What are common features of low data quality?

The characteristics of low data quality primarily include unreliable information, incomplete data, and duplicated data. Each of these features can significantly hinder the performance of data-driven models and analytics.

Unreliable information indicates that the data may produce incorrect conclusions or insights, leading to potentially poor decision-making. This unreliability can stem from various sources, including errors during data collection or processing, leading to inaccurate representations of real-world scenarios.

Incomplete data presents a challenge as it may lack essential entries or fields that are necessary for robust analysis. Missing data can skew results and interpretations, causing models to function under misleading assumptions.

Duplicated data creates redundancy and can distort analyses, as the same information is counted multiple times. This can lead to inflated metrics, such as counts and sums, which do not accurately reflect the true nature of the dataset.

On the other hand, excessive detail and highly organized data, while potentially complicating processes, do not inherently reflect low data quality. Instead, richness in detail and organization can, in many cases, enhance data quality by providing comprehensive insights when managed properly. Thus, option B encapsulates the primary features indicative of low data quality effectively.