· 8 min read
By the end of this guide, you will have a 4-agent data analysis team that takes a dataset and a business question, profiles the data for quality and structure, performs statistical analysis, recommends visualizations, and produces a narrative report with actionable insights. The team handles the complete analysis pipeline from raw data to executive-ready findings.
Data analysis is one of the most common knowledge work bottlenecks. Business teams have questions. Data teams have backlogs. The gap between "I need to understand our Q2 retention numbers" and "Here is your analysis" can be days or weeks. This agent team compresses that cycle by automating the mechanical parts of analysis -- profiling, computation, visualization selection, and narrative construction -- so human analysts can focus on the judgment calls that require domain expertise.
You need Claude Code or the Claude Agent SDK configured and ready. You also need a dataset (CSV, JSON, or database query results) and a business question you want answered. The question should be specific: "What factors are driving the increase in customer churn among enterprise accounts in Q1 2026?" is workable. "Analyze our data" is not.
Prepare a data dictionary if one exists -- a description of each field, its type, valid ranges, and business meaning. If no formal dictionary exists, at minimum document the column names, what they represent, and any known data quality issues.
Mission: Receive the dataset and business question, plan the analysis approach, assign tasks to specialist agents, review intermediate outputs for quality and relevance, and compile the final analysis report.
The Coordinator functions as the senior analyst on the team. It reads the business question, examines the dataset structure, and decides what analyses are needed to answer the question. It does not perform the analyses itself -- it plans, delegates, and synthesizes.
Prompt guidance: Give the Coordinator the business context behind the question. "The VP of Customer Success asked this question because enterprise churn increased from 4% to 7% last quarter. She needs to decide whether to invest in a dedicated enterprise support tier or a product improvement initiative." This context determines which analytical angles are most valuable.
Mission: Examine the dataset for structure, quality, completeness, distributions, outliers, and potential issues before any analysis begins. Produce a data quality report that informs downstream analysis decisions.
The Data Profiler is the quality gate. Before anyone runs a correlation analysis or builds a chart, the Profiler answers: Is this data trustworthy? It checks for missing values (and whether they are random or systematic), detects outliers that could skew results, examines distributions for normality assumptions, identifies data type mismatches, and flags potential join key issues if multiple datasets are involved.
Prompt guidance: Instruct the Profiler to be specific about the impact of quality issues. "Column last_login_date has 12% missing values" is informative. "Column last_login_date has 12% missing values, concentrated in accounts created before 2024, which will bias any analysis of engagement patterns toward newer accounts" is actionable.
Mission: Perform the quantitative analysis required to answer the business question. Run descriptive statistics, segment comparisons, correlation analyses, trend analyses, and hypothesis tests as appropriate. Present results with proper context and caveats.
This agent does the computational heavy lifting. It calculates the numbers, runs the comparisons, and identifies statistically meaningful patterns. Critically, it also identifies patterns that look meaningful but are not -- a correlation that disappears when you control for account size, or a trend that is within normal variance.
Prompt guidance: Specify the analytical depth you need. For an executive briefing, descriptive statistics and segment comparisons may be sufficient. For a strategic decision, you need correlation analysis, cohort comparisons, and ideally some form of statistical significance testing. Tell the Analyst what methods are appropriate for your audience: "The audience is non-technical. Present findings as plain-language comparisons, not p-values."
Mission: Transform statistical findings into a coherent narrative that answers the original business question. Translate numbers into implications. Recommend specific actions based on the evidence. Flag where the data supports confident conclusions and where it only suggests hypotheses.
The Insight Narrator bridges the gap between data and decisions. It takes the Profiler's quality assessment and the Analyst's statistical results and constructs a story: here is what we asked, here is what the data shows, here is what it means for the business, and here is what we recommend doing about it.
Prompt guidance: Tell the Narrator who the audience is and what they care about. A CFO wants to know the financial impact. A product manager wants to know which features to prioritize. A customer success leader wants to know which accounts to intervene with. The same data can tell different stories depending on who needs to act on it.
