Data analysis

Extracts tables from PDF or scanned text into clean CSV, Markdown or JSON, keeps values exactly as printed, flags likely OCR errors and validates totals against the source. Use before analysis.

Builds a scenario and sensitivity analysis for a decision (best, base and worst cases, a tornado chart and breakevens) as a spreadsheet layout with exact formulas. Use before committing to a plan.

Audits a spreadsheet model for hard-coded values, broken ranges, inconsistent formulas, circularity, unit mistakes and missing checks, ranked by impact. Use before relying on someone else's sheet.

Builds a loan amortisation schedule in a spreadsheet with payment formulas, extra-payment scenarios and total interest, explaining each column. Use to see how a loan or mortgage pays down.

Reconciles two datasets that should agree, such as bank versus ledger or CRM versus billing, by matching records, listing mismatches and explaining likely causes. Use for month-end checks.

Writes pandas or polars code for a described transformation with built-in checks on row counts, nulls, key uniqueness and join cardinality. Use when reshaping, joining or aggregating data.

Analyses contact centre data - volume by interval, handle time, abandonment, service level, repeat contacts and contact reasons - and recommends staffing alignment and process fixes.

Analyses promotion data for incremental lift, cannibalisation, pull-forward and margin impact against a fair baseline, and says which to repeat. Use after a sale, coupon or discount campaign.

Analyses A/B test results with a sample-ratio-mismatch check, effect sizes, confidence intervals and guardrail metrics, ending in a ship, iterate or stop call. Use when an experiment ends.

Analyses Likert-scale survey data with distribution summaries, diverging bar charts and tests suited to ordinal items or multi-item scales. Use before reporting agreement scores.

Builds a weighted composite index from several indicators, covering direction, normalisation, weighting, aggregation and rank sensitivity checks. Use for scorecards and health scores.

Builds a statistical process control chart with the right chart type, control limits, special-cause rules and an interpretation that separates signal from noise. Use to monitor a process over time.

Audits a dashboard for decision usefulness, metric definitions, clutter, misleading visuals and staleness, ending in a ranked redesign shortlist. Use when a dashboard is ignored or distrusted.

Builds a self-contained HTML dashboard from a CSV with a script, a chart per question, filters and accessible colours, and checks every number against the data. Use for a quick, shareable dashboard.

Plans a Looker Studio report with data sources, credentials, blends, calculated fields, pages, controls and performance settings, ready to build step by step. Use before building a shared dashboard.

Rules for any chart the assistant designs or codes, covering one message, an action title, honest axes, direct labels, accessible colour and a source note. Load whenever a chart or plot is made.

Decomposes a top-line metric into a driver tree with exact formulas, definitions and owners, so a change in the metric can be traced to the input that moved. Use for metric design and reviews.

Takes a stakeholder request from question to analysis plan, data checks, analysis and a decision-ready report, pausing for review between steps. Use when an analyst takes on a request.

Designs automation for a recurring report (sources, refresh, transformations, data checks, delivery) with tools matched to the team's skills. Use when a weekly or monthly report eats hours.

Builds an organisation's metrics glossary with definitions, formulas, grain, sources, owners and caveats, and surfaces conflicting definitions. Use when teams quote different numbers.