framework including treating data as an asset and assess how best it can be adapted and implemented in the Indian context." Garg told ET the ministry is working on an Index of Service Production, expected to be released by July, and has initiated a Data Innovation Lab and collaborations with multiple stakeholders to develop practical use cases. MoSPI emphasised multiple layers of scrutiny, validation, and field training ahead of national surveys, and reported ongoing work on harmonising administrative datasets across states and union territories.
Technical details
Editorial analysis - technical context: The reported measures listed by MoSPI that relate to practitioner workflows are systematic data harmonisation, incorporation of alternative and non-traditional data sources, and applying AI, machine learning, and big data techniques to official statistics. These elements collectively point to needs practitioners typically expect: standardized identifiers, interoperable administrative data schemas, documented lineage for training datasets, and reproducible validation pipelines. Public reporting lists planned items including:
- •integration of AI and machine learning into official statistics
- •a national business register
- •the Data Innovation Lab and stakeholder collaborations
Context and significance
Government adoption of AI for core official statistics is an inflection point for data infrastructure because it can change what administrative and survey data become available and how they are processed. Standard adoption of the SNA 2025 concept that "data is an asset," as noted by Garg to ET, aligns India with international accounting frameworks and may raise demand for enterprise-grade data governance, metadata standards, and secure compute environments. For researchers and model builders, a national business register and better harmonised administrative records typically improve sampling frames and firm-level analysis, while also introducing new requirements around privacy-preserving linkage and documentation.
What to watch
For practitioners: follow these indicators reported by MoSPI and ET to assess implementation risk and opportunity: the sub-committee's technical recommendations on SNA 2025, the July release and methodology of the Index of Service Production, documentation and API access policies from the Data Innovation Lab, and concrete interoperability standards for administrative datasets across states. Observers should also track whether published efforts include published data schemas, access procedures, and documented validation/quality-assurance processes. MoSPI has not provided a detailed public roadmap in the ET interview beyond these items, and timelines and technical specifics remain to be published.
Scoring Rationale #
The story is notable because it signals national-level adoption of AI and data-as-asset thinking, which can affect data availability and standards used by practitioners. Implementation details and timelines are still sparse, limiting immediate operational impact.
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