Which best describes the relationship between data quality and analytics?

High data quality improves the accuracy of analytics because analytics relies heavily on the integrity and reliability of the underlying data. When data is high quality, it means it is accurate, complete, consistent, and timely. This enhances the ability of analytical models and tools to generate meaningful insights, make predictions, and support decision-making processes effectively. High-quality data minimizes errors and biases, leading to more trustworthy outcomes and conclusions drawn from the analysis.

In contrast, lower quality data can lead to misleading analytics, erroneous conclusions, and poor decision-making. Therefore, ensuring high standards in data quality is crucial for deriving valuable insights from data analytics efforts. This relationship highlights the critical importance of data management practices that prioritize data quality as foundational for effective analytics.