IT Emerging Technologies Review Paper: A Literature Review

IT Emerging Technologies Review Paper: A Literature Review Approach

CPSC 531 Advanced Database Management spring 2015 individual written assignment #2 requires selecting and reviewing a current emerging IT technology, focusing on its concepts, applications, limitations, and future directions. The paper should be a scholarly literature review that discusses how the technology works, its practical adoption, applications, shortcomings, and trends. It must include an introduction, literature review, discussion of advantages and disadvantages, and conclusions with implications, supported by at least six credible sources formatted in APA style.

Paper For Above instruction

The rapid evolution of information technology (IT) has led to numerous emerging technologies that are transforming the landscape of data management, security, and analytics. In this paper, we explore a selected emerging IT technology—Big Data Analytics—to understand its fundamental concepts, current applications, limitations, and trajectory. A comprehensive literature review is conducted to synthesize current research, theoretical frameworks, and practical implications, providing insights into how this technology influences various domains and what future developments it may entail.

Introduction

Big Data Analytics (BDA) has become a critical component of modern data-driven decision-making processes profoundly impacting industries such as healthcare, finance, retail, and manufacturing. Defined broadly, BDA refers to the methodologies, architectures, and tools used to analyze vast, complex datasets that traditional data-processing software cannot handle efficiently (Chen, Mao, & Liu, 2014). The significance of BDA lies in its capacity to extract valuable insights from large-scale data, enabling organizations to enhance operational efficiency, improve customer experiences, and foster innovation. Given the exponential growth of data volumes, understanding BDA’s concepts, current state, and future trends is essential for researchers and practitioners alike.

Literature Review

Fundamentally, Big Data Analytics encompasses various techniques such as machine learning, data mining, statistical analysis, and text analytics, supported by scalable architectures like Hadoop and Spark (Zikopoulos et al., 2012). Nominal definitions of BDA emphasize its role in processing data characterized by Volume, Variety, and Velocity—commonly referred to as the 3Vs (Laney, 2001). Operational definitions of BDA involve specific platforms and methodologies used for data ingestion, storage, processing, and visualization (Gandomi & Haider, 2015). The relationship among these concepts is mediated by technological architectures that enable real-time or batch processing of data streams, fostering actionable insights (Katal et al., 2013). However, critics highlight limitations such as data privacy concerns, high infrastructure costs, and challenges in data quality management (Madden, 2012). Current research focuses on integrating BDA with artificial intelligence (AI), Internet of Things (IoT), and cloud computing to enhance scalability and real-time analytics (Chen et al., 2014). The future direction involves developing more intuitive, cost-effective, and privacy-preserving data analytics tools, along with advancing predictive modeling and automation capabilities (Manyika et al., 2011).

Discussion

The advantages of Big Data Analytics include improved decision-making accuracy, enhanced operational efficiency, and the ability to identify new market opportunities. For example, in healthcare, BDA facilitates personalized medicine through analysis of genomic data (Jiang et al., 2017). In retail, it enables targeted marketing and inventory optimization (Wamba et al., 2015). Nonetheless, disadvantages include significant infrastructural investments, complexities in managing data privacy and security, and the potential for biased insights if data quality is compromised (Katal et al., 2013). The technology’s impact extends into various sectors, fostering innovation but also raising ethical and legal concerns about data ownership and consent. Ongoing issues revolve around scalability, interoperability among diverse data sources, and ensuring transparency in algorithms used for decision-making (Mayer-Schönberger & Cukier, 2013). With advancements in cloud computing and AI, future applications are poised to become more accessible, automated, and capable of delivering real-time insights, pushing the boundaries of what organizations can achieve with data (Manyika et al., 2011).

Conclusion and Implications

Big Data Analytics holds transformative potential for multiple industries, promising enhanced efficiency, innovation, and strategic advantage. Its future lies in developing more manageable, secure, and intelligent systems capable of processing diverse datasets efficiently. Practitioners should stay abreast of technological advancements, particularly in AI integration and privacy-preserving techniques, to maximize benefits while mitigating risks. Continued research and development are necessary to address current limitations, including data privacy, cost, and complexity. As organizations increasingly adopt BDA, understanding its capabilities and challenges becomes crucial for making informed decisions about technology investments and policy regulations.

References

  • Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., et al. (2017). Artificial intelligence in healthcare: Past, present, and future. Stroke and Cerebrovascular Diseases, 26(4), 261-271.
  • Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools, and technology. In 2013 6th International Conference on Contemporary Computing (IC3) (pp. 404-409). IEEE.
  • Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group Research Note, 6(3), 1-4.
  • Madden, S. (2012). From databases to big data. IEEE Internet Computing, 16(3), 4-6.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt.
  • Manyika, J., Chui, M., Brown, B., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
  • Wamba, S. F., Akter, S., Edwards, A., et al. (2015). Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities. Journal of Business Research, 70, 356-365.
  • Zikopoulos, P., Eaton, C., deRoos, D., et al. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media.

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