Analytics Data Analysis

Rylai Analytics Implementation

Build analysis that another person can rerun and audit.

Define The Contract

Capture the following before choosing methods:

• decision or question the analysis must support;
• input files, tables, date range, units, and grain;
• metric definitions and inclusion rules;
• expected deliverable: script, notebook, chart set, table, or report;
• runtime limits, privacy constraints, and required output path.

Inspect the real input before assuming its schema.

Implementation Workflow

1. Inventory

   • Record source names, sizes, columns, types, row counts, and keys.
   • Detect encoding, delimiter, duplicate-key, timezone, and locale issues.

2. Validate

   • Check missingness, ranges, uniqueness, referential integrity, and impossible values.
   • Separate source defects from intentional filtering.
   • Stop or quarantine records when a defect would invalidate the result.

3. Transform

   • Keep raw input unchanged.
   • Make cleaning steps explicit and deterministic.
   • Preserve units and document joins, filters, imputations, and derived fields.

4. Analyze

   • Start with counts and distributions.
   • Choose statistical methods that match variable type, sample design, and question.
   • Report effect size or practical magnitude when significance tests are used.
   • Test important assumptions and provide a fallback when they fail.

5. Visualize

   • Select a chart based on the comparison, trend, distribution, or relationship.
   • Label units, time windows, filters, and sample sizes.
   • Avoid visual encodings that exaggerate small differences.

6. Package

   • Keep configuration and paths separate from analysis logic.
   • Use stable output names and create parent directories deliberately.
   • Include a concise run command and dependency information when code is delivered.

7. Verify

   • Run the analysis from a clean start.
   • Reconcile important totals against the source.
   • Inspect generated tables and charts, not only exit codes.

Code Standards

• Prefer clear functions with explicit inputs and outputs.
• Use structured parsers for structured data.
• Favor vectorized or set-based operations where they improve clarity and scale.
• Never hide data loss inside broad exception handling.
• Add assertions at boundaries where a silent mismatch would corrupt results.
• Use a fixed random seed only when randomness is part of the method, and record it.
• Do not overwrite source files unless the user explicitly requests it.

Notebook Standards

• Put purpose and assumptions before the first analysis cell.
• Keep setup, loading, validation, transformation, analysis, and conclusions in visible sections.
• Ensure cells run top to bottom without relying on stale state.
• Remove noisy exploratory output while preserving evidence needed to review the result.

Delivery

Summarize:

• inputs and scope;
• cleaning and exclusion decisions;
• methods and assumptions;
• key outputs;
• validation performed;
• limitations and unresolved data-quality risks.

Never present an estimate as observed fact or infer causation from association alone.

Runtime Notes

• Use tools and libraries already available in the workspace when practical.
• If a dependency is missing, explain the smallest installation or fallback required.
• Keep paths portable between Codex, Hermes, and Claude by resolving from the workspace or skill directory.