Start by Not Touching the Data
Good analysis often starts by clarifying the question and the business context before touching the dataset.
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If you are new here, start with the essays below. Together they explain what this publication values: better framing, more honest analytical work, and fewer confident mistakes dressed up as insight.
What you will find here
- Business analysis anchored in decisions rather than dashboards
- Experimentation writing that takes context and limits seriously
- Statistics and data-quality essays for analysts, not just textbooks
- Writing about how analysts practice, learn, and build judgment
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Recommended reading path
The 6 Dimensions of Data Quality - Completness
Missing data can look perfectly normal. How completeness failures distort analysis and the decisions that follow.
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Statistics and analysts
Statistics matters for analysts, but mostly as a way to reason better rather than as a badge of mathematical purity.
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Dashboards are the problem, Not the solution
Dashboards often manufacture the feeling of clarity before a team has done the harder work of defining what matters.
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The Temporal Limitations of AB Testing
A/B test results are bounded by time, context, and population, even when the output looks statistically neat.
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What are the characteristics of good business analysis?
Good business analysis is repeatable, grounded in business rationale, and explicit about what action it makes possible.
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