What Claude Does (and Doesn't Do) with Data

Setting expectations correctly is important for Claude data analysis workflows. Claude is not a database, BI tool, or statistical computing environment. It is a reasoning model that can interpret, compare, explain, and extract insights from data you provide — and it does this exceptionally well for business analysis purposes.

Claude can: identify trends in time series data, flag anomalies against stated benchmarks, compare periods and calculate growth rates, generate narrative commentary on numerical data, identify the most significant variances in a dataset, synthesise multiple data sources into a coherent picture, and write Python or SQL code to perform more complex analysis in other environments. Claude cannot: connect to live databases directly (without API/MCP integration), generate charts or visualisations, handle very large datasets that exceed its context window, or perform complex statistical inference reliably without external tool support.

Within those bounds, Claude is transformative for the data analysis tasks that consume the most analyst time: the interpretation and commentary work that sits between the numbers and the decision. That's where Claude saves 70-80% of the time.

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Financial Data Analysis

Finance teams are the highest-volume users of Claude data analysis in our deployment network. The primary use case: turning financial data (actuals vs budget, period comparisons, variance analysis) into clear narrative commentary for management reports, board packs, and investor materials.

Variance Analysis Commentary

Variance analysis commentary — explaining why actuals differ from budget — is one of the most time-consuming and repetitive tasks in finance. It requires examining each line item, understanding context, and writing clear business-English explanations. Claude handles this in minutes once you provide the data.

Variance Analysis Commentary Prompt
You are a senior finance analyst producing variance analysis commentary for [monthly/quarterly] management accounts. Write in clear business English — no jargon, no hedging. Assume readers are non-financial senior managers. COMPANY: [context — size, industry, what drives the business] PERIOD: [Month/Quarter Year] MATERIALITY THRESHOLD: [only explain variances >X% or >$Xk] DATA: [Paste actuals vs budget table here] For each material variance, provide: - What the variance is (in $ and %) - The primary cause (be specific — "Q4 contractor costs for [project]" not "increased costs") - Whether it is expected to reverse or continue - Any management action being taken Format: section headers by P&L category (Revenue, Gross Profit, Opex, EBITDA). Use 1-2 sentences per variance. Do not state the obvious (e.g., "revenue was above budget because revenues were higher than planned"). End with a 3-sentence overall financial summary for the executive summary.

KPI Trend Analysis

Beyond variance analysis, Claude excels at identifying patterns in KPI trend data — especially when you ask it to find the story, not just describe the numbers.

KPI Trend Analysis Prompt
Analyse the following KPI data for [COMPANY] over [TIME PERIOD]. I am a [ROLE] and I need to understand: (1) what the most significant trends are, (2) what is performing unusually well or badly, and (3) what 3 actions I should consider based on this data. DATA: [paste KPI table or series] BUSINESS CONTEXT: [2-3 sentences on what this business does and what drives the KPIs] Produce: 1. HEADLINE INSIGHT: The single most important thing this data tells us (1-2 sentences) 2. POSITIVE TRENDS: What is improving and at what rate 3. CONCERNS: What is declining, deteriorating, or below benchmark — with severity assessment 4. ANOMALIES: Anything that looks inconsistent with the surrounding trend 5. CORRELATIONS: Any metrics that appear to be moving together in noteworthy ways 6. 3 RECOMMENDED ACTIONS: Specific and actionable — not generic advice Do not restate data I have already provided. Interpret and analyse — that is the value I need.
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Financial analysis workflows, variance commentary templates, board reporting, and audit support — the complete Claude library for finance teams.

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Operational Data Analysis

Operations, customer success, and product teams deal with operational metrics — ticket volumes, response times, conversion rates, customer satisfaction scores — that need to be interpreted and communicated to decision makers. Claude handles both the interpretation and the communication.

