DM5 Assignment 1 Discussion: Techniques And Tools For Managi

Dm5 Assignment 1 Discussiontechniques And Tools For Managing The Dat

DM5-Assignment 1: Discussion—Techniques and Tools for Managing the Data You have explored many options for managing data as well as its importance to the overall health of an organization in making well-informed decisions. Many organizations feel that they have to utilize powerful and expensive solutions, but there are also cheaper alternatives. For example, MS Excel can be a great tool to manage data and identify answers to any questions. Thus, whether your organization is big or small, all the tools need to be evaluated to determine the one that will work best, not only in managing the data but also in lowering the overall cost. Managing cost is important, as you do not want to implement a solution that will bankrupt the organization; that, in itself, is an ill-informed decision.

Using the Argosy University online library resources and the Internet, research tools and techniques of managing data. Respond to the following: What would be some of the mistakes or consequences of not investigating the costs associated with the organization’s information systems (data collection) choice? Besides going bankrupt, what other effects could it have on the organization? Could it lead to bad decision making? Explain.

What systems does your organization utilize, either as a whole or per department? Is this solution effective? Why or why not? Is there a solution that would be more effective? If not, explain why.

With the various solutions available today, which one do you think would work best for you? Meaning, which of these solutions (such as MS Excel or a decision-support system) would work best on the following criteria: Ease of use, Interpretation of data, Sharing of data. Often, we think of business analytics as only for businesses. However, can any of these tools be used for personal decision making? Provide some examples of how you could utilize these tools. Write your initial response in 300 words.

Apply APA standards to citation of sources. By Saturday, December 5, 2015, post your response to the appropriate Discussion Area. Through Wednesday, December 9, 2015, review and comment on at least two peers’ responses. Consider the following in your response: Provide a statement of clarification or a point of view with rationale. Challenge a point of discussion or draw a relationship between one or more points of the discussion.

Paper For Above instruction

Effective management of organizational data relies heavily on selecting appropriate tools and techniques that balance functionality and cost-efficiency. Failure to investigate the costs associated with data collection systems can have significant adverse effects beyond immediate financial strain. One of the most critical consequences is the potential for poor decision-making, which can result from utilizing inadequate or improperly evaluated data tools, leading to misguided strategic initiatives or operational inefficiencies.

Organizations that neglect thorough cost analysis in their data systems risk implementing solutions that either underperform or are unnecessarily expensive. For instance, opting for high-cost enterprise systems without assessing their benefits versus costs could strain financial resources, particularly in small or resource-constrained organizations. Conversely, choosing overly simplistic tools like spreadsheets without understanding their limitations might hinder data accuracy or limit collaborative efforts essential for sound decision-making.

Beyond financial concerns, inadequate evaluation might lead to data silos, inconsistent data, or security vulnerabilities. These issues compromise data integrity and operational efficiency, increasing the risk of compliance violations or data breaches. Such failures impact organizational reputation and stakeholder trust, which are critical for long-term success.

In my organization, we utilize a combination of enterprise resource planning (ERP) systems and specialized departmental tools. While these solutions often serve their purpose, their effectiveness varies depending on implementation and user training. For example, an ERP system may streamline processes but can be cumbersome if not properly tailored or if employees are not adequately trained. Sometimes, these systems lack flexibility, leading to bottlenecks or data-entry errors, undermining their overall effectiveness.

A more effective solution might involve integrating more user-friendly decision-support tools or adopting cloud-based platforms that facilitate real-time data sharing and collaboration. These systems can improve responsiveness and decision agility, especially in fast-paced environments. However, costs, security concerns, and organizational readiness must be carefully evaluated before transitioning.

Considering personal and organizational needs, MS Excel remains a versatile and accessible tool. Its simplicity, combined with advanced functions like pivot tables and data analysis add-ins, makes it suitable for individual use. For organizational data interpretation and sharing, decision-support systems or Business Intelligence (BI) platforms such as Tableau or Power BI are preferable, given their ease of use, visual interpretation capabilities, and collaborative features.

Interestingly, business analytic tools are not limited to organizational use. Individuals can leverage these tools for personal decision-making, such as budgeting, investment analysis, or tracking health metrics. For example, Excel can help create detailed budgets or track personal expenses, while BI tools like Power BI can visualize investment performance or health data trends, enabling informed lifestyle choices.

References

• Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
• Laudon, K. C., & Traver, C. G. (2021). E-commerce 2021: Business, technology, society (15th ed.). Pearson.
• Marr, B. (2018). Data-driven HR: How to use analytics and metrics to drive performance. Kogan Page.
• Power, D. J. (2013). What is a decision support system? Information Systems, 8(4), 309-319.
• Sharma, A., & Sharma, S. (2019). Big data analytics in healthcare. Journal of Healthcare Engineering, 2019, 1-14.
• Turban, E., Sharda, R., & Delen, D. (2018). Decision support and business intelligence (10th ed.). Pearson.
• Wixom, B. H., & Watson, H. J. (2019). The current state of business intelligence. Computer, 52(2), 96-99.
• Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2019). Business research methods (10th ed.). Cengage.
• Rouse, M. (2014). Business Intelligence (BI). TechTarget. https://searchcio.techtarget.com/definition/business-intelligence-bi
• Davenport, T. H. (2013). Analytics at Work: Smarter Decisions, Better Results. Harvard Business Review Press.