Building a Modern Data Stack for Revenue Teams

Revenue teams are drowning in tools but starving for data. The average B2B GTM organization uses 20-30 different applications, each creating its own data silo. Customer information is fragmented across CRM, marketing automation, product analytics, enrichment providers, and dozens of point solutions.

The modern data stack solves this by creating a unified data layer that powers intelligent revenue operations. This guide covers how to build the data infrastructure your revenue team needs.

The Revenue Data Problem #

Data Fragmentation Reality

Typical GTM data sources:

• CRM (Salesforce, HubSpot)
• Marketing automation (Marketo, Pardot)
• Product analytics (Amplitude, Mixpanel)
• Customer success (Gainsight, Totango)
• Enrichment (Clearbit, ZoomInfo)
• Intent (Bombora, G2)
• Sales engagement (Outreach, Salesloft)
• Conversation intelligence (Gong, Chorus)
• Support (Zendesk, Intercom)
• Billing (Stripe, Chargebee)

Each system has a different view of the customer. None tell the complete story.

Consequences of Fragmentation

Modern Data Stack Architecture #

The Layers

flowchart BT
    Sources[SOURCE SYSTEMS<br/>CRM, MAP, Product, Enrichment, etc.]
    Ingestion[INGESTION LAYER<br/>ETL/ELT: Fivetran, Airbyte, etc.]
    Storage[STORAGE LAYER<br/>Data Warehouse: Snowflake, BigQuery]
    Transform[TRANSFORMATION LAYER<br/>dbt, SQL transforms, Models]
    Activation[ACTIVATION LAYER<br/>Reverse ETL, Orchestration, Applications]

    Sources --> Ingestion
    Ingestion --> Storage
    Storage --> Transform
    Transform --> Activation

Layer 1: Data Sources

Everything that generates revenue-relevant data:

Customer Relationship Data

• CRM opportunities, accounts, contacts
• Sales activities and emails
• Meeting notes and call recordings

Marketing Data

• Campaign engagement
• Website behavior
• Content consumption
• Ad performance

Product Data

• User signups and activations
• Feature usage
• Trial behavior
• In-app events

Third-Party Data

• Firmographic enrichment
• Intent signals
• Technographic data
• Contact information

Financial Data

• Revenue and billing
• Subscription status
• Payment history

Layer 2: Data Ingestion

Moving data from sources to storage:

ETL/ELT Platforms

Ingestion Patterns

Layer 3: Data Storage

The central repository for all data:

Cloud Data Warehouses

Storage Best Practices

• Organize by source system (bronze/raw)
• Create transformed models (silver/staging)
• Build business models (gold/marts)
• Implement data retention policies
• Manage access controls

Layer 4: Data Transformation

Converting raw data into usable models:

Transformation Tools

Revenue Data Models

Essential models for revenue teams:

-- Unified Account Model
accounts_unified:
  - account_id
  - company_name
  - industry
  - employee_count
  - funding_stage
  - tech_stack
  - icp_score
  - engagement_score
  - intent_score
  - pipeline_value
  - revenue_total
  - health_score

-- Unified Contact Model
contacts_unified:
  - contact_id
  - account_id
  - email
  - first_name, last_name
  - title, department
  - seniority
  - persona
  - engagement_score
  - last_activity_date
  - source

-- Engagement History
engagement_events:
  - event_id
  - account_id
  - contact_id
  - event_type
  - event_source
  - event_timestamp
  - event_properties

Layer 5: Data Activation

Getting insights back to operational systems:

Reverse ETL

Push warehouse data to SaaS applications:

Activation Use Cases

Building Revenue Data Models #

The 360° Customer View

Combine all data sources into unified models:

flowchart TB
    subgraph Account360[ACCOUNT 360° PROFILE]
        subgraph Firmographic
            F1[Company size, Industry<br/>Revenue, Location]
            F2[Source: Clearbit, ZoomInfo]
        end
        subgraph Technographic
            T1[Tech stack, Integrations]
            T2[Source: BuiltWith, G2]
        end
        subgraph Engagement
            E1[Website, Email<br/>Product, Sales]
            E2[Source: Analytics, MAP<br/>Product analytics, CRM]
        end
        subgraph Intent
            I1[Topic intent, Signals]
            I2[Source: Bombora, G2]
        end
        subgraph Revenue
            R1[Pipeline, Revenue, Health]
            R2[Source: Salesforce, Stripe<br/>CS platform]
        end
    end

Key Revenue Metrics Models

Pipeline Model

pipeline_metrics:
  - snapshot_date
  - pipeline_total
  - pipeline_by_stage
  - pipeline_by_source
  - weighted_pipeline
  - coverage_ratio
  - new_pipeline_created
  - pipeline_moved_forward
  - pipeline_moved_backward
  - pipeline_closed

