Abstract
In the era of globalization, industries are undergoing a rapid digital transformation, driven by big data ecosystems, conducting data intensive operations and interconnected technologies, generating huge data by means of IoT devices, AI systems, social media, Edge processing, smart systems and autonomous machines, all are designed to collect, process and transmit multi-modal data continuously and at a high rate. Even though these innovations enhance overall efficacy, productivity and competitiveness, they also present significant unavoidable data privacy challenges that threaten data breaches, operational continuity and regulatory compliance.
Bearing in mind the criticality of the topic, this study targets to identify and evaluate the most critical big data privacy challenges, analyses their probability of occurrence, contextual relevance to the aviation and consequential impact on industrial operations. Although this research is industry-agnostic; however, illustrative examples were drawn from the aviation industry perspective to demonstrate applicability.
A combined approach of systematic literature review and risk assessment frameworks is used to examine mention challenges. The research highlights prominent challenges related to data privacy within the big data ecosystem, that can lead to multiple severe consequences like brand damage, loss of trust, business disruptions, financial losses and other undesirable outcomes. Finally, this study presents actionable workarounds to overcome existing challenges in the domain of big data privacy, followed by potential directions for further research.
Read My Full Article: https://doi.org/10.70844/ijas.2025.2.42
For further reading
- Big Data – Metrics
- Big Data – Key Cross-Functional Roles
- Big Data – Readiness and Risk Assessment
- Big Data – Visualization Tools
- Big Data – Cloud Solutions and Statistical Computing
- Big Data – Distributed File-Based Databases
- Big Data – MPP Shared-nothing Technologies and Architecture
- Big Data Strategy- Activities
- Sentiment Analysis and Data/Text Mining
- Machine Learning
- Services-Based Architecture – SBA
- Data Lake
- What is Big Data: A Comprehensive Overview
- Data Science – Process and Iterative Phases
- Data Science – Dependency
- Big Data and Data Science – A Glance
- Data Quality – Policy and Metrics
