Ashutosh Prasad Maurya

Work place: Delhi School of Management, Delhi Technological University, New Delhi, India

E-mail: ashurya@gmail.com

Website: https://orcid.org/0009-0005-9868-9655

Research Interests:

Biography

Ashutosh Prasad Maurya, Doctoral student in Delhi School of Management, Delhi Technological University, New Delhi. His research areas are e-governance, analytics and information system. He has diversified work experience of more than 20 years in the area of e-governance and technology management. He has worked with national level Government and Public Sector Organizations. He is having expertise in the area of project execution and applications of analytics under e-governance across multiple domains.

Author Articles
Enabling Data-Driven Governance through Collective Analytics: Challenges and Framework for Indian E-Governance

By Ashutosh Prasad Maurya Pradeep Kumar Suri

DOI: https://doi.org/10.5815/ijieeb.2026.04.01, Pub. Date: 8 Aug. 2026

The demand for data-driven insights in government has highlighted the importance of collective analytics. This study attempts to explore the key challenges of collective analytics in the context of Indian e-governance and the framework for addressing them. The study is based on a literature review, references to two cases, and expert views obtained from professionals involved with analytics solutions in government. In this study, analytics projects are considered as dashboard-based analytics. Based on the content analysis of expert responses, 14 key challenges of collective analytics in e-governance have been identified. The novelty of the present study is the focused exploration of challenges and their framework related to collective analytics in e-governance-a topic that received limited attention in the extant literature. This study brings forth the fact that unless the challenges of collective analytics in e-governance, including those related to data visualization, data quality, capacity building, technological capabilities, and inter-agency communications, are recognized, the implementation of collective analytics can be challenging. This study provides the basic understanding needed for data-driven governance through collective analytics. The output of the study will be helpful to the managers, e-governance experts, academicians, planners, and policymakers to understand the dynamics of collective analytics in government for handling discussed challenges well in advance. This study will also helpful to reduce the cost and time of the collective analytics project for effective decision-making.

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