{"repo":"AmirhosseinHonardoust/Coverage-is-The-Silent-Killer","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Coverage-is-The-Silent-Killer","clone":"git clone https://github.com/AmirhosseinHonardoust/Coverage-is-The-Silent-Killer.git","description":"A long-form, practical article on data coverage: why clean dashboards still lie when datasets don’t represent the full calendar or population. Includes definitions, real failure modes (joins, filters, late data), coverage metrics, visualization patterns, anomaly/forecasting pitfalls, and reusable checklists.","language":null,"stars":10,"topics":["anomaly-detection","best-practices","dashboards","data-analytics","data-engineering","data-observability","data-quality","data-science","data-validation","documentation"],"license":"MIT","category":"dashboards-admin","readme_excerpt":"Coverage is the Silent Killer of Analytics Why clean charts can tell false stories, and how to build coverage-aware analysis that survives real data People blame analytics failures on: bad dashboards, bad models, bad SQL, bad stakeholders, or “data quality.” But one problem quietly causes more wrong conclusions than almost anything else: Coverage. Coverage is what your dataset actually represents. Not what you hope it represents. Not what the chart implies. What it truly covers. A chart can be perfectly labeled. A query can be correct. A KPI can be computed exactly. And the conclusion can still be wrong, because the data didn’t cover reality the way your brain assumed it did. This article is a deep, practical guide to coverage. What it is. How it breaks analysis. How to measure it. How to visualize it. How to communicate it. And how to build metrics that don’t lie when coverage changes. You’ll leave with: a coverage framework you can reuse, a playbook you can apply to any dataset, and the instincts to spot coverage traps before they ship. --- Table of contents 1. Why coverage is more dangerous than missing values 2. What “coverage” actually means 3. Coverage dimensions: time, entity, channel, geography, behavior 4. The silent assumptions that turn missing into fiction 5. Coverage failure stories (realistic and common) 6. How coverage breaks KPI math 7. How coverage breaks seasonality and trends 8. How coverage breaks anomaly detection 9. How coverage breaks forecasting 10. Th","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Coverage-is-The-Silent-Killer/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}