International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
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p-ISSN: 2395-0072
Comparative Analysis of Matrices for Big Data Analytics: A Comprehensive Review Aarti1, Sonali Kapoor2 1Assistant Professor, AIT CSE, Chandigarh University, Punjab, India
2Senior Technical Trainer, AIT CSE, Chandigarh University, Punjab, India
---------------------------------------------------------------------***--------------------------------------------------------------------2. Literature Review Abstract - big data analytics is a powerful tool for extracting valuable insights from vast datasets to inform business decision making. However, the reliability of big data analytics remains a topic of debate. This review paper aims to provide a comprehensive evaluation of the reliability of big data analytics for business decision making by exploring its strengths, limitations, and potential challenges by analyzing the current literature and case studies to identify the factors that affect the reliability of big data analytics and its impact on the decision-making process.
Big Data analytics is a formidable process that entails extracting invaluable insights from extensive and heterogeneous datasets, commonly denoted as Big Data [1]. These datasets encompass structured, semi-structured, and unstructured data generated at high velocity from diverse sources like social media, sensors, devices, and online activities [2]. To tackle the challenges posed by these colossal datasets, Big Data analytics harnesses advanced techniques, tools, and algorithms [3].
Key Words: Big data analytics, Reliability, Business decision making, Data quality, Data bias, Data privacy, Big Data Analytics, Reliability Assessment.
The journey of Big Data analytics comprises several pivotal stages [4][5]. It initiates with data collection, wherein information is amassed from a myriad of sources, spanning transactional databases, social media platforms, web logs, and sensors [6]. Subsequently, the collected data finds its abode in distributed and scalable storage systems, such as the Hadoop Distributed File System (HDFS) or cloud-based storage solutions [7]. To process and metamorphose these massive datasets, technologies like MapReduce, Apache Spark, or other distributed processing frameworks come into play [8][9]. This facilitates effective data analysis and exploration, employing an array of statistical, machine learning, and data mining techniques [10][11]. The insights derived from this analysis are then presented in easily comprehensible formats, such as graphs, charts, or dashboards, aiding decision-makers in grasping the outcomes [12].
1. INTRODUCTION In the digital age, businesses are generating vast volumes of data at an unprecedented rate, leading to the emergence of Big Data analytics as a powerful tool for extracting valuable insights. In the pursuit of data-driven decision-making, organizations have increasingly turned to Big Data analytics to gain a competitive advantage and enhance their strategic capabilities. However, as the reliance on Big Data analytics grows, so does the need to critically assess its reliability and effectiveness for business decision-making. This research paper aims to evaluate the reliability of Big Data analytics in the context of business decision-making, examining its strengths,
Big Data analytics casts its wide net across various industries and domains [13][14]. In the realms of business, finance, healthcare, marketing, manufacturing, and beyond, organizations harness these insights to steer data-driven decisions, enrich product offerings, and augment overall business performance [15][16][17]. As technological advancements unfurl, Big Data analytics is poised to assume an increasingly pivotal role in steering innovation and transformative changes across diverse industries, thereby cementing its stature in the contemporary data-driven landscape [18].
Limitations, and potential impact on organizational outcomes. The proliferation of Big Data has enabled businesses to explore new avenues of growth, optimize processes, and identify customer trends. By harnessing sophisticated data analytics techniques, organizations can uncover patterns and correlations within their data, providing them with valuable insights for informed decision-making. Scholars and practitioners alike have recognized the potential of Big Data analytics as a transformative force in the business landscape [1][3]. Nevertheless, the integration of Big Data analytics into decision-making processes also raises important questions about its reliability and the degree to which it can be trusted to yield accurate and actionable results.
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The fusion of Big Data and Business Intelligence (BI) empowers organizations to gain a competitive edge, streamline processes, and elevate decision-making process
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