ISM4011 Lesson 6 Assignment

1 Lesson 6 - Assignment Harrison Harding Palm Beach State College ISM 4011-3 Professor McRae September 30, 2023
2 Determining Data Quality Issues Effective data management is a cornerstone of business success. The quality of data, especially its accuracy and completeness, plays a pivotal role in shaping a company's ability to make informed decisions. Reliability hinges on these two fundamental aspects, and subpar data quality can lead to detrimental consequences, including wasted time, resources, and missed opportunities. In the presented case study, data gaps are conspicuous in certain fields, and the origins of such gaps are diverse. The last name poses a potential problem due to its commonality among recipients. If mailings are organized solely by last name, there's a risk of inadvertently switching products, resulting in customers receiving an unintended item. While the street address is correctly provided, the omission of a specific house number introduces a potential roadblock. This incomplete data may lead to the return of mailings to the seller due to insufficient address details. The zip code also presents a further challenge, as both OH and CO states share the same zip code in the data, which differs from the actual situation. Specifically, the zip code for the city of Columbus should be 43085, not 87654, as indicated. Often, data entry errors are the culprits, stemming from human mistakes like typos or overlooked fields. These errors are particularly prevalent when data input relies on manual processes carried out by employees. Another potential source of incomplete data arises from inadequate data collection practices (Schwager & Meyer, 2007), where companies may overlook or fail to capture essential information during customer interactions. To rectify this situation, the company should implement a comprehensive strategy. Firstly, they should institute a robust data entry verification system to ensure accuracy during data input. This involves implementing checks and balances to verify the correctness of data
3 during the entry process. Secondly, they should optimize data collection protocols, ensuring that all required fields are consistently gathered when customers provide their information. Thirdly, the company should leverage data validation tools to validate and cross-verify the accuracy of data stored in their database. These tools act as safeguards to ensure that data is not only complete but also accurate. Fourthly, a data verification system should be established to routinely validate customer data, confirming its accuracy over time. Finally, the company should adopt automated data collection methods, such as web forms or mobile applications, to ensure comprehensive and precise data capture. These measures collectively bolster data accuracy and completeness, enabling the company to make well-informed decisions while minimizing costly errors. Additionally, they enhance customer service by ensuring that clients receive the most current and accurate information available. Ultimately, these steps elevate data quality, enabling the company to base decisions on reliable and complete information. In conclusion, data accuracy and completeness are indispensable facets of data quality. To guarantee these standards, a company should implement a multifaceted approach encompassing data entry verification, thorough data collection, validation tools, data verification systems, and automated data collection methods. Through these steps, the company not only ensures that their decisions are rooted in accurate and complete data but also delivers enhanced service to their customers.
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