How does filtering impact data quality?

What is data filtering and why is it important?

Data filtering is the process of removing unwanted or irrelevant data from a dataset. It's important because it enhances data quality by ensuring that only accurate and relevant information is analyzed.

How does filtering improve the accuracy of data?

Filtering improves accuracy by eliminating noise and outliers, which can skew results. This leads to more reliable insights.

Can filtering also remove useful data?

Yes, if not done carefully, filtering can inadvertently remove useful data. It's crucial to define clear criteria to avoid this.

What are some common filtering techniques?

Common techniques include removing duplicates, applying thresholds, and using statistical methods to identify outliers.