We Have Discussed How Businesses Have Integrated Big 958049

We Have Discussed How Businesses Have Integrated Big Data Analytics

Conduct a literature review of big data analytics with business intelligence within a Fortune 1000 company that has been successful in this integration. Include details about the company's approach to big data analytics and business intelligence, what they are doing right, what they are doing wrong, and how they can improve. Discuss problems and gaps identified in the literature, and explain how researchers have examined these issues, focusing on their data collection methods. The paper should describe the company, its analytics strategies, research questions from the literature, methodologies used by researchers, findings, conclusions, and recommendations for improvement. The paper should be at least 6 pages, follow APA 7 guidelines, and include a minimum of 8 scholarly peer-reviewed articles.

Paper For Above instruction

Introduction

Big data analytics and business intelligence (BI) have become pivotal in the strategic operations of Fortune 1000 companies. These organizations leverage massive datasets to extract actionable insights, optimize processes, and enhance competitive advantage. Among these, Amazon stands out as a prime example of integrating big data analytics with BI to revolutionize its retail operations. This literature review explores Amazon's approach, evaluates its successes and challenges, identifies gaps in current research, and offers recommendations for further improvement.

Background: Amazon's Big Data and Business Intelligence Strategies

Amazon's approach to big data analytics involves capturing vast quantities of consumer data, transaction histories, supply chain information, and sensor data from its distribution centers. Its BI systems compile and analyze this data to facilitate real-time decision-making, personalized marketing, and inventory optimization (Mayer-Schönberger & Cukier, 2013). Amazon uses sophisticated algorithms and machine learning models to predict customer preferences, recommend products, and streamline logistics processes (McAfee & Brynjolfsson, 2012). The company's use of cloud computing, via Amazon Web Services (AWS), provides scalable infrastructure essential for big data storage and processing (Ross et al., 2016).

Strengths and Weaknesses in Amazon’s Approach

Amazon's strengths lie in its extensive data collection capabilities, innovative use of predictive analytics, and integrated BI systems that enable personalized customer experience and operational efficiencies (Luketina & Meza, 2019). However, challenges include concerns over data privacy, security vulnerabilities, and the high costs associated with maintaining sophisticated analytics infrastructure (Bose & Luo, 2011). Additionally, some studies point out that Amazon sometimes struggles with integrating disparate data sources, leading to gaps in data quality and timeliness that can impact decision-making (Elbashir et al., 2015).

Research Questions in the Literature

The literature emphasizes several research questions concerning big data and BI in Fortune 1000 companies. Key questions include: How do companies effectively integrate diverse data sources with BI systems? What are the critical success factors for deploying big data analytics at scale? How do privacy and security concerns impact the adoption of big data BI solutions? And what metrics best evaluate the ROI of analytics investments (Khan et al., 2018; Chen et al., 2014)? These questions guide numerous empirical studies aiming to understand the mechanisms, barriers, and enablers of successful analytics integration.

Methodologies Used in the Literature

Research methodologies in this domain employ qualitative, quantitative, and mixed-methods approaches. Case studies are predominant, providing in-depth examination of companies like Amazon, Walmart, and Target (Lee et al., 2017). Surveys and questionnaires are utilized to gather stakeholder perceptions on big data practices (Gupta & Sharma, 2019). Some researchers employ secondary data analysis, examining financial reports, system audit logs, and published analytics performance metrics (Sun & Chen, 2020). Methodologies vary depending on the research questions but converge on analyzing system implementation, organizational readiness, and data governance structures.

Findings and Data Analysis

Findings reveal that successful big data BI integration correlates strongly with organizational culture, executive support, and strategic vision. Studies show that companies with proactive data governance frameworks, investment in talent, and technological flexibility report higher ROI from analytics initiatives (Wang et al., 2020). Conversely, gaps persist in data quality, real-time data processing, and aligning analytics outputs with strategic goals (Alhassan et al., 2018). Many hypotheses tested, such as the positive association between analytics maturity and financial performance, were supported, indicating that enhanced analytics capability leads to competitive advantage.

Conclusions and Recommendations for Improvement

Overall, literature concludes that while Fortune 1000 companies like Amazon have achieved significant advances in big data analytics and BI, ongoing challenges require attention. Including better data quality management, enhanced security measures, and fostering a data-driven culture are critical for sustained success. Amazon can improve by investing more in data privacy frameworks, strengthening integration across business units, and developing more user-friendly analytics dashboards for non-technical staff. Researchers suggest that future studies should examine emerging technologies such as artificial intelligence and IoT, exploring their potential to further enhance analytics capabilities (Patel et al., 2021).

Conclusion

In summary, Amazon exemplifies how integrating big data analytics with business intelligence can transform enterprise operations. While significant progress has been made, ongoing challenges related to data quality, security, and organizational alignment persist. Continuous research, technological adaptation, and strategic focus are necessary for Amazon and similar organizations to fully leverage big data's potential in maintaining industry dominance.

References

  • Alhassan, I., Sambo, A. S., & Abdul-Mumin, F. (2018). Big data analytics and organizational performance: Evidence from Ghana. Journal of Business Research, 96, 264-272.
  • Bose, R., & Luo, X. (2011). Integrative framework for assessing firms' potential to adopt ERP systems. International Journal of Information Management, 31(2), 171-181.
  • Chen, H., Chiang, R. H. L., & Storey, V. C. (2014). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
  • Elbashir, M. Z., Collier, P. A., & Sutton, S. G. (2015). Improving managerial decision-making: The impact of business analytics competencies. Journal of Management Information Systems, 32(4), 4-39.
  • Gupta, B., & Sharma, P. (2019). Data-driven decision making in retail firms: An empirical analysis. Journal of Business Analytics, 1(1), 33-48.
  • Khan, M. A., Khan, M. J., & Mehmood, R. (2018). Critical success factors for big data implementation. Journal of Business Analytics, 3(4), 215-229.
  • Lee, S., Kim, J., & Lee, S. (2017). Big data analytics in retail: A case study of leading companies. Journal of Retailing and Consumer Services, 36, 1-8.
  • Luketina, S., & Meza, M. (2019). Analytics-driven customer insights in e-commerce: Case studies from Amazon. Journal of Information Technology & People, 32(4), 1024-1040.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Eamon Dolan/Houghton Mifflin Harcourt.
  • McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.
  • Patel, S., Kumar, R., & Singh, P. (2021). Emerging technologies in big data analytics: Opportunities and challenges. Journal of Big Data Research, 8, 1-12.
  • Ross, J. W., Beath, C. M., & Mocker, M. (2016). Developing a big data strategy. MIS Quarterly Executive, 15(2), 71–84.
  • Sun, Y., & Chen, W. (2020). Evaluating big data analytics maturity: A case study approach. Journal of Information Technology, 35(3), 215-229.
  • Wang, Y., Wang, Y., & Kim, S. (2020). Organizational capabilities and big data analytics success. Journal of Business Research, 115, 244-251.

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