Data Standardisation And Governance Capacity

Context

The article examines India’s fragmented public data ecosystem and argues that data standardisation is essential for accountability, welfare efficiency and evidence-based governance.
Source: The elephant in India’s data room, The Hindu, May 9.

Basic data demand: Many parliamentary questions seek routine facts such as school toilets, pension disbursal or scheme beneficiaries, which should already be publicly available.
Accountability burden: MPs are forced to use parliamentary questions for basic information because official data is not easily accessible, standardised or usable.
Youth employment example: Questions during the 17th Lok Sabha on youth employment showed that many queries sought basic factual data.
Core problem: India’s data system is fragmented and lacks interoperability, making data standardisation a governance priority.

Incoherent standards: Ministries and departments often use different standards for common indicators.
Inconsistent definitions: Basic attributes such as time period and region may be defined differently across datasets.
Usability gap: India generates large volumes of data, but abundance does not ensure integration or policy usability.
Consolidation problem: Programme-level data collected by separate Ministries is difficult to merge and prone to errors.

Repeated beneficiaries: Welfare databases often list the same beneficiary multiple times, causing fiscal leakages.
Fiscal impact: Such duplication can inflate welfare spending by 4%–7% annually.
Clean-up gains: Removal of ineligible or bogus entries from schemes such as PM-KISAN, LPG and ration cards shows potential fiscal savings.
Policy distortion: Duplicate or inconsistent records can produce conflicting estimates and weaken decision-making.

Health data problem: Childhood tuberculosis cases may be recorded separately across health information, surveillance and immunisation systems, leading to double counting.
Decision uncertainty: Conflicting estimates can push decision-makers towards anecdote or political expediency instead of evidence.
Global index weakness: Missing or outdated data affects India’s representation in indices such as the Global Innovation Index.
Economic loss: Weak public-sector data sharing can reduce potential gains in GDP and limit the value of public and private data use.

NDGFP role: The National Data Governance Framework Policy provides a route to address data inefficiencies.
IDMO potential: The proposed India Data Management Office can become the central institution for common rules, standards, guidelines and protocols.
Need for authority: IDMO must have powers to set binding standards, audit compliance and resolve disputes over definitions and methodologies.
Global alignment: India should align with global statistical frameworks and harmonise definitions through a National Statistical Standards Manual.

Data.gov.in upgrade: India’s open data platform should become a centralised, schema-consistent repository.
Regular uploads: Ministries should upload standardised datasets regularly for public and internal use.
Real-time access: Parliamentarians should be able to access district-level, real-time figures without relying on repeated basic questions.
DGQI benchmark: NITI Aayog’s Data Governance Quality Index should become an annual benchmark linked to reviews and incentives.
Competitive improvement: Healthy competition among Ministries and States on data quality can improve governance performance.

Quick Fact Box: India’s Data Governance Framework

Proposed Policy Frameworks:

National Data Governance Framework Policy: The National Data Governance Framework Policy was released by MeitY as a draft for public consultation on May 26, 2022. It aims to create rules, standards, guidelines and protocols for responsible sharing of non-personal and anonymised datasets.
Policy Objective: The draft framework seeks to improve access to government-held non-personal datasets while maintaining privacy, security and trust in digital governance.
Current Status: The NDGFP should be treated as a proposed policy framework, not as a binding statute or standalone legal framework. The government had described it as being under finalisation after public consultation.

India Data Management Office:

Institutional Location: The India Data Management Office is proposed under the draft NDGFP as an institutional mechanism under the Digital India Corporation, MeitY.
Core Role: The IDMO is envisaged to frame, manage, review and revise the policy, along with developing rules, standards and guidelines for data governance.
Coordination Function: The IDMO is expected to coordinate with line ministries, State governments and government programmes to standardise data management and strengthen institutional capacity.
India Datasets Programme: The IDMO is also linked to the inclusion of non-personal datasets from ministries and private companies into the India Datasets programme.

Operational Data-Governance Mechanisms:

Data Governance Quality Index: The DGQI is an operational benchmarking tool initiated by DMEO, NITI Aayog, to assess the data preparedness of ministries and departments on a standardised framework.
Institutional Support: The DGQI exercise was undertaken by DMEO, NITI Aayog, with support from NIC/NICSI to evaluate and improve data preparedness across ministries and departments.
Assessment Themes: The DGQI covers key themes such as data generation, data quality, use of technology, data analysis, use and dissemination, data security, human-resource capacity and case studies.
Governance Purpose: The DGQI is intended to promote capacity enhancement, inter-departmental collaboration, informed policy-making and healthy competition among ministries and departments.

Evidence-Based Governance:

Policy Monitoring: India increasingly uses surveys, indicators, dashboards and evaluation systems to support policy design, monitoring and administrative review.
Illustrative Tools: NFHS-based health indicators, SDG India Index, DMEO evaluations and scheme-monitoring dashboards reflect the growing use of data in governance.
Administrative Value: These tools help link government data with programme monitoring, policy feedback and outcome-based governance.

Continuing Data-Governance Challenges:

Fragmented Databases: Government data systems remain affected by fragmentation across departments, schemes and levels of government.
Uneven Data Quality: Differences in data quality, reporting formats and institutional capacity limit consistent evidence-based decision-making.
Interoperability Gaps: Limited interoperability continues to affect the integration and use of datasets across ministries, departments and States.
Capacity Variation: Administrative capacity varies across departments and States, creating uneven implementation of data-governance practices.

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