An online retailer is receiving customer about receiving different items from what they ordered on the organization's website.
The root cause has been traced to poor data quality.
Despite efforts to clean erroneous data from the system, multiple data quality issues continue to occur.
Which of the following recommendations would be the BEST way to reduce the likelihood of future occurrences?
Click on the arrows to vote for the correct answer
A. B. C. D.A.
The situation described in the question is a common problem in online retail, where customers receive items different from what they ordered. This issue has been traced to poor data quality, indicating that the data used to fulfill orders is incorrect or incomplete.
Despite efforts to clean erroneous data from the system, the organization is still experiencing multiple data quality issues. The question asks which recommendation would be the best way to reduce the likelihood of future occurrences.
Option A recommends implementing business rules to validate employee data entry. This option is a good recommendation as it can help prevent incorrect data from being entered into the system. Business rules can be used to set specific data entry standards and ensure that all entered data meets those standards. For example, the system could be set to validate that product codes match product descriptions, or that customer addresses are complete and correctly formatted.
Option B suggests investing in additional employee training for data entry. This option is also a good recommendation, as it can improve employee awareness and skills in data entry. With proper training, employees will understand the importance of data quality and how to enter data accurately. However, training alone may not address all data quality issues, especially if the root cause is systemic.
Option C assigns responsibility for improving data quality. This option is a vague recommendation that does not specify how to improve data quality. While assigning responsibility is important, it may not be enough to solve the problem without clear direction and action plans.
Option D recommends outsourcing data cleansing activities to reliable third parties. This option may not be the best recommendation, as it does not address the root cause of the problem. Outsourcing data cleansing can be expensive and may not address the underlying issues that lead to poor data quality in the first place.
In conclusion, the best recommendation to reduce the likelihood of future occurrences of data quality issues would be to implement business rules to validate employee data entry. This option can help prevent incorrect data from being entered into the system, and coupled with training can result in long term improvements in data quality.