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
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