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Article | Data Intelligence and Informatics Practice | Volume 1 - Issue 1 - 2026 | 32−41 |
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Adoption and Sectoral Impact of Data Science Practices in India: A Cross-Sectional Case Study

Author(s): Karthik Subramanian , Prasanna S. Reddy
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Sahyadri College of Engineering & Management, Sahyadri Campus, Adyar, Mangaluru - 575007, Karnataka, India

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Data Intelligence and Informatics Practice

Volume 1 - Issue 1 - 2026

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Data science has become a defining capability for organisations seeking to convert large volumes of structured and unstructured information into actionable decisions, yet the extent and pattern of its adoption across India's diverse industrial landscape remain incompletely documented. This study reports a cross-sectional survey of 420 organisational respondents drawn from seven sectors—information technology and IT-enabled services (IT/ITES), banking, financial services and insurance (BFSI), healthcare, government and public sector, agriculture and allied industries, retail/e-commerce, and education—across metro and non-metro locations in India. A structured, validated questionnaire was used to capture adoption levels, tools and techniques in use, perceived barriers, and organisational impact. Results showed that 55.0% of organisations had reached at least an intermediate level of data-science adoption, with marked sectoral disparity: IT/ITES and BFSI organisations reported the highest adoption (81.3% and 72.6% intermediate-to-advanced, respectively), while agriculture and allied industries and education lagged considerably (21.3% and 23.4%). Python, SQL and spreadsheet tools dominated the technology stack, while distributed big-data frameworks and natural-language-processing tools remained comparatively underused. Shortage of skilled manpower (74.3%), data-privacy concerns (65.2%) and poor data quality (52.6%) were the most frequently cited barriers. Adopting organisations reported the strongest perceived gains in decision-making (mean = 4.21/5) and operational efficiency (mean = 4.05/5). A five-year retrospective trend indicated accelerating adoption across all sectors between 2020 and 2025, with the gap between leading and lagging sectors widening rather than narrowing. These findings suggest that targeted workforce development, clearer data-governance frameworks and infrastructure investment in lagging sectors are needed to translate India's data-science momentum into more broadly distributed organisational value.

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SUBMITTED: 18 June 2026
ACCEPTED: 31 July 2026
PUBLISHED: 4 August 2026
SUBMITTED to ACCEPTED: 43 days

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Subramanian, K. ., & Reddy, P. S. . (2026). Adoption and Sectoral Impact of Data Science Practices in India: A Cross-Sectional Case Study. Data Intelligence and Informatics Practice, 1(1), 32−41. Retrieved from https://tecnoscientifica.com/journal/diip/article/view/1267
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