Claude Code for Marketing Data Teams: Query Your Analytics Stack with Natural Language

Marketing data teams spend 60% of their time writing SQL queries for stakeholders who needed the answer yesterday. Claude Code eliminates this bottleneck. Connect it to BigQuery, Snowflake, or Databricks via MCP, and your entire team queries data in plain English.

This is not about replacing your data team. It is about giving them leverage. One analyst with Claude Code handles the ad-hoc query volume of three analysts without Claude Code. The SQL still runs. The governance still applies. The difference is who writes it.

TL;DR

Claude Code connects to your analytics data warehouse through Model Context Protocol (MCP) integrations. Marketing teams query campaign performance, attribution data, and customer behavior using natural language instead of SQL. Configure your CLAUDE.md file with marketing-specific context, build skills for recurring analysis workflows, and use slash commands to standardize reports across your team. Context management determines success. Keep your agent session focused by clearing memory when it fills past 40-60% and using sub-agents for isolated analysis tasks.

Key Takeaways

• Claude Code connects to BigQuery, Snowflake, and Databricks via MCP servers for live data queries
• The CLAUDE.md file loads marketing context into every session, including metric definitions, table schemas, and business rules
• Skills activate automatically when Claude detects relevant tasks, loading specialized analytics knowledge on-demand
• Slash commands standardize recurring reports like weekly campaign summaries and attribution analysis
• Sub-agents isolate focused analysis tasks with fresh context windows, preventing memory pollution
• Context fills up at 40-60% capacity and degrades query quality. Start new sessions or clear context regularly
• MCP servers add significant context overhead. Use skills for common analysis patterns to reduce memory consumption

What Is Claude Code and Why Do Marketing Data Teams Need It?

Claude Code is Anthropic’s agentic coding tool that runs in the terminal. You type natural language commands. Claude writes and executes SQL, Python, or bash commands to answer your question. It reads your data warehouse schema, understands table relationships, and generates accurate queries without you touching SQL.

Marketing data teams face a fundamental scaling problem. Every stakeholder wants ad-hoc analysis. The CMO needs attribution by channel. The demand gen manager needs campaign performance. The product marketing team needs funnel conversion by segment. Each request requires SQL. The backlog grows faster than the team delivers.

Claude Code solves this by letting anyone query data in plain English. “What was our cost per acquisition by channel last quarter, broken down by marketing campaign?” Claude writes the SQL, executes it against your warehouse, and returns the answer. Business users get insights without waiting for the data team. Analysts focus on complex modeling instead of ad-hoc requests.

Snowflake reports greater than 90% accuracy on complex text-to-SQL tasks using Claude through Cortex AI. Google Cloud integrated Claude into BigQuery ML for natural language queries directly on your data. Databricks signed a five-year partnership with Anthropic to bring Claude natively to their Data Intelligence Platform. The infrastructure exists. The question is whether your team adopts it.

Action item: Install Claude Code using npm install -g @anthropic-ai/claude-code and run claude in your terminal. Ask it to list files in your current directory to verify installation works before connecting to any data sources.

How Does Context Management Determine Success with Claude Code?

Context is the session memory that Claude Code uses to understand your requests. Think of it like a computer’s RAM. When it fills up, performance degrades. Claude Code’s context window has a limit, and everything you discuss accumulates in that space.

Marketing analytics sessions fill context quickly. Table schemas, query results, follow-up questions, error messages, and clarifications all consume space. When context reaches 40-60% capacity, query accuracy begins to deteriorate. Claude starts forgetting earlier context, misinterpreting requests, and generating less relevant SQL.

Manage context proactively with these strategies. Clear your session with /clear when context fills past 50%. Start new sessions for unrelated analysis tasks rather than continuing in the same conversation. Use sub-agents to isolate focused analysis with fresh context windows. Minimize verbose output requests that dump large datasets into context.

The components covered in this guide all relate to context management. CLAUDE.md defines what loads at session start. Skills load specialized knowledge on-demand rather than constantly. Commands trigger specific prompts without accumulating chat history. Sub-agents provide isolated context for focused tasks. Understanding context is the meta-skill that determines whether Claude Code works for your team.

Action item: During your first Claude Code session, monitor context usage by running /context periodically. Note when queries start degrading and what percentage shows at that point. Use this as your personal threshold for clearing sessions.

What Is CLAUDE.md and How Do You Configure It for Marketing Analytics?

