4 min readApr 4, 2026 06:47 AM IST First published on: Apr 4, 2026 at 06:46 AM ISTAdministrative data has emerged as a powerful and strategic asset for modern governance and decision-making. Generated as a byproduct of routine administrative processes, it offers granular insights not only for decision-making but also to enable governments and institutions to move towards more continuous and comprehensive data systems.In India, over the past decade, the growing digitalisation of public services has led to a substantial increase in the availability of digital data across diverse socio-economic dimensions. Almost every ministry and department at the Centre and in states/UTs now relies on internal MIS systems and dashboards that collect and collate data from stakeholders. Examples include the Unified District Information System for Education (UDISE) for school education, Udyam for MSMEs, Prime Minister’s Overarching Scheme for Holistic Nourishment (POSHAN) Tracker for child nutrition, Aadhaar, GSTN data and Health MIS.AdvertisementWhile these systems provide significant value within individual departments, their impact can be enhanced through harmonisation of datasets, enabling information from different sources to be combined to generate more meaningful insights. The vast volume and diversity of administrative datasets make them highly suitable for advanced analytics using AI and machine learning. As datasets become increasingly discoverable, machine-readable, consistent, and easier to integrate, their analytical potential expands. In this context, the growing focus on “AI-ready” data highlights the importance of data harmonisation.By strengthening standardisation, ensuring uniformity in concepts and definitions, and enabling interoperability, data harmonisation makes it possible to transform isolated datasets into a coherent whole. To support these outcomes, a structured and enabling environment is being established, anchored in five pillars: Clear documentation of what the data represents, how it is collected, and how it can be used (metadata); ensuring accuracy and reliability (data quality); adopting common definitions and classification frameworks (standards and classifications); establishing unique identifiers to enable linkage across datasets and carefully reconciling differences.An updated National Metadata Structure (NMDS 2.0) has been introduced, providing a common framework for ministries/ departments and states/ UTs to present and share data. There is a continued emphasis on the use of standard national and international classification systems such as National Industrial Classification (NIC), National Classification for Occupations (NCO), Classification of Individual Consumption According to Purpose (COICOP), etc.AdvertisementAn institutional mechanism has been established to align and reconcile definitions across datasets. For example, concepts such as “pucca house” or “household” may be captured differently in various sources. In addition, efforts have been made to work across ministries and departments to identify unique identifiers that enhance interoperability.Data is only as powerful as its quality and the trust it inspires. A Statistical Quality Assessment Framework has been put in place to help agencies take a closer look at their own data, spot gaps in quality, and improve over time.you may likeCore attributes of data like timeliness, frequency, granularity and coverage remain important. The time lag in releasing survey results has been reduced from eight-nine months to 45-90 days, and monthly estimates are now being generated under flagship surveys such as the Periodic Labour Force Survey (PLFS) and quarterly estimates under the Annual Survey of Unincorporated Sector Enterprises (ASUSE). New surveys have been introduced, covering areas like household income, the service sector, and capital expenditure, and district-level estimates can now be generated by states under surveys.In today’s digital age, with frontier technologies evolving rapidly, data in isolation is powerless. Integrating information from multiple sources transforms numbers into knowledge, knowledge into understanding, and understanding into action. Harmonised datasets, when combined, are a force capable of driving transformative development, guiding smarter decisions, and charting the course of societal progress. As we harness this potential, we are reminded to “measure what we treasure, and treasure what we measure”.Edited excerpts from a speech delivered by the secretary, MoSPI, at the Loksatta Jilha Nirdeshank (District Development Index) awards in Mumbai in March