How Much Can Business Intelligence And Business Analytics He ✓ Solved

How Much Can Business Intelligence And Business Analytics Help Compani

How much can business intelligence and business analytics help companies refine their business strategy? Explain your answer. Why is selecting a systems development approach an important business decision? Who should participate in the selection process? Your company wants to do more with knowledge management. Describe the steps it should take to develop a knowledge management program and select knowledge management applications.

Paper For Above Instructions

In the contemporary corporate environment, the role of Business Intelligence (BI) and Business Analytics (BA) has become increasingly pivotal in refining business strategies. BI refers to the technologies and strategies used by organizations for data analysis of business information, while BA involves statistical analysis to predict future outcomes based on historical data. Together, these tools help businesses make data-driven decisions, which can significantly enhance their strategic direction.

Firstly, the ability of BI and BA to streamline operations and improve decision-making is crucial. These tools provide insights into market trends, customer preferences, and operational efficiencies, enabling companies to adapt their strategies accordingly. For example, companies can utilize predictive analytics to identify potential market shifts and adjust their plans proactively. This proactive approach minimizes risks and positions companies more favorably against competitors (Davenport & Harris, 2007).

Moreover, these tools facilitate enhanced performance measurement through dashboards and reporting capabilities. Businesses can track key performance indicators (KPIs) in real-time, making it easier to assess the success of strategies and identify areas for improvement (Chen, Chiang, & Storey, 2012). This level of insight allows businesses to be agile, continually refining their strategies in response to fresh data.

Secondly, when it comes to developing systems that support BI and BA, selecting the right systems development approach is critical. The development approach chosen can greatly influence the effectiveness and efficiency of the implemented system. Commonly, organizations may choose between traditional waterfall methods or agile methodologies. Each approach has its strengths and weaknesses, and the decision should consider factors such as project size, complexity, and company culture (Boehm & Turner, 2004).

The selection of a systems development approach is not merely a technical decision; it is a business decision that can impact overall strategy execution. Involving a diverse group of stakeholders in the selection process is essential. This group should include IT personnel, business analysts, and end-users who will interact with the systems daily. Their insights can help determine which approach aligns best with organizational needs (Schmidt et al., 2014). Also, involving executive leadership ensures that the selected approach aligns with broader business objectives.

Transitioning to knowledge management (KM) poses another opportunity for business enhancement. When a company aims to improve its KM, a structured program for development is essential. This program typically follows several key steps: assessing current knowledge assets, defining KM goals, engaging stakeholders, selecting appropriate KM technologies, and implementing the strategy.

To start with, organizations should assess their current knowledge assets. This includes identifying existing content, processes, and tools that facilitate knowledge sharing. Following this assessment, it’s crucial to define clear KM goals that align with the overall business objectives. For instance, an organization might aim to improve collaboration among departments or enhance customer satisfaction through better knowledge sharing.

Next, engaging stakeholders from across the organization ensures that the KM program receives the necessary support and input from those who will be impacted. This may include conducting workshops or surveys to gather feedback on what knowledge management tools or processes would be most beneficial. Once stakeholder input has been gathered, organizations should proceed to select knowledge management applications that fit their needs. Various tools exist—from content management systems to collaboration platforms—and the choice should reflect the previously defined goals and user preferences (Nonaka & Takeuchi, 1995).

Finally, implementing the KM strategy is crucial. This involves not just launching the technology, but also training employees on how to use it effectively, establishing policies for knowledge sharing, and fostering a culture that values continuous learning and collaboration. Monitoring and refining the KM program based on user feedback and changing business needs will help ensure its long-term success.

In conclusion, business intelligence and business analytics are instrumental in refining business strategy through data-driven decisions and performance measurement. The selection of a systems development approach is a vital business decision that benefits from a collaborative and inclusive process. Furthermore, to develop a robust knowledge management program, companies must assess their assets, define goals, engage stakeholders, select appropriate technologies, and effectively implement and refine their strategies.

References

  • Boehm, B., & Turner, R. (2004). Balancing Agility and Discipline: A Guide for the New Software Engineer. Addison-Wesley.
  • Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
  • Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Review Press.
  • Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.
  • Schmidt, R., Lyytinen, K., Keil, M., & Culnan, M. (2014). Identifying Software Project Risks: An International Delphi Study. Journal of Management Information Systems, 18(2), 5-36.
  • Tapscott, D., & Williams, A. D. (2006). Wikinomics: How Mass Collaboration Changes Everything. Portfolio.
  • Wang, W., & Noe, R. A. (2010). Knowledge Sharing: A Review and Directions for Future Research. Human Resource Management Review, 20(2), 115-131.
  • Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1), 107-136.
  • Szulanski, G. (2000). The Process of Knowledge Transfer: A Diachronic Analysis of the Stickiness of Knowledge. Organizational Behavior and Human Decision Processes, 82(1), 9-27.

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