CLAUDE.md is a markdown file that loads into every Claude Code session as system context. Place it in your project root directory. Claude reads it at session start and treats the contents as foundational knowledge for every interaction. It becomes part of Claude’s system prompt.

For marketing data teams, CLAUDE.md should contain metric definitions, table schemas, business rules, and common calculation patterns. When Claude knows that “CAC” means customer acquisition cost calculated as total_spend / new_customers, it generates accurate SQL without you defining it each time.

Keep CLAUDE.md concise. Every word consumes context capacity. Claude Code’s built-in system prompt already contains approximately 50 instructions. As you add more instructions, Claude follows all of them less reliably. Include only information needed in every session, not edge cases.

Run /init to have Claude scan your project and generate an initial CLAUDE.md. Refine it based on actual friction you encounter.

Here is an example CLAUDE.md for a marketing analytics project:

# Marketing Analytics Project

## Data Warehouse
- Platform: BigQuery
- Project: company-analytics
- Dataset: marketing_data
- Connection: Use BigQuery MCP server

## Key Tables
- campaigns: id, name, channel, spend, start_date, end_date
- conversions: id, campaign_id, user_id, conversion_date, revenue, conversion_type
- users: id, email, signup_date, first_touch_source, acquisition_channel
- ad_spend: date, campaign_id, platform, impressions, clicks, spend

## Metric Definitions
- CAC = total_spend / COUNT(DISTINCT new_customers)
- ROAS = revenue / spend
- CVR = conversions / clicks
- LTV = SUM(revenue) per customer over 12 months
- Payback Period = CAC / (monthly_revenue_per_customer)

## Attribution Model
- Default: Last-touch attribution
- Available: First-touch, linear, time-decay (7-day half-life)
- Attribution window: 30 days

## Business Rules
- Exclude internal traffic (email domain = company.com)
- Paid channels: google_ads, meta_ads, linkedin_ads, tiktok_ads
- Organic channels: seo, direct, referral, organic_social
- A conversion counts once per user per 24-hour period

## Date Conventions
- Fiscal year starts April 1
- Week starts Monday
- Always use UTC timestamps
- Current quarter: Q4 FY25 (Jan-Mar 2025)

Do not include API keys, credentials, or sensitive data in CLAUDE.md. It becomes part of the prompt and could be logged or shared. Use environment variables for authentication.

Action item: Create a CLAUDE.md file with your five most-used metric definitions and your primary table schemas. Test by asking Claude a question that requires one of those metrics and verify it calculates correctly without additional explanation.

How Do Skills Load Marketing Expertise On-Demand?

Skills are packaged expertise that Claude loads when it detects relevance to your task. Unlike CLAUDE.md content that loads in every session, skills activate automatically based on keyword matching in your request. This keeps base context lean while providing deep knowledge when needed.

Each skill lives in a folder under /.claude/skills/<skill-name>/ with a SKILL.md file containing metadata and instructions. The frontmatter (the top section between --- markers) describes when to use the skill. Claude reads frontmatter for all skills at session start but only loads full content when triggered.

For marketing data teams, build skills for recurring analysis patterns. Attribution analysis has specific SQL patterns, business rules, and interpretation guidelines. Campaign performance has standard metrics, comparison frameworks, and anomaly detection logic. Funnel analysis has specific stage definitions and conversion calculations.

Here is an example skill for multi-touch attribution analysis. Create /.claude/skills/attribution-analysis/SKILL.md:

---
name: attribution-analysis
description: Multi-touch attribution modeling for marketing campaigns. Use when user asks about attribution, channel contribution, touchpoints, or conversion paths.
allowed-tools: Read, Bash, Write
---

# Multi-Touch Attribution Analysis

## When to Use This Skill
- User asks about attribution by channel or campaign
- User wants to understand conversion paths
- User asks which touchpoints drive conversions
- User needs to compare attribution models

## Standard Attribution Models

### Last-Touch Attribution
Assigns 100% credit to the final touchpoint before conversion.

~~~sql
SELECT 
  last_touch_channel,
  COUNT(*) as conversions,
  SUM(revenue) as attributed_revenue
FROM (
  SELECT 
    c.user_id,
    c.revenue,
    t.channel as last_touch_channel,
    ROW_NUMBER() OVER (PARTITION BY c.user_id ORDER BY t.timestamp DESC) as rn
  FROM conversions c
  JOIN touchpoints t ON c.user_id = t.user_id
  WHERE t.timestamp <= c.conversion_timestamp
    AND t.timestamp >= DATE_SUB(c.conversion_timestamp, INTERVAL 30 DAY)
)
WHERE rn = 1
GROUP BY last_touch_channel
~~~

