Which is NOT a suitable method for assuring the quality of data collected by a third-party company?
Which is NOT a suitable method for assuring the quality of data collected by a third-party company?
Introducing erroneous data to see if it is detected is not a suitable method for assuring the quality of data collected by a third-party company. This method introduces errors intentionally, which can complicate the data validation process and potentially contaminate the dataset. More effective and reliable methods include verifying the accuracy of the data by contacting users, validating the company's data collection procedures, and tracking changes to data through auditing.
The question asks which is NOT the correct approach. A is correct, C is NOT correct so the answer is C
Why not "B" " Introducing erroneous data to see if its detected."?
A is correct then
C guys
C. Introducing erroneous data to see if it's detected. Introducing erroneous data to see if it's detected is not a suitable method for assuring the quality of data collected by a third-party company. This approach is likely to introduce additional errors into the data and may not provide a reliable measure of the quality of the data. Suitable methods for assuring the quality of data collected by a third-party company include verifying the accuracy of the data by contacting users, validating the company's data collection procedures, and tracking changes to data through auditing. These methods can help to ensure that the data collected is accurate, complete, and reliable, which is important for making informed decisions based on that data.
A is absurd and would be a privacy violation
C is NOT correct, see page 63 book Privacy in Technology by Cannon
The answer is C. The method that is NOT suitable for assuring the quality of data collected by a third-party company is C. Introducing erroneous data to see if it’s detected. While it might seem like a way to test the system, intentionally introducing incorrect data can lead to unintended consequences and potentially harm the accuracy and reliability of the collected information. Instead, focus on other methods like verifying accuracy, validating procedures, and tracking changes through auditing to ensure data quality.