Fortifying Data Integrity using Holistic Approach to Master Data Management and Cybersecurity Safeguarding

Pansara, Ronak Ravjibhai; Vaddadi, Srinivas Aditya; Vallabhaneni, Rohith; Alam, Naved; Khosla, Balaram Yadav; Whig, Pawan

doi:10.23919/indiacom61295.2024.10498671

Cited by 3 publications

References 10 publications

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The Complexity of Competitive Intelligence in the Age of data ambiguity and Artificial Intelligence

Cekuls

2024

JISIB

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The aim of the study is to explore the development aspects of competitive intelligence (CI) in terms of the challenges posed by the increase in data volume, incl. unverified data, data reliability, and the integration of artificial intelligence (AI) into data analysis processes. The primary research question explores how the role of the human factor has become increasingly important in ensuring the accuracy and reliability of data used by AI, especially in an era dominated by AI and rapid information dissemination. The study highlights the imperative role of human judgment in the era of AI-driven data analysis, highlighting skills, competencies, and authority as critical factors in evaluating data processing outcomes. This points to the risks associated with the uncritical adoption of AI-generated solutions, which can lead to innovative but impractical outcomes that consume significant organizational resources. Furthermore, the study calls for a balanced approach to integrating AI into CI processes, supporting strategies that enhance the synergy between human analytical prowess and AI computational efficiency. This approach is critical to overcoming the challenges posed by data reliability and ensuring the effective implementation of CI strategies that are both innovative and grounded in reality.

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The Complexity of Competitive Intelligence in the Age of data ambiguity and Artificial Intelligence

Cekuls

2024

JISIB

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Data Science for Personalized Nutrition Harnessing Big Data for Tailored Dietary Recommendations

Gami,

Dhamodharan,

Dutta

et al. 2024

Advances in Medical Diagnosis, Treatment, and Care

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The advent of data science has revolutionized various sectors, with personalized nutrition emerging as a significant beneficiary. This chapter explores the integration of data science techniques in developing tailored dietary recommendations. By leveraging big data, including genomic, phenotypic, and lifestyle information, personalized nutrition aims to optimize health and prevent diseases. The authors delve into the methodologies for data collection, analysis, and interpretation, highlighting the role of machine learning algorithms in predicting individual nutritional needs. Case studies and real-world applications are discussed to illustrate the practical benefits and challenges of implementing personalized nutrition strategies. This chapter aims to provide a comprehensive understanding of how data science can transform nutrition into a highly individualized and effective approach to health and well-being.

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Cybersecurity in the Digital Era: A Comprehensive Framework for Safeguarding Data Integrity, Privacy and Critical Infrastructures Against Evolving Threats

Harsh C Vachheta,

Ishita Pawar,

Ketan Girish Hukare

et al. 2024

IJARSCT

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In an increasingly digital world, the transfer of data has become seamless yet the growing reliance on technology has amplified cyber threats endangering sensitive information. Existing cybersecurity measures often fail to keep pace with the dynamic nature of cyberattacks, particularly in areas like cloud computing, the Internet of Things (IoT)and online financial transactions. This highlights critical gaps in safeguarding data, user awareness and incident management strategies. This research paper proposes a comprehensive cybersecurity framework that addresses these gaps by integrating risk management, secure configurations, user privilege control and advanced incident response mechanisms. Emphasizing critical infrastructures and emerging technologies, the study explores preventive measures, user education and advanced monitoring solutions. By bridging the gaps in current security practices, the study aims to enhance data integrity, privacy and availability offering actionable solutions to reduce vulnerabilities and foster a resilient digital environment for individuals, organizations and governments

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Fortifying Data Integrity using Holistic Approach to Master Data Management and Cybersecurity Safeguarding

Cited by 3 publications

References 10 publications

The Complexity of Competitive Intelligence in the Age of data ambiguity and Artificial Intelligence

The Complexity of Competitive Intelligence in the Age of data ambiguity and Artificial Intelligence

Data Science for Personalized Nutrition Harnessing Big Data for Tailored Dietary Recommendations

Cybersecurity in the Digital Era: A Comprehensive Framework for Safeguarding Data Integrity, Privacy and Critical Infrastructures Against Evolving Threats

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