Understanding Big Data Analytics (BDA) for Quality Decision Making in the IT Sector – A Systematic Literature Review

Abstract

Big Data Analytics (BDA) has become an innovative influence in the IT sector, allowing companies to make data-driven, high-quality decisions. This dissertation studies the factors impacting BDA adoption and analyzes the primary tools and technologies employed to enhance quality decision-making. The study uses a systematic literature review to integrate findings from previous research, offering an in-depth understanding of the role, constraints, and advantages of BDA.

The study defines key BDA tools, comprising data storage and management systems (e.g. Amazon Redshift, MongoDB), distributed computing frameworks (e.g., Apache Hadoop, Spark), AI-driven analytics instruments (e.g., TensorFlow, Scikit-learn), and business intelligence platforms (e.g., Tableau, Power BI). These technologies allow businesses to manage extensive, complex datasets effectively, derive significant insights, and improve operational efficiency.

In addition to technology, non-technological elements like leadership commitment, budget limitations, workforce competencies, and organizational culture substantially impact BDA implementation. The study indicates that businesses with strong data governance, multidisciplinary teamwork, and investment in staff training are more effective in utilizing analytics. However, challenges including increased implementation costs, a lack of competent staff, and reluctance to change persist as barriers to extensive adoption.