Data analysis is inherently sequential in its early stages (you must profile before you analyze) but allows parallelism later:
plan_tier field").Data analysis prompts must emphasize rigor. The biggest risk in automated analysis is producing confident-sounding conclusions from flawed data or inappropriate methods.
For the Data Profiler: "You are a data quality analyst. For the provided dataset, produce a comprehensive quality assessment covering: (1) Schema summary -- column names, types, and descriptions; (2) Completeness -- missing value counts and patterns for each column; (3) Distributions -- summary statistics for numeric columns, value counts for categorical columns; (4) Outliers -- values more than 3 standard deviations from the mean or otherwise anomalous; (5) Quality flags -- any issues that could affect downstream analysis, with specific recommendations for how the Statistical Analyst should handle them."
For the Statistical Analyst: "You are a quantitative analyst. You will receive a business question, an analysis plan, and a data quality report. Execute the planned analyses with the following standards: (1) Always report sample sizes for any comparison; (2) When comparing groups, report both absolute and relative differences; (3) Flag any finding where the sample size is too small for reliable conclusions; (4) Distinguish between correlation and causation explicitly; (5) Present uncertainty ranges, not just point estimates; (6) If the data does not support a conclusion about the business question, say so directly rather than stretching the analysis."
For the Insight Narrator: "You are a business analyst who translates data into decisions. You will receive statistical results and a data quality report. Produce a narrative that: (1) Directly answers the business question in the first paragraph; (2) Supports the answer with the 3-5 most important findings; (3) Explains what the findings mean for the business in practical terms; (4) Recommends 2-3 specific actions with expected impact; (5) Discloses limitations -- what the data cannot tell us and what additional analysis would strengthen the conclusions."
The Coordinator compiles a report with these sections:
For a churn analysis question, the output might include:
Answer Summary: Enterprise churn increased from 4.1% to 7.3% in Q1 2026, driven primarily by accounts in the 50-200 seat range that were not assigned a dedicated Customer Success Manager. Accounts without a CSM churned at 11.2% compared to 3.1% for accounts with a CSM -- a 3.6x difference that is statistically significant (p < 0.01, n=847).
Key Finding 1: CSM assignment is the strongest predictor of enterprise retention. Among enterprise accounts, those with a dedicated CSM had 91% higher NPS scores and submitted 60% fewer support tickets in the 90 days before renewal.
Key Finding 2: The churn spike is concentrated in accounts that expanded rapidly (added 30%+ seats in the past 6 months). These accounts outgrew their onboarding support but were not flagged for CSM assignment because they started below the CSM threshold.
Recommendation 1: Assign CSMs to all enterprise accounts with 50+ seats. Based on the observed retention differential, this would prevent an estimated 14-18 churned accounts per quarter, representing $840K-$1.1M in preserved ARR.
Recommendation 2: Create an automated trigger that assigns CSM coverage when any account crosses the 50-seat threshold through expansion, rather than only evaluating at contract signing.
Limitation: The analysis cannot determine whether CSM assignment causes better retention or whether accounts that receive CSMs are already healthier. A controlled pilot would strengthen the causal claim.
Iterative deepening. Start with a broad exploratory analysis. Once the Coordinator identifies the most promising findings, dispatch the Statistical Analyst for deeper investigation on those specific angles. Two passes -- one broad, one deep -- produce better results than a single medium-depth pass.
Automated anomaly detection. Run the Data Profiler on a schedule against your key business metrics. When it detects an anomaly (a metric outside its normal range), it triggers the full analysis team to investigate. This turns reactive analysis into proactive monitoring.
Multi-dataset joins. For questions that span multiple data sources (combining product usage data with billing data and support ticket data), add a Data Engineer agent that handles joining, deduplication, and entity resolution before the Profiler and Analyst begin their work.
Reproducibility. Have the Statistical Analyst document every calculation as a pseudo-code specification. This lets a human analyst reproduce the analysis in their preferred tool (Python, R, SQL) if they want to validate or extend the findings.