### Linear Attribution
Distributes credit equally across all touchpoints in the conversion path.

~~~sql
WITH touchpoint_counts AS (
  SELECT 
    c.user_id,
    c.revenue,
    COUNT(*) as total_touchpoints
  FROM conversions c
  JOIN touchpoints t ON c.user_id = t.user_id
  WHERE t.timestamp <= c.conversion_timestamp
    AND t.timestamp >= DATE_SUB(c.conversion_timestamp, INTERVAL 30 DAY)
  GROUP BY c.user_id, c.revenue
)
SELECT 
  t.channel,
  SUM(tc.revenue / tc.total_touchpoints) as attributed_revenue
FROM touchpoint_counts tc
JOIN touchpoints t ON tc.user_id = t.user_id
GROUP BY t.channel
~~~

## Interpretation Guidelines
- Compare at least two models before drawing conclusions
- Last-touch favors bottom-funnel channels (paid search, retargeting)
- First-touch favors awareness channels (display, social)
- Linear provides balanced view but may overweight low-impact touchpoints

## Output Format
Always include:
1. Total conversions and revenue in attribution window
2. Channel breakdown by model
3. Comparison table showing model differences
4. Recommendation based on business context

When you ask “What channels are driving conversions this month?”, Claude detects the attribution relevance and loads this skill automatically. The SQL patterns and interpretation guidelines become available without you requesting them.

Action item: Identify your team’s three most common analysis requests. Create a skill for each with standard SQL patterns, metric definitions, and output format requirements. Test each skill by asking related questions and verify Claude loads the skill automatically.

How Do Slash Commands Standardize Recurring Reports?

Slash commands trigger predefined prompts when you type /command-name. Unlike skills that activate automatically, commands require explicit invocation. They are ideal for standardized reports that should produce consistent output every time.

Create commands in .claude/commands/ for project-level access or ~/.claude/commands/ for personal access across all projects. Each command is a markdown file. The filename becomes the command trigger. Use $ARGUMENTS as a placeholder for parameters passed at invocation.

For marketing data teams, build commands for weekly reports, monthly summaries, and standard analysis requests. Every team member running /weekly-campaign-report gets the same output format, making results comparable and reducing interpretation variance.

Here is an example command for weekly campaign performance. Create .claude/commands/weekly-campaign-report.md:

SYSTEM: You are a marketing analyst generating a weekly campaign performance report.

<context>
Reporting period: Last 7 days (Monday through Sunday)
Comparison period: Previous 7 days
Focus: Paid media performance across all channels
</context>

Generate a weekly campaign performance report with these sections:

1. Executive Summary (3 bullets max)
   - Total spend and week-over-week change
   - Total conversions and week-over-week change
   - Blended ROAS and week-over-week change

2. Channel Performance Table
   - Columns: Channel, Spend, Conversions, CPA, ROAS, WoW Change
   - Sort by spend descending
   - Flag any channel with CPA increase >20% or ROAS decrease >15%

3. Top 5 Campaigns by Spend
   - Include: Campaign name, channel, spend, conversions, CPA, ROAS
   - Note any campaigns significantly over or under target CPA

4. Anomalies and Alerts
   - Campaigns with spend >$1000 and zero conversions
   - Campaigns with CPA 2x above channel average
   - Significant day-over-day volatility

5. Recommendations
   - 2-3 specific actions based on the data
   - Budget reallocation suggestions if warranted

Output: Formatted markdown report suitable for sharing in Slack or email.

Run with /weekly-campaign-report and Claude generates the standardized report. Add parameters: /weekly-campaign-report google_ads to filter to a specific channel.

Here is another example for ad-hoc funnel analysis. Create .claude/commands/funnel-analysis.md:

SYSTEM: You are a marketing analyst performing funnel conversion analysis.

