Question.4167 - Review the Case Study in Chapter 1: Do You Trust Your Data? After reviewing the case, answer the following questions. Be sure to use outside resources and your textbook to validate your responses. Why do you believe that data can be inaccurate? What can a business do to ensure data is correct? Explain how bad data will impact information, business intelligence, and knowledge. Have you ever made a decision based on bad data? If so, please share how you could have verified the data quality. Argue for or against the following statement "It is better to make a business decision with bad data than with no data". This assignment should be written using APA format and should include a title page, headings, conclusion, and references. Don't forget to submit your assignment for grading.
Answer Below:
Introduction This assignment will evaluate the case study considering how data can be trusted or how the association of bad data may affect the overall knowledge. In nexus with this, this case study from Chapter One focuses on how data drives fact-based decisions, especially with the role of manager where one needs to rely on the data in order to drive the business decision. Hence, with this evaluation of bad data and decision-making properties in bad data across the assignment. Why do you Believe Data Can be Inaccurate? Data can be inaccurate due to several factors including human error, incomplete data, outdated information, data integration errors, and lack of standardization. In nexus with this, mistakes in data entry can occur through various points such as typos, omissions, or incorrect formulas; for instance, as seen in the London Olympics and University of Toledo cases (Baltzan, 2020). The case study highlights a typo in the spreadsheet formula that led to an overestimation of enrollment with overinflated revenue of approximately 2.5 million USD. On the other hand, missing values or incomplete datasets can even affect the results and may lead to incorrect conclusions as evident in a missing negative sign on a dividend report costing the financial company a loss of $2.6 billion (Baltzan, 2020). Similarly, using old data or inconsistent data collection methods may create discrepancies leading to inaccurate or bad data. What can a Business Do to Ensure Data is Correct? In order to ensure that the data collected is correct a business needs to focus on data validation, quality control processes, training employees, automated tools, standardization, and backups and version control (Ngcobo et al., 2024). Similarly, implementing validation rules at the point of entry may focus on catching errors early where regular audits, data cleansing, and integrity checks can help in maintaining accuracy in data collection. On the other hand, training the employees with proper data management and entry with appropriate utilization of data management software may focus on error detection features. However, it would be even essential to maintain historical versions of the collected data for cross-verification in the long term. Explain How Bad Data will Impact Information, Business Intelligence, and Knowledge In consideration of the bad data, it affects the information generated from the raw data leading to misleading summaries, reports, and dashboards. On the other hand, Business intelligence relies mainly on the process in order to generate insights into the report; however, if the data has been flawed, the overall insights will be unreliable, leading to poor strategic decisions (Gade, 2024). Similarly, knowledge has been built on patterns and insights derived from information where bad data undermines the foundation of organizational learning and strategic planning. Have You Ever Made a Decision Based on Bad Data? In various instances, bad data decisions can be evident when a manager may overestimate staff availability based on outdated shift data. This incident has happened several times, leading to understaffing during peak hours. In order to prevent this, verifying data accuracy through regular updates and cross-checking the attendance records would have been essential. Argue for or Against the Statement: “It is Better to Make a Business Decision with Bad Data Than with No Data” In accordance with Peng et al. (2022), decision-making with bad data can be very harmful since it can generate an illusion of confidence, compounding errors, financial loss, and reputation damage. For instance, decisions based on bad data insights may trigger a chain of poor strategic moves where bad data can create the illusion of certainty, leading to misguided action. Similarly, incorrect data in business decisions can damage stakeholder trust damaging brand credibility and resulting in massive financial loss. On the contrary, no data encourages caution, and in this scenario prompting further investigation and more strategic thinking rather than hasty decisions. Conclusion Hence, it can be concluded that data accuracy is critical for effective decision-making where businesses need to prioritize data quality management through validation, standardization, and employee training in order to avoid costly mistakes. In this scenario, bad data as referred to in the Case Study in Chapter One may lead to poor decisions, financial losses, and damage to the organizational reputation, while accurate data essentially provides a foundation for sound strategic choices. References Baltzan, P. (2020). Business driven technology. McGraw-Hill. Gade, K. R. (2024). Data Quality Metrics for the Modern Enterprise: A Data Analytics Perspective. MZ Journal of Artificial Intelligence, 1(1). Ngcobo, K., Bhengu, S., Mudau, A., Thango, B., & Lerato, M. (2024). Enterprise data management: Types, sources, and real-time applications to enhance business performance-a systematic review. Systematic Review| September. Peng, Z., Ye, K., & Li, J. (2022). Break the Cycle of Collusion: Simulation to Influence Mechanism of Cognitive Bias on To-Collude Decision Making. Buildings, 12(
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