Data Modeling Before Dashboards: Build Metrics People Can Trust
Use business definitions, entities, events, and trusted marts before investing in dashboard polish.
Data Foundations
Data Modeling
How to shape raw data into documented tables, shared definitions, and durable business concepts.
Warehouse First Analytics: Plain-English Guide
A practical introduction to using the data warehouse as the center of your analytics system.
Ownership And Runbooks: Plain-English Guide
A practical guide to deciding who owns data work, what a runbook should contain, and how to keep data systems reliable after the first build.
Legacy Reporting Migration: Plain-English Guide
How to move old reports into a modern data stack without losing metric trust, business context, or operational continuity.
Analytics Handoff: Plain-English Guide
How to pass business questions, metrics, models, and ownership from one team or system to another without losing trust.
Pipeline Freshness: Common Mistake
Why a successful pipeline run does not always mean the data is current, and how to model freshness so dashboards stay trustworthy.
Backfills: Common Mistake
The practical mistake that causes historical data repairs to create new trust problems instead of fixing old ones.
Customer Data Modeling: Common Mistake
Why most customer models fail by mixing people, accounts, subscriptions, and events into one unstable definition.
Metric Definitions: Operator Checklist
A practical checklist for defining metrics clearly enough that dashboards, data models, and business conversations stay aligned.
Source System Drift: Operator Checklist
A practical checklist for spotting, triaging, and controlling changes in source systems before they damage models, pipelines, and dashboards.
AI-Ready Data: Operator Checklist
A practical checklist for turning messy operational data into data that analytics, automation, and AI systems can safely use.
Warehouse First Analytics: Founder Framework
A practical way for founders to decide when the warehouse should become the center of reporting, modeling, and business measurement.
Legacy Reporting Migration: Founder Framework
A practical way to move old reports into a trusted data model without breaking the numbers the business still runs on.
Ownership And Runbooks: Founder Framework
A practical way for founders to make data systems accountable, recoverable, and less dependent on memory.
Analytics Handoff: Founder Framework
A practical way to move analytics out of the founder's head and into a trusted operating system.
Pipeline Freshness: Migration Playbook
A practical migration plan for making stale data visible, measurable, and fixable before users lose trust in the system.
Backfills: Migration Playbook
A practical beginner playbook for moving, rebuilding, or repairing historical data without breaking trust in the new model.
Customer Data Modeling: Migration Playbook
A practical way to redesign customer entities, identifiers, and history before migrating dashboards, pipelines, or CRM reporting.
Metric Definitions: Reliability Field Note
A practical note on making business metrics clear, testable, and stable enough for dashboards and decisions.
Source System Drift: Reliability Field Note
How small changes in operational systems quietly break models, pipelines, and dashboard trust.