Analyze the conversion funnel for: $ARGUMENTS

MUST include:
1. Stage-by-stage conversion rates
2. Drop-off analysis at each stage
3. Comparison to previous period (default: 30 days prior)
4. Segment breakdown if relevant dimensions exist

MUST flag:
- Any stage with >50% drop-off
- Any stage with conversion rate declining >10% period-over-period
- Unusual patterns in time-to-convert between stages

Output: Summary table with stage metrics, followed by key findings and recommended focus areas.

Run with /funnel-analysis signup_to_purchase to analyze a specific funnel.

Action item: Create three slash commands for your most-requested recurring reports. Share them with your team by committing the .claude/commands/ folder to your repository. Document the commands in your team wiki with example invocations.

How Do Sub-Agents Isolate Focused Analysis Tasks?

Sub-agents are separate Claude instances with their own context windows. They run isolated from your main session, preventing context pollution when you need focused analysis. Think of them as spinning up a fresh analyst who only knows about the specific task at hand.

The main Claude Code session accumulates everything you discuss. After asking about campaign performance, attribution, funnel analysis, and customer segmentation, your context is full of unrelated information. When you then ask a specific question about one topic, all that other context influences the response, often negatively.

Sub-agents start fresh. They have their own memory, their own context window, and their own focus. When the sub-agent completes its task, it returns results and cleans up. Your main session stays clear for coordination and new tasks.

Three built-in sub-agent types ship with Claude Code. General-purpose handles complex multi-step workflows. Plan handles research and planning. Explore helps understand data structures and schemas.

Create custom sub-agents in .claude/agents/ for project-level access. Use the /agents command to create and manage them interactively.

Here is an example sub-agent for cohort analysis. Create .claude/agents/cohort-analyzer.md:

---
name: cohort-analyzer
description: Perform cohort retention and behavior analysis. Use for user segmentation, retention curves, and cohort comparisons.
tools: Read, Bash
model: sonnet
---

You are a marketing data analyst specializing in cohort analysis.

When invoked:
1. Identify the cohort definition (signup date, first purchase, etc.)
2. Query the relevant tables to build cohort membership
3. Calculate retention by period (weekly or monthly based on data volume)
4. Compare cohorts to identify meaningful differences

Standard metrics to calculate:
- Period-over-period retention rate
- Average revenue per user by cohort age
- Time to second conversion
- Cohort size trends

Output format:
- Retention curve data (suitable for visualization)
- Cohort comparison table
- Statistical significance notes where relevant
- Key findings summarized in 3-5 bullets

Invoke the sub-agent by asking Claude to delegate: “Use the cohort-analyzer agent to compare Q3 vs Q4 signup cohorts by 30-day retention.”

Sub-agents run in parallel when you launch multiple in one message:

Launch these subagents simultaneously:
1. Cohort Analyzer: Compare retention for users acquired via paid vs organic
2. Attribution Analyzer: Calculate channel attribution for Q4 conversions
3. Funnel Analyzer: Identify drop-off points in the trial-to-paid funnel

Each sub-agent operates independently and returns results when complete.

Action item: Create a sub-agent for your most complex recurring analysis type. Test it by delegating a real analysis task. Compare the output quality to asking the same question in a main session with existing context.

How Does MCP Connect Claude Code to Your Data Warehouse?

Model Context Protocol (MCP) is a standardized way to connect Claude Code to external systems. MCP servers wrap authentication, query execution, and response handling into a package Claude knows how to use. Connect an MCP server for BigQuery, and Claude queries your data warehouse using natural language.

MCP servers exist for major data platforms. BigQuery has an open-source MCP server that connects Claude to your datasets. Snowflake offers MCP through Cortex AI. Databricks provides managed MCP servers for SQL execution and Genie spaces. Third-party providers like CData offer MCP servers for 350+ data sources.

Configure MCP servers in your .mcp.json file for project-level access or Claude Code settings for global access. Once connected, Claude accesses your tables, schemas, and data directly.