Customer Data Analysis

Customer-facing metrics analysis is particularly valuable because it combines quantitative data (numbers) with qualitative signals (verbatim feedback, NPS comments) that Claude can process together. Paste customer satisfaction scores alongside sample verbatim feedback and ask Claude to identify the connection between the numbers and the themes.

Customer Metrics Analysis Prompt
Analyse the following customer metrics data and feedback. I need to understand what is driving our customer satisfaction scores and what we should prioritise to improve them. METRICS DATA: [paste CSAT, NPS, response time, resolution rate, etc.] SAMPLE CUSTOMER VERBATIM FEEDBACK: [paste 15-30 customer comments — positive and negative] Produce: 1. METRICS SUMMARY: What the numbers say about current performance vs [benchmark/target/prior period] 2. FEEDBACK THEMES: Group verbatim comments into 3-5 themes — what are customers consistently saying? 3. CONNECTION BETWEEN DATA AND FEEDBACK: What in the qualitative feedback explains the quantitative trends? 4. HIGHEST-IMPACT IMPROVEMENT: What single change would most likely improve satisfaction scores? 5. QUICK WINS: 2-3 improvements that could be implemented quickly (weeks, not quarters) 6. STRATEGIC IMPROVEMENTS: 1-2 longer-term structural changes suggested by the data

Sales and Market Data Analysis

Sales teams and strategy teams use Claude to analyse pipeline data, win/loss patterns, and market signals. Claude's ability to identify patterns across multiple data points simultaneously is particularly valuable in sales analysis, where the signal is often distributed across many small data points.

Sales Pipeline Analysis Prompt
Analyse the following sales pipeline data. I am the [VP Sales / CRO / Sales Director] and need to understand the health of our pipeline and where we should focus attention. PIPELINE DATA: [paste opportunity data — stage, value, close date, owner, source, industry, deal age] CONTEXT: - Our typical sales cycle: [N weeks/months] - Our average ASP: [$X] - Our target for [period]: [$X] Produce: 1. PIPELINE HEALTH SUMMARY: Coverage ratio, weighted pipeline, current close probability 2. STAGE ANALYSIS: Where deals are accumulating (which stages have the most deals/value stuck) 3. DEAL VELOCITY: Any patterns in deal age — deals aging unusually fast or slow vs typical cycle 4. OWNER ANALYSIS: Performance variation across reps (flag outliers — over and underperformers) 5. CONCENTRATION RISKS: Any single deals or customers representing > 20% of pipeline 6. CLOSE DATE ANALYSIS: Do Q-end deals look realistic or compressed? 7. 3 FOCUS AREAS: Where attention this week/month will have the highest impact on close rates

Building Repeatable Data Analysis Workflows

The most efficient teams build standardised data analysis workflows: a consistent data input format, a tested prompt template, and a defined output format. When the workflow is standardised, analysis that used to take 3-4 hours can be produced in 20-30 minutes by any team member — not just senior analysts.

The Standardised Analysis Template Approach

For each recurring analysis (monthly finance review, weekly pipeline review, quarterly customer analysis), create a "analysis template" with three components: the data input format (a standard spreadsheet layout that feeds Claude), the prompt template (pre-written and tested), and the output format (the specific structure you need for your reports).

Store these templates in a Claude Project with your company context. Each analysis cycle becomes a simple three-step process: export data to the standard format, paste into Claude, review and refine the output. Our training programme covers building these templates for every department in your organisation.

API Integration for Automated Analysis

For teams running the same analysis repeatedly on live data — daily sales dashboards, real-time customer health scores, automated financial variance reports — the Claude API enables fully automated analysis workflows. Data flows from your source systems through the API to Claude, which generates analysis and commentary automatically and delivers it to the right channel (Slack, email, Confluence). Our implementation team designs and deploys these integrations as part of our standard enterprise deployment.

Related workflows: Report Generation · Research Synthesis · Financial Reporting Automation · 100 Claude Workflows · Claude for Finance