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Informative Article | Management | India | Volume 9 Issue 10, October 2020 | Popularity: 5.5 / 10

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Strategies for Building Resilience in Analytics Environments

Paraskumar Patel

Abstract: This paper addresses the critical role of resilience in analytics environments amidst the challenges of digital transformation. With the growing dependency on analytics for business intelligence, operational efficiency, and competitive advantage, the complexity and vulnerability of these systems to disruptions such as data breaches, cyber-attacks, and natural disasters have significantly increased. This study explores the concept of resilience within analytics environments, emphasizing the necessity of incorporating resilience not as an option but as an essential strategy for sustainable operations and the protection of critical data assets. Drawing on existing literature, theoretical models, and case studies, the paper provides a comprehensive overview of strategies across technical, organizational, and policy domains to enhance resilience. The research highlights the importance of adaptive, absorptive, and transformative capacities in analytics systems and proposes metrics and indicators for assessing resilience. Through a detailed examination of challenges and strategic interventions, the paper offers insights into developing analytics environments that are robust, adaptable, and secure against an array of global challenges. A case study of a healthcare technology company underscores the practical application of resilience strategies in improving data reporting processes and patient care. The paper concludes with future research directions, advocating for a holistic approach that integrates technological innovation, organizational strategy, and policy framework to pursue resilience, ensuring that analytics environments can navigate and thrive in the unpredictable digital transformation landscape.

Keywords: Resilience Strategies, Analytics Environments, Regulatory Compliance, Resilience, Disaster Recovery Planning, Sustainability in Analytics

Edition: Volume 9 Issue 10, October 2020

Pages: 1779 - 1785

DOI: https://www.doi.org/10.21275/SR24321121137

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