NIRF Data Analysis
- TLR, RPC, GO, OI and Perception
- Submitted vs accepted data review
- Category-wise scores for each institution
Data-Driven N⁴ Success
EDUCATION GROUP · WEST BENGAL
Six institutions, one NIRF strategy
How Paradigm used parameter-level analytics and peer benchmarking to lift the NIRF rankings of six JIS Group institutions across engineering, university, pharmacy and dental categories.
6
JIS Group institutions in one engagement
4
NIRF categories: Engineering, University, Pharmacy, Dental
5
NIRF parameters analysed down to sub-parameter level
2026–29
Multi-cycle support, from NIRF 2026 to NIRF 2029
The challenge
A large group, ranked below its scale
Eastern India's largest education group, with 30 institutes and 37,000+ students, wanted its NIRF results to reflect the quality of its colleges and university.
Our approach
Analytics first, institution by institution
Subject experts, a data analyst and a chartered accountant used our predictive model and peer-group micro-analysis to find each institution's highest-weightage gaps.
The impact
Rankings up across the group
JIS Group institutions improved their NIRF rankings, with an action plan and data practices in place for the next three cycles.
What we delivered
Four workstreams, six institutions
01
Gap and Peer Benchmarking
- Gaps against top-ranked institutions
- Peer-group comparison by category
- Predictive rank modelling
Weightage-Led Recommendations
- Focus on highest-impact criteria
- Better data presentation
- Feasible fixes for lagging areas
Implementation and Filing
- Action plans with owners and resources
- Periodic data reviews
- Data-table support before each submission
Outcome: NIRF ranking improvement across JIS Group institutions
NIRF 2026
The journey
From analysis to multi-year gains
December 2025
NIRF 2025 data analysed
January 2026
Recommendations and data support
NIRF 2026
Rankings improved
2026–2028
Annual analysis and filing support
NIRF 2029
Sustained gains across the group
Paradigm analysed each of our institutions parameter by parameter and showed us where marks were being lost. Their recommendations and data support helped lift our NIRF rankings across the group.
Are your NIRF ranks reflecting your real strengths?
For a single institution or a whole group, our predictive model and NIRF experts find the marks you are missing.