Here is an example BigQuery MCP configuration. Create .mcp.json in your project root:

{
  "bigquery": {
    "command": "npx",
    "args": ["-y", "mcp-server-bigquery"],
    "env": {
      "GOOGLE_APPLICATION_CREDENTIALS": "${GOOGLE_APPLICATION_CREDENTIALS}",
      "BIGQUERY_PROJECT_ID": "${BIGQUERY_PROJECT_ID}"
    }
  }
}

For Snowflake, the MCP configuration looks similar:

{
  "snowflake": {
    "command": "npx",
    "args": ["-y", "@snowflake/mcp-server"],
    "env": {
      "SNOWFLAKE_ACCOUNT": "${SNOWFLAKE_ACCOUNT}",
      "SNOWFLAKE_USER": "${SNOWFLAKE_USER}",
      "SNOWFLAKE_PASSWORD": "${SNOWFLAKE_PASSWORD}",
      "SNOWFLAKE_WAREHOUSE": "${SNOWFLAKE_WAREHOUSE}",
      "SNOWFLAKE_DATABASE": "${SNOWFLAKE_DATABASE}"
    }
  }
}

Store credentials in environment variables, not in the configuration file. The ${VARIABLE_NAME} syntax tells Claude Code to read from your shell environment.

After MCP setup, query your data with natural language:

"What was our total ad spend by channel last month?"
"Show me the top 10 campaigns by conversion volume this quarter"
"How many users signed up from paid search in December?"

Claude writes the SQL, executes it against your warehouse, and returns results.

Important caveat: MCP servers add significant context overhead. Every tool definition, every query result, and every schema exploration consumes context space. For frequently repeated analysis patterns, skills often provide better context efficiency than MCP queries. Use MCP for ad-hoc exploration and skills for standardized workflows.

Action item: Set up the MCP server for your primary data warehouse. Test with a simple query like “List the tables in the marketing_data schema.” Verify Claude accesses live data before building more complex workflows.

What Are the Top Use Cases for Marketing Data Teams?

Campaign Performance Analysis

Campaign performance analysis is the most common ad-hoc request for marketing data teams. Stakeholders want spend, conversions, CPA, and ROAS by campaign, channel, and time period. They want comparisons to previous periods. They want anomaly detection.

Claude Code handles this in natural language. “How did our Google Ads campaigns perform last week compared to the week before? Flag any campaigns where CPA increased more than 20%.”

Time saved: 30-45 minutes per request reduced to 2 minutes.

Use this prompt for comprehensive campaign analysis:

SYSTEM: You are a performance marketing analyst.

<context>
Data warehouse: BigQuery
Tables: ad_spend, conversions, campaigns
Attribution: Last-touch, 30-day window
</context>

Analyze campaign performance for $ARGUMENTS.

MUST include:
1. Summary metrics: Total spend, conversions, CPA, ROAS
2. Period-over-period comparison (vs previous equivalent period)
3. Channel breakdown with same metrics
4. Top 5 and bottom 5 campaigns by efficiency (ROAS)
5. Anomaly flags for statistical outliers

Output: Executive summary (3 bullets), detailed tables, and recommended actions.

Multi-Touch Attribution Analysis

Attribution analysis requires complex SQL with window functions, date logic, and multiple joins. Most marketing teams rely on platform-reported attribution, which overcounts and cannot be reconciled across channels.

Claude Code generates attribution SQL for your preferred model. “Calculate linear attribution for Q4 conversions, showing channel contribution. Compare to last-touch attribution to identify channels that are under-credited.”

Time saved: 2-4 hours per attribution analysis reduced to 10 minutes.

Funnel Conversion Analysis

Funnel analysis requires defining stages, calculating conversion rates, identifying drop-offs, and comparing segments. The SQL involves multiple CTEs, conditional logic, and time-based filters.

Claude Code handles funnel complexity naturally. “Show me the conversion funnel from landing page visit to purchase for users acquired via paid social. Compare mobile vs desktop.”

Time saved: 1-2 hours per funnel analysis reduced to 5 minutes.

Cohort Retention Analysis

Cohort analysis requires grouping users by acquisition date, calculating retention by cohort age, and comparing across cohorts. The SQL is complex and easy to get wrong.

Claude Code builds cohort retention curves from natural language. “Create a cohort retention analysis for users who signed up in each month of 2024. Show weekly retention for the first 12 weeks.”

Time saved: 3-4 hours per cohort analysis reduced to 15 minutes.

Data Quality Auditing

Marketing data has quality issues. Duplicate conversions, missing UTM parameters, attribution window violations, and timezone mismatches corrupt analysis. Auditing requires SQL that checks for specific anomalies.

Claude Code runs quality checks on demand. “Check the conversions table for duplicate conversion_id values, conversions with no associated campaign, and conversions where the timestamp is in the future.”

Time saved: 1-2 hours per audit reduced to 5 minutes.

Action item: Pick one of these use cases that matches your team’s highest-volume request type. Create a slash command that standardizes the analysis. Track time saved over your first 10 uses to quantify the ROI.

