From Passive Reporting to Active Systems.

Most B2B scaleups fail at data not because they lack tools, but because they lack Architecture. They treat analytics as a visualization problem ("How do I graph this?") rather than a systems problem ("How do I embed this logic into the business?").

My work is grounded in a specific, opinionated philosophy: The Warehouse-Native GTM Signal Methodology.

I do not sell "insights" or generic reports. I deliver the Revenue Architecture that turns your Data Warehouse into a self-driving revenue engine. I execute this through a strict three-phase framework: The Logic, The Architecture, and The Execution.

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1. The Logic: Uncover the Revenue Formula

Principle: "Find the Signal, Ignore the Noise."

Before we can build, we must define what we are building for. I do not guess what "Activation" or "Expansion" means; I uncover the specific Revenue Logic hidden in your customer behavior.

• Uncovering the Logic: Whether it is the friction points in your onboarding or the "Whale Signals" for expansion, I use your historical data to identify the specific behaviors that drive value.
• The Deliverable: We separate signal from vanity noise. This results in the Retention Analysis or PQA Scoring Recipe—a clear, agreed-upon definition of "Value" that aligns Product and Sales.

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2. The Architecture: Govern the Signal

Principle: "The Warehouse is the Single Source of Truth."

Once we have the logic, we must make it dependable. I do not build on "Islands of Partial Truth" (isolated dashboards). I build a governed, warehouse-native infrastructure where the signal is calculated once and trusted everywhere.

• Targeted Governance: I replace "track everything" with a Targeted Tracking Plan. We define exactly what events to track and how to calculate the metrics that matter, creating a "Source of Truth" dictionary.
• Resilience & Health: A signal is useless if it isn't dependable. I design the Metric Specifications to ensure the logic is robust and unambiguous. This ensures that when the "Active Customer" signal fires, it is accurate and consistent across every department. Furthermore, I define the Drift Detection Logic to monitor the health of the signal over time, alerting your team if data quality degrades or if the underlying behavior changes.
• Explainability: Black-box models are a liability. I ensure that every piece of logic is transparent and explainable, providing your team with the confidence to act on it.

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3. The Execution: Turn the Signal Into Action

Principle: "Embed the Signal in the Workflow."

The signal has zero value if it requires a user to open a new tab, another dashboard, yet alone a new tool to make it actionable. The goal is not Analysis; the goal is Workflow Integration.

• The Reference Implementation: Strategy must become code. I bridge the gap by delivering the Tracking Plan, SQL Recipes or dbt Models your team needs to deploy without ambiguity.
• Workflow Integration: I design the piping to embed the signal into the tools your team already uses. I provide the Reverse ETL Push Spec to send the signal directly to Salesforce, HubSpot, or Slack.