Data Interpretation Techniques in Practice

A healthcare data analyst audited a breast cancer diagnostic dataset to evaluate the impact of various deduplication rules on a 699-row baseline. Replacing a black-box Excel process, the dashboard compares three scenarios. "Full-row matching" conservatively dropped 8 rows (1.1%), leaving 691. Conversely, "Features-only" dropped 236 rows (33.8%), and "ID-only" dropped 54 rows (7.7%). The analyst discovered that 39 of 46 repeated IDs had distinct feature profiles, warning against naive ID-only deduplication. A horizontal stacked bar chart visualizes these retention differences, while a combo chart tracks how often IDs repeat, showing most repeat exactly twice. This exemplifies the importance of carefully interpreting data to preserve dataset integrity.

What it shows:

Always compare the row-level impact of different deduplication rules before finalizing a cleaned dataset.

A financial due diligence analyst examined raw SEC JSON data to identify unusual accrual patterns for a target company between 2009 and 2025. A combo chart visualizes accrued liabilities balances against year-over-year percentage changes. The data shows growth from under $5B in 2009 to roughly $25B in 2017, with a massive YoY spike exceeding 200% around 2012. Following a reporting gap from 2018 to 2022, liabilities jumped near $50B in 2023. By applying a horizontal dashed orange "+15% watch band," the analyst could quickly flag significant YoY swings. This workflow demonstrates effective analysis and interpretation of financial timing distortions.

What it shows:

Overlaying percentage change watch bands on absolute balances quickly highlights financial anomalies.