What Are the Best Practices for Marketing Data Teams?

Start with Read-Only Access

Configure your MCP server credentials with read-only permissions. Claude should query data, not modify it. This prevents accidental data corruption while you learn the tool. Upgrade permissions only after you trust the workflow.

Document Metric Definitions in CLAUDE.md

Metric definitions vary across teams. Your CAC calculation differs from the finance team’s CAC. Document your definitions in CLAUDE.md so Claude uses your team’s conventions. Update definitions when business rules change.

Build Skills for Repeatable Patterns

Every time you write a good prompt, consider whether it should become a skill. Attribution analysis, funnel analysis, and cohort analysis follow patterns. Skills encode those patterns so you do not reinvent them each session.

Use Commands for Standardized Reports

Weekly reports should produce consistent output. Commands enforce consistency. Everyone on your team running /weekly-campaign-report gets comparable results. This makes reports actionable instead of debatable.

Monitor Context Usage

Context fills faster than you expect. Large query results, schema explorations, and verbose outputs consume space quickly. Check context with /context periodically. Clear sessions before analysis quality degrades.

Validate SQL Before Trusting Results

Claude generates SQL accurately, but not perfectly. Review generated queries for the first few weeks. Check that table references, date filters, and metric calculations match your expectations. Trust increases with validation.

Use Sub-Agents for Complex Analysis

Complex analysis benefits from isolated context. Attribution analysis does not need to know about your funnel questions from earlier. Sub-agents provide fresh context for focused work. Delegate complex tasks to purpose-built sub-agents.

Version Control Your Configuration

Store CLAUDE.md, commands, skills, and agents in GitHub. Track changes over time. Roll back when experiments fail. Share configurations across team members through the repository.

Action item: Create a team playbook document that specifies which configurations belong in CLAUDE.md, which patterns should become skills, and which reports need commands. Review it quarterly as your usage matures.

What Does a 4-Week Implementation Look Like?

Week 1: Foundation

Install Claude Code and configure your development environment:

1. Run npm install -g @anthropic-ai/claude-code
2. Run claude and complete authentication
3. Create your project directory structure
4. Build initial CLAUDE.md with metric definitions and table schemas
5. Test five natural language queries against public data or CSVs

Verify basics work before connecting to production data.

Week 2: Data Connection

Connect Claude Code to your data warehouse:

1. Set up MCP server for BigQuery, Snowflake, or Databricks
2. Configure credentials securely using environment variables
3. Test connection with simple schema queries
4. Run three ad-hoc analysis queries to verify data access
5. Document any schema-specific quirks in CLAUDE.md

Ensure data access works reliably before building workflows.

Week 3: Workflow Development

Build skills and commands for recurring tasks:

1. Create your first skill for a common analysis pattern
2. Create three slash commands for weekly reports
3. Test each component with real analysis requests
4. Share commands with one team member for feedback
5. Refine based on actual usage

Focus on the highest-volume request types first.

Week 4: Team Rollout

Expand usage across your team:

1. Commit configurations to your team repository
2. Document setup instructions for team members
3. Train team on basic commands and context management
4. Track time saved on the first 20 analysis requests
5. Gather feedback and prioritize improvements

Measure ROI to justify continued investment.

Action item: Block two hours this week to complete Week 1 activities. Schedule Week 2 for next week. Maintain momentum through the full four weeks.

Final Takeaways

Claude Code transforms marketing data teams from SQL-writing bottlenecks into insight-generating accelerators. Natural language queries execute against live data warehouse connections.

Context management determines success. Monitor usage with /context, clear sessions before degradation, and use sub-agents for isolated analysis. Skills load specialized knowledge on-demand without bloating base context.

CLAUDE.md encodes your team’s metric definitions, table schemas, and business rules. Every session starts with this foundation. Keep it concise to preserve context capacity for actual analysis.

Slash commands standardize recurring reports. Weekly campaign performance, attribution analysis, and funnel breakdowns produce consistent output across team members when triggered via commands.

MCP servers connect Claude Code to BigQuery, Snowflake, and Databricks. Setup requires proper credentials and environment configuration. Start with read-only access until you trust the workflow.

The ROI is measurable. Track time per analysis request before and after Claude Code adoption. Teams report 60-80% reduction in time spent on ad-hoc queries.