The recent slew of developments around Bharat Innovation 2026, a number of UK universities establishing campuses in India and examination paper leaks have put the spotlight on higher education again, attracting significant public attention. But our ability to understand this system has not kept pace. We are still trying to make sense of a large elephant while blindfolded.
For most, understanding the system begins on Google, with rankings and expert opinion, and ends with ChatGPT, or one wanders through social media and news headlines, and occasionally through scattered reports. These hardly tell what’s beneath the surface. It’s not just about public interest in higher education; the lack of transparency and adequate data can also distort higher education markets, privileging those with access and resources.
The higher education system itself is enormous. India has 1,168 universities, more than 45,000 colleges, and 4.33 crore enrolled students. The Gross Enrolment Ratio (GER) stands at 28.4% as per the last AISHE survey conducted in 2021-22. NEP 2020 aims to push it to 50% by 2035. 54 Indian universities now feature in the QS World Rankings 2026.
More than 15 foreign universities have announced campuses in India. Yet it is also deeply fragmented, with several types of universities and affiliated colleges, myriad degrees and nomenclatures, and governance structures that make coherence, comprehension, and standardization difficult. For a system of this scale and ambition, the shortage of reliable, updated, and publicly accessible data is not a side effect; it is the heart of the problem. The system has grown faster than the infrastructure to understand it.
A data system built for compliance, not insight
India does collect data about its higher education. AISHE remains the most comprehensive source we have, although it depends heavily on institutions’ self-reporting. Given it was last carried out in 2021-22, a fresh AISHE survey is much needed to reflect the changes brought about by Covid-19 and the NEP 2020. The survey itself is around 15 years old; for much of the period before that, there is little continuous, comparable data. NIRF also provides data about universities, but it’s limited to 100 universities in various categories, shaped by ranking logic. Similarly, NAAC captures significant data about various quality indicators and infrastructure measures of universities. Beyond these, household surveys like the NSSO, the Periodic Labour Force Survey, and the CMIE Consumer Pyramid capture higher education-related data only incidentally. They are valuable for understanding citizens, but they cannot substitute for a coherent institutional evidence system.
Much of our current data collection system is a bureaucratic exercise geared towards compliance rather than institutional learning. Universities report numbers not because the information is used or made publicly meaningful but because they are required to. Institutional data about several aspects, such as faculty recruitment, student placements, research outcomes, and financials, remains scattered across individual institution websites locked in PDFs rather than aggregated in any form that enables access, search, and system-level analysis. The data is aggregated at the national or state level in ways that flatten real differences between states and between a central university and a private college in a tier-three town. What is happening inside universities, such as student wellbeing, teaching quality, learning outcomes, research productivity, and managerial efficiency, remains largely invisible.
Other countries’ higher education systems have long recognized that evidence is not optional. The United Kingdom’s Research Excellence Framework distributes nearly £2 billion annually in research funding based on peer review of outputs across every university department. In the United States, the College Scorecard publicly tracks programme-level earnings and student debt, giving prospective students information they can actually use while holding institutions accountable.
Brazil’s CAPES has evaluated graduate programmes on a four-year cycle since 1970s, steadily raising quality. Across these examples, the emphasis is not on data collection alone, but to treat evidence as public infrastructure and to link it to decision-making, accountability, and equity. Indian higher education needs to change to build a data infrastructure that goes beyond mere data collection to generate insights, inform policy and practice.
India does not need to start from scratch
We have built large data systems before. School education already has UDISE+, covering nearly 15 lakh schools, with state-level dashboards and student-level data. Higher education has the building blocks too, such as AISHE, APAAR student IDs, the Academic Bank of Credits and DigiLocker for verified credentials. The challenge is to connect these pieces carefully, with strong privacy protections, consent-based data use and clear limits on surveillance. The aim should not be to create one more reporting burden for universities. It should be to build a trusted decentralized public platform in which state higher education councils can link their own systems and universities can populate their own data, be it about courses, academic credits, student wellbeing and pathways, which would give India a higher education data platform that most countries lack.
The place to start is not all 1,168 universities at once. A practical way to begin is with better-resourced Institutes of National Importance, including IITs, NITs, IISERs, AIIMS, and IIMs, around 200 well-resourced institutions. Once the architecture works for flagships, it can be scaled up to central universities, then to state public universities, and eventually to private institutions. What helps scale up is the centralized structure of higher education governance in India, be it centralized admissions through examinations like CUET, JEE, and NEET, or a common platform of SWAYAM for offering MOOCs, which can make data collection and integration with states and universities easier while also retaining the local and contextual aspects.
Another important question remains: what kind of data do we need? The data collection efforts need to move beyond participation, GER, and infrastructure towards what higher education actually delivers. For every degree from every institution, citizens should be able to find answer to: Did students complete? Do degrees translate into employment, mobility, or meaningful learning? Which institutions serve students well, and which do not? These questions cannot be answered solely through national averages and standardized measures but require contextual and qualitative data that can be shared and standardized over time. This includes data about student experiences related to discrimination, mental health, belonging, campus climate, and academic support, which remain largely invisible within existing frameworks.
Additionally, a set of disclosure requirements, alongside standardized filing requirements applicable to industries, should be put in place that requires universities to make certain types of public-interest data available to the public, including audited financial statements, faculty vacancy data, and standardized placement reports, and public funding usage data. Instead of making these available on university websites, the platform can enable them in a searchable, standardized format that is publicly accessible and available across languages.
Lastly, it is important to create an ecosystem of decentralized actors, such as research think tanks, civil society organizations, state higher education councils, and universities, that are empowered to build on and contribute to the platform. These actors can capture critical local and regional needs that a centralized system cannot. But common standards can make local information nationally visible. We need more actors beyond the limited ones, such as the Foundation for Advancement of Science and Technology, university-based research centres, and philanthropic foundations, to contribute to this data generation and build on the data to inform and shape practice and policy.
Data needs to be used to inform policy and practice and to enhance public engagement with higher education. Quality data and evidence that no one uses changes nothing, and it becomes credible only when multiple actors contribute to, use, and scrutinize it. This is not going to happen on its own if the data is inaccessible. Civil society, journalists, researchers, and parents should have access to open data, enforceable RTI, and standardized disclosures to ask hard questions. Similarly, consulting firms and think tanks can build on the data to produce insightful and thematic reports (for example, EY-Parthenon, in partnership with FICCI, has released several in recent years). India is preparing for a massive expansion of higher education, and the question is whether that expansion will be guided and governed by evidence or largely by intuition, anecdote, and fragmented information.
Dr Nirved Kumar holds a PhD from Ravi J. Matthai Centre for Educational Innovation at IIM Ahmedabad and currently works as a senior project scientist at SoPP, IIT Delhi. Debananda Misra is an Associate Professor at School of Public Policy, IIT Delhi.
Disclaimer: The views expressed are the author’s own and do not reflect the official policy or position of Financial Express.
