How to Repeat a Weekly CSV or Excel Report Without Starting Over
A practical workflow for rerunning weekly CSV or Excel analysis while preserving inputs, metric definitions, SQL, validation checks, and report limitations.
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Data Analysis Practice
9 articles
Before Analyzing a CSV with AI, Run These 8 Data Quality Checks
AI can explore CSV files quickly, but it cannot define correct data for you. This checklist covers encoding, types, nulls, duplicates, keys, time zones, units, and outliers with rerunnable SQL checkpoints.
Read article →Query Excel and CSV with SQL—No Database Installation Required: Who Is DuckDB For?
DuckDB is an embedded analytical database that queries local CSV, Parquet, and Excel files directly. It excels at file-based ad hoc analysis but does not replace a shared warehouse or scheduled ETL.
Read article →How to Review AI-Generated SQL: A 7-Point Checklist Beyond 'Does It Run?'
Successful execution does not mean AI-generated SQL answered the right question. Review grain, JOINs, filters, NULLs, time, denominators, and LIMITs with these minimal validation queries.
Read article →Why Does Everyone Calculate Conversion Differently? Align Denominators, Events, and Time Windows First
A funnel conversion rate has no single universal formula. User versus event grain, cohort versus calendar windows, event order, and cancellation rules all change the answer.
Read article →Numbers Explode After a JOIN? The Fan-Out Trap AI Analysis Often Misses
When totals grow after a JOIN, the aggregation is usually not broken—the tables have incompatible grains. Use these order examples and SQL checks to catch duplicate counting before delivery.
Read article →Quick Question or Formal Analysis? Decide by Risk, Not Length
Quick and formal analysis differ not by question length but by the impact of a wrong answer and the chance it will be reused. This two-dimensional matrix helps choose the right workflow.
Read article →Data Lineage Is Not Just for Big Data Platforms: Even a Local Report Should Show Its Origins
Data lineage traces sources, transformations, and outputs. Even with local CSV files and a monthly report, reusable work benefits from minimal lineage, naming rules, and a rerun path.
Read article →What Are the 6 Essential Parts of a Trustworthy AI Analysis Report?
A trustworthy AI report needs more than a summary: decision question, definitions, findings, traceable evidence, limitations, and next steps. Use this six-part template tied to SQL evidence.
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