How would you approach ensuring data integrity in a Stage 1 data analysis task?

How would you approach ensuring data integrity in a Stage 1 data analysis task?

• A. Delete all rows with missing values; keep everything else.
• B. Validate data types, check for duplicates, handle missing values appropriately, and document transformations.
• C. Rely on the dataset as is without checks.
• D. Convert all data to strings for uniformity.

Answer: (B)

Ensuring data integrity means keeping data accurate, complete, and consistent, and making sure your processing steps are reproducible. In Stage 1 tasks, this starts with validating the data types so each column holds the right kind of values (numbers, dates, categories) and converting where needed to enable correct analysis. Next, you check for duplicates to prevent counting the same observation more than once, which can distort results. Handling missing values thoughtfully is crucial—simply deleting every row with missing data can bias outcomes and waste information, so you choose a strategy (imputation, flagging, or exclusion) based on the context. Finally, documenting every transformation and cleaning step creates an audit trail, so others can reproduce your results and understand how the data was prepared. This approach is better than leaving data unchecked, converting everything to strings, or discarding data without a principled plan, because it preserves information, supports accurate analysis, and ensures results are trustworthy and traceable.

Ensuring data integrity means keeping data accurate, complete, and consistent, and making sure your processing steps are reproducible. In Stage 1 tasks, this starts with validating the data types so each column holds the right kind of values (numbers, dates, categories) and converting where needed to enable correct analysis. Next, you check for duplicates to prevent counting the same observation more than once, which can distort results. Handling missing values thoughtfully is crucial—simply deleting every row with missing data can bias outcomes and waste information, so you choose a strategy (imputation, flagging, or exclusion) based on the context. Finally, documenting every transformation and cleaning step creates an audit trail, so others can reproduce your results and understand how the data was prepared.

This approach is better than leaving data unchecked, converting everything to strings, or discarding data without a principled plan, because it preserves information, supports accurate analysis, and ensures results are trustworthy and traceable.