Funnel Model

funnel_metrics:
  - period
  - visitors
  - leads
  - mqls
  - sqls
  - opportunities
  - won
  - conversion_rates_by_stage
  - velocity_by_stage

Account Scoring Model

account_scores:
  - account_id
  - icp_fit_score
  - engagement_score
  - intent_score
  - health_score
  - expansion_score
  - composite_score
  - score_tier
  - score_change_7d
  - score_change_30d

Implementation Roadmap #

Phase 1: Foundation (Months 1-2)

Objectives

• Select and configure warehouse
• Implement core integrations
• Build basic data models

Actions

• Choose warehouse (Snowflake/BigQuery)
• Set up Fivetran/Airbyte for key sources
• Connect CRM, MAP, product analytics
• Build initial staging models
• Establish data governance basics

Phase 2: Core Models (Months 3-4)

Objectives

• Build unified customer models
• Create revenue metrics
• Enable basic activation

Actions

• Build account and contact unification
• Create engagement aggregation
• Build pipeline and funnel models
• Set up reverse ETL for key use cases
• Deploy initial dashboards

Phase 3: Advanced Analytics (Months 5-6)

Objectives

• Implement scoring models
• Add predictive capabilities
• Enable self-serve analytics

Actions

• Build scoring models (ICP, engagement, health)
• Integrate intent data
• Add attribution modeling
• Enable business user access
• Implement data quality monitoring

Phase 4: Optimization (Ongoing)

Objectives

• Scale and optimize
• Add advanced use cases
• Improve data quality

Actions

• Performance optimization
• Additional data sources
• ML model integration
• Data quality automation
• Documentation and governance

Cargo in the Modern Data Stack #

Cargo serves as the revenue activation layer:

Data Unification

• Connect directly to sources and warehouse
• Real-time data synchronization
• Identity resolution across sources

Workflow Orchestration

• Trigger workflows from data changes
• Multi-system coordination
• Signal-based automation

Operational Activation

• Push insights to operational systems
• Enable sales and marketing actions
• Close the loop on data

flowchart LR
    Sources --> Warehouse --> Cargo --> OpSys[Operational Systems]
    Warehouse --> Analytics
    Cargo --> Workflows

Data Stack Best Practices #

Best Practice 1: Start with Use Cases

Don’t build infrastructure for its own sake. Start with:

• What decisions need better data?
• What processes need automation?
• What insights are we missing?

Best Practice 2: Own Your Data

Your data warehouse is your strategic asset:

• Centralize data under your control
• Don’t depend solely on vendor silos
• Build institutional data knowledge

Best Practice 3: Invest in Quality

Bad data scales faster than good data:

• Implement data quality checks
• Monitor for anomalies
• Document data lineage
• Establish ownership

Best Practice 4: Enable Self-Serve

Data teams shouldn’t be bottlenecks:

• Build intuitive data models
• Create documentation
• Train business users
• Provide appropriate access

Best Practice 5: Plan for Scale

Your data needs will grow:

• Choose scalable infrastructure
• Design for future sources
• Build modular components
• Monitor costs

Common Data Stack Mistakes #

Mistake 1: Tool Proliferation

Buying tools before defining needs.

Fix: Start with use cases, then select tools.

Mistake 2: Ignoring Data Quality

Building on a foundation of bad data.

Fix: Invest in data quality from day one.

Mistake 3: Over-Engineering

Building for future needs that may never come.

Fix: Start simple, iterate based on actual needs.

Mistake 4: No Governance

Data becomes a mess without rules.

Fix: Establish ownership, documentation, and standards.

Mistake 5: Siloed Teams

Data team builds without business input.

Fix: Embed data team with revenue operations.

Key Takeaways #

1. Unified data beats fragmented tools
2. The warehouse is your single source of truth
3. Activation closes the loop on insights
4. Quality matters more than quantity
5. Start simple, scale with needs

The modern data stack enables revenue teams to move from reactive to proactive, from guessing to knowing, from manual to automated. Build the foundation right, and everything else becomes possible.

Ready to build your revenue data stack? Cargo provides the activation layer that turns warehouse data into intelligent revenue operations.

Key Takeaways #

• Revenue teams are drowning in tools but starving for data: average GTM org uses 20-30 apps, each creating its own data silo
• Five stack layers: sources → ingestion (Fivetran/Airbyte) → storage (Snowflake/BigQuery) → transformation (dbt) → activation (reverse ETL)
• The warehouse is your single source of truth: own your data infrastructure—don’t depend solely on vendor silos
• Data quality matters more than quantity: bad data scales faster than good data—invest in quality from day one
• 6-month implementation roadmap: foundation (months 1-2) → core models (3-4) → advanced analytics (5-6) → ongoing optimization

Frequently Asked Questions #