Data Quality Remediation Backlog Builder
Turn messy data quality findings into a prioritized remediation backlog with root causes, owners, validation checks, controls, and governance cadence.
Category
Data Analysis
Prompts for auditing datasets, planning SQL analysis, defining dashboards, reviewing experiments, and checking causal assumptions.
Advertising opportunity
Help keep Amo.ng free and public
Evidence-Grounded Spreadsheet KPI Variance Investigation
Analyze spreadsheet KPI movement, reconcile numerator, denominator, volume, rate, mix, timing, and data effects, and produce evidence-qualified findings with concrete verification and decision gates.
Dataset and Benchmark Source Validation Brief
Produce an evidence-linked assessment of a dataset, benchmark, leaderboard result, or research metric, including provenance, methodology, freshness, comparability, limitations, claim fidelity, and fit for the intended use.
Infographic Concept Art Direction
Create Midjourney-ready visual concept prompts for infographic layouts that explain data, processes, comparisons, or ideas clearly.
KPI Dashboard Requirements and Data Quality Audit
Produce decision-linked KPI requirements, calculation-ready metric definitions, source assessments, executable data quality test specifications, dashboard recommendations, and an evidence-based build-readiness decision.
Causal Assumption Check Prompt
Assess whether an analysis can support a causal claim by examining identification assumptions, design-specific threats, diagnostics, and alternative explanations.
Experiment Readout Prompt
Produce an evidence-grounded experiment readout covering design validity, metric effects, statistical uncertainty, caveats, segment findings, and an approval-ready recommendation.
Dashboard Requirements Specification Prompt
Turn dashboard goals, user decisions, KPI definitions, data sources, interactions, and governance constraints into an implementation-ready requirements specification.
SQL Analysis Plan Prompt
Design an execution-ready SQL analysis plan covering data grain, tables, joins, metrics, filters, query structure, validation queries, risks, and acceptance evidence.
Messy Dataset Audit Prompt
Audit a dataset for schema defects, missingness, duplicates, invalid values, outliers, join failures, privacy risks, and fitness for analysis.