Module 1 Discussion Forum: Briefly Describe How Data And Inf
Module 1 Discussion Forumbriefly Describe How Data And Information Di
Briefly describe how data and information differ and relate to each other. How does data quality affect information? Provide one example from your experience or from research where data quality has had an impact on the quality of decision making. 2 to 3 paragraphs.
Paper For Above instruction
Data and information are foundational concepts in the realm of data management and decision-making. Data refers to raw, unprocessed facts and figures without context, such as numbers, dates, or observations collected from various sources. It is essentially the raw input that, when processed and interpreted, becomes meaningful. Information, on the other hand, is data that has been processed, organized, or structured in a way that provides context and relevance, enabling individuals or systems to derive insights for informed decisions. For example, a list of sales figures (data) becomes meaningful (information) when it is aggregated and analyzed to reveal sales trends over time or across regions.
The relationship between data and information is thus cyclical; data serves as the raw material, and through processing, it transforms into useful information. The quality of data critically influences the quality of the resulting information. Poor quality data—such as inaccurate, incomplete, outdated, or inconsistent data—can lead to misleading or erroneous information. Consequently, decision-makers relying on such flawed information may make poor choices, adversely impacting organizational outcomes. An illustrative example is in healthcare, where inaccurate patient data, such as incorrect medication records, can lead to wrong treatment decisions, jeopardizing patient safety. This example underscores how data quality directly affects the reliability of information, emphasizing the need for stringent data validation and accuracy to support effective decision-making processes.
References
- Batini, C., & Scannapieco, M. (2006). Data Quality: Concepts, Methods and Techniques. Springer Science & Business Media.
- Redman, T. C. (2018). Data and Information Quality: The Evolution of Data Quality Management. Annals of Business Analytics, 2(2), 131–137.
- Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33.
- OECD. (2013). Data Quality and Validation. OECD Digital Economy Papers.
- English, L. (1999). Improving Data Quality. New South Wales Department of Education and Training.
- Lee, A., & Strong, D. (2003). When Data Quality Compromises Business Success. Information Management Journal, 37(5), 41-46.
- Kahn, B., Strong, D., & Wang, R. (2002). Information Quality Benchmarks: Product and Service Performance. Communications of the ACM, 45(4), 184-197.
- Anderson, C., & West, B. (2018). Data-Driven Decision Making in Healthcare. Journal of Healthcare Management, 63(2), 134–143.
- Sparkes, M. C., & Clarke, J. (2016). Data Quality Control in Business Analytics. Journal of Data and Information Quality, 8(2), 1-15.
- Fisher, C. (2010). Researching and Writing a Dissertation: An Essential Guide for Business Students. Pearson Education.