What are the challenges of dirty data in analytics?

What are the challenges of dirty data in analytics?

Dirty data can lead to inaccurate insights, poor decision-making, and wasted resources. It includes errors, duplicates, and inconsistencies.

How can I identify dirty data in my datasets?

You can identify dirty data by checking for duplicates, missing values, and outliers. Data profiling tools can help automate this process.

What are some common sources of dirty data?

Common sources include manual data entry errors, system integrations, and outdated information from third-party sources.

What strategies can I use to clean dirty data?

Strategies include data validation, deduplication, standardization, and using automated tools for data cleansing.

How does dirty data affect machine learning models?

Dirty data can lead to biased models, inaccurate predictions, and reduced model performance, ultimately affecting business outcomes.