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The phrase "poor data quality" is correct and can be used in written English.
It refers to data that is inaccurate, unreliable, or of low quality. Example: The company's decision-making process was hindered by poor data quality, as the data collected from various sources was inconsistent and outdated.
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Declining response rates and poor data quality are therefore unsurprising.
Think of the difference as the cost of poor data quality.
The authors believe this is owing to poor data quality and a lack of consensus on best practices.
Attempts to examine the situation through various data sources have always faced limitations of poor data quality.
If these data are defective, there are many ways that poor data quality can affect organizational effectiveness and efficiency.
But there are still huge hurdles to overcome on the ground, from poor data quality to resistant governments to technological illiteracy about publishing or releasing data online.
These issues are compounded when there is poor data quality such as missing data, erroneous data, or uncertainty in values or timestamps.
In this company poor data quality and usage caused 77% of missed deliveries, resulting in significant operating costs due to repetition of work and rerouting of shipments.
Bladder voiding diaries are an important part of evaluating and managing lower urinary tract symptoms but are subject to poor data quality and diary completion rates.
Some of these factors can be troublesome and can result in poor data quality or ambiguous results; these are "bad" ones.
However, the emission inventory construction of Chinese cities has not been well researched, mainly owing to the lack of systematic statistics and poor data quality.
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