Assignment 1 Discussion: Business Intelligence And Knowledge ✓ Solved

Assignment 1 Discussionbusiness Intelligence And Knowledge Managemen

Analyze how companies use business intelligence, knowledge management, and expert systems to process large data sets. Determine whether the increase in data and computing power consistently benefits organizations employing these tools, and discuss the costs associated with this expansion of information available to managers and business users. Evaluate whether more data always equates to better decision-making in the corporate context, providing reasons and examples to support your responses. Additionally, present a different perspective or an opposing viewpoint related to the discussion.

Sample Paper For Above instruction

The revolution in data analytics, driven by advances in business intelligence (BI), knowledge management (KM), and expert systems, has transformed organizational decision-making processes. These technologies enable companies to analyze vast amounts of data to glean actionable insights, streamline operations, and gain competitive advantages (Luhn, 1958; Watwood & Taylor, 2014). However, the benefits of increased data and computational power are not universally advantageous, and organizations must weigh these benefits against associated costs and challenges.

Benefits of Business Intelligence and Knowledge Management

Organizations employing BI and KM tools can uncover hidden patterns, customer preferences, and operational inefficiencies that would otherwise remain obscured in large data repositories (Sharda, Delen, & Turban, 2020). For example, retail giants like Amazon utilize advanced BI to personalize recommendations, enhance inventory management, and improve supply chain efficiency (Mayer-Schönberger & Cukier, 2013). Expert systems, which mimic human decision-making, further enable firms to automate complex tasks, reducing human error and increasing consistency (Turban et al., 2015).

Are Benefits Always Realized?

Despite these advantages, increasing data and processing power do not guarantee better outcomes (Kuk, 2015). Over-reliance on data-driven decisions can lead to information overload, where managers struggle to discern relevant from irrelevant data (Eppler & Mengis, 2004). Moreover, organizations may face diminishing returns as the cost and complexity of managing massive datasets grow. Implementing and maintaining BI systems require significant financial investments, specialized skills, and infrastructure (Gartner, 2020). Poorly managed data can produce misleading insights, resulting in flawed decision-making—highlighting that more data does not necessarily equate to better decisions.

Costs of Expanding Data and Computing Power

The expansion of information availability introduces several costs. First, there are financial costs related to acquiring, implementing, and maintaining BI and knowledge management systems, including hardware, software, and personnel (Holsapple & Joshi, 2003). Second, data privacy and security become critical issues, with organizations vulnerable to breaches that can compromise sensitive information (Kshetri, 2014). Third, there's the organizational cost of change management, as employees must adapt to data-centric decision processes, which may face resistance (Iyengar et al., 2017).

Does More Data Lead to Better Decisions?

The straightforward answer is not necessarily. Quality of data, relevance, and context matter more than quantity (Barton & Court, 2012). For example, a company with accurate, timely, and relevant data can make superior decisions compared to one overwhelmed with irrelevant information. Conversely, excessive or poorly curated data can hinder decision-making, inducing paralysis or misguided actions (Davenport & Harris, 2007). Therefore, organizations must focus on the strategic use of data—collecting what is necessary and ensuring data integrity—rather than just accumulating data indiscriminately.

Opposing Viewpoint

Some scholars argue that overemphasis on data and technology risks diminishing human judgment and creativity in decision-making. While BI tools are powerful, critical thinking, intuition, and contextual understanding remain essential (Drucker, 2007). Over reliance on machines may lead to automation biases, where decision-makers unquestioningly accept system outputs, possibly overlooking ethical considerations or subtle contextual nuances (Sherman & Freis, 2020). Hence, technology should augment, not replace, human judgment.

In conclusion, while advances in data analytics offer significant strategic benefits, their advantages are contingent on proper management, relevant data selection, and responsible implementation. Recognizing potential costs and limitations ensures organizations leverage these tools effectively, avoiding the pitfalls of information overload and decision-making bias.

References

  • Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(10), 78-83.
  • Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business Press.
  • Drucker, P. F. (2007). The effective executive. HarperBusiness.
  • Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization and industrial psychology. The International Journal of Information Management, 24(5), 402-412.
  • Gartner. (2020). Market Guide for Business Intelligence Platforms. Gartner Research.
  • Holsapple, C. W., & Joshi, K. D. (2003). A unified framework for requirement analysis: Integrating different perspectives. Decision Support Systems, 34(3), 259-271.
  • Kkuk, B. (2015). Overcoming data overload in decision making: Strategies and challenges. Journal of Business Analytics, 1(3), 225-236.
  • Kshetri, N. (2014). Big data's impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134-1145.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Eamon Dolan/Houghton Mifflin Harcourt.
  • Sharda, R., Delen, D., & Turban, E. (2020). Business Intelligence, Analytics, and Data Science: A Managerial Perspective. Pearson.
  • Turban, E., Sharda, R., Delen, D., & King, D. (2015). Business Intelligence and Analytics: Systems for Decision Support. Pearson.
  • Watwood, L. E., & Taylor, R. (2014). Applying knowledge management techniques in healthcare. Journal of Healthcare Management, 59(1), 42-55.

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