Analyst Playbook — Quick Recipes for Augmented Analysis

Analyst Playbook — Quick Recipes for Augmented Analysis

These short recipes help analysts use AI to speed routine work while keeping outputs reliable and decision-ready. Each recipe states when to use it, what you need, step-by-step actions, validation checks to avoid common mistakes, and example prompts you can adapt.

1. Autogenerated narrative from dashboards

Purpose: Turn charts and KPI tiles into concise, prioritized narratives for stakeholders (insight + action). Use when you need readable commentary for daily reports, executive summaries, or story-driven slide text.

• Data snapshot or chart metadata (metric names, values, time window, comparisons)
• Context: audience, decision needed, what changed historically
• Access to your dashboard export (CSV, JSON, or metadata) and a language model or assistant

1. Export the relevant KPI values with timestamps and any comparison periods (previous period, target).
2. Filter to the top 5 signals by business impact (volume, percent change, or flagged items).
3. Construct a structured prompt that includes: a short context sentence, a 3–5 row data table (or metric list), the audience, and an instruction template (summary, implication, recommended next step).
4. Run the model and request a 2–4 sentence executive summary plus 1 recommended action per key signal.
5. Manually check each recommended action against known constraints (capacity, budget, policies) before publishing.

• Ensure numeric claims precisely match the supplied table; flag any hallucinated numbers.
• Confirm at least one action is testable and assigned to a team or owner.
• Spot-check for contextual errors (wrong region, wrong product) and correct the prompt source if needed.

Context: Weekly e-commerce dashboard for North America. Table: Revenue: $1.2M (+4% vs last week), Conversion: 2.1% (-0.3pp), Sessions: 570k (+6%). Audience: VP Growth. Task: Generate a 3-sentence summary and one recommended immediate action for each metric.

2. Smart anomaly triage

Purpose: Rapidly assess and prioritize anomalies detected by monitoring systems so analysts focus on high-impact investigations.

• Anomaly record (metric, time, magnitude, detection method)
• Related contextual fields: region, product line, segment, any recent deployments or promotions
• Access to logs, incident history, and a model/assistant

1. Collect anomaly metadata and any correlated metrics for the same window.
2. Enrich the anomaly with recent changelog items (deploys, config changes, campaigns).
3. Use an assistant to score the anomaly on three dimensions: business impact, likelihood of data/measurement error, and probable root cause category (data, code, demand, supply).
4. Prioritize incidents where business impact is high and likelihood of measurement error is low.
5. Create a short triage card: score, suggested owner, first diagnostic steps, and expected SLA.

• Confirm the assistant used only supplied facts; be wary of invented loglines or events.
• Cross-validate the assistant's root-cause suggestion with one independent data source (e.g., logs or another metric).

Given this anomaly record and two correlated metrics, assign scores 1–5 for business impact, measurement-likelihood, and suggest 1–2 first diagnostics and an owner role.

3. Automated data summarization prompts and validation checks

Purpose: Produce concise, structured summaries from raw tables (e.g., cohort analysis, churn drivers, defect logs) that include a confidence statement and data provenance.

• CSV or table extract (include column names and types)
• Key questions to answer (top 3)
• Model or assistant

1. Clean and sample the table to a manageable size. Provide column types and a short schema to the assistant.
2. Ask the assistant to produce: (a) three bullet insights with the supporting rows/aggregation, (b) a one-line confidence level (low/medium/high) and why, and (c) the exact SQL or aggregation used to derive each insight.
3. Run the SQL/aggregation yourself or via an automated check; compare results to assistant claims.
4. Only publish insights where the SQL check matches; for mismatches, record the discrepancy and re-run with corrected prompts or code.

• Require the assistant to return explicit aggregations or queries (never only prose claims).
• Automate a numeric equality check between the model's claims and the dataset's computed values.

Return three insights, each with: supporting aggregation (SQL or step-by-step), a confidence level, and the 3 most relevant rows that justify the insight.

Common mistakes and how to avoid them

• Relying on free-form outputs without structured checks — require queries or provenance statements.
• Feeding whole data dumps without schema or context — summarize and present focused slices.
• Blindly trusting model-suggested actions — always map actions to capacity and policies before execution.

Quick KPIs to monitor adoption

• Time saved per report (hours/week)
• Percent of auto-generated narratives that pass first-pass validation
• Mean time to triage for flagged anomalies

Next steps & platform opportunities

Start by applying one recipe to a recurring report or anomaly stream and build simple validation checks into your workflow. Consider converting high-value recipes into interactive templates that prompt for the dataset, time window, audience, and output format so analysts can reuse them reliably.