data-analyst-explore

To profile a dataset for completeness and freshness, you run the PROFILE mode after loading your data. This quantifies key data quality metrics like consistency, queryability, and discoverability to determine if the data is usable.

Data validation against source definitions is the process of comparing loaded data against expected source schemas to catch discrepancies early. You need it to drive remediation planning and ensure datasets are reliable before analytics.

Yes, you can explore loaded datasets interactively using the EXPLORE mode. This allows you to run interactive queries across the dataset to answer business questions and inform stakeholder decisions after profiling.

Yes, you need the validate-data framework to quantify data queryability and discoverability. The skill requires this framework to support data-analytic workflows and route discovered issues to appropriate personas.

The best way to surface data gaps early in the data product lifecycle is to profile loaded datasets and validate them against source expectations. This catches discrepancies and routes issues to appropriate personas for remediation.