AI-Ready Data: Build the Foundation Before the AI Project
How to prepare trusted, governed, well-described data so AI workflows can use it safely and with less ambiguity.
AI-Ready Data
AI-Ready Data
Prepare clean, governed, contextual data so AI systems have something trustworthy to use.
Backfills: Plain-English Guide
How to safely rebuild historical data after code changes, late arrivals, migrations, or broken pipelines.
Customer Data Modeling: Plain-English Guide
A practical guide to defining customers, accounts, events, and relationships so analytics and AI systems can trust the data they use.
Pipeline Freshness: Plain-English Guide
A practical way to define, measure, monitor, and repair whether data is arriving when the business expects it.
Metric Definitions: Common Mistake
The mistake is treating a metric name as a definition. Learn how to define metrics so dashboards, teams, and AI systems can use them consistently.
Source System Drift: Common Mistake
The mistake is assuming the operational system you connected to yesterday will keep meaning the same thing tomorrow.
AI-Ready Data: Common Mistake
The mistake is treating AI readiness as a cleanup task instead of a data system capability.
Warehouse First Analytics: Operator Checklist
A practical checklist for building analytics around a governed warehouse instead of scattered tool-specific copies of business data.
Legacy Reporting Migration: Operator Checklist
A practical checklist for moving old reports into a trusted, AI-ready data foundation without recreating the same problems in newer tools.
Ownership And Runbooks: Operator Checklist
A practical checklist for assigning data ownership, writing useful runbooks, and making data systems safer to operate.
Analytics Handoff: Operator Checklist
A practical checklist for moving reports, metrics, datasets, and analytical ownership without breaking trust.
Pipeline Freshness: Founder Framework
A practical way for founders to define, measure, and repair data freshness before dashboards, automations, or AI workflows lose trust.
Backfills: Founder Framework
A practical way to decide when, why, and how to replay historical data without breaking trust in the system.
Customer Data Modeling: Founder Framework
A practical way for founders and operators to define customers, accounts, events, and metrics before dashboards or AI workflows depend on them.
Metric Definitions: Migration Playbook
A practical playbook for moving from dashboard-specific formulas to trusted, reusable metric definitions.
Source System Drift: Migration Playbook
A practical way to find, classify, and control source changes before they break a migration or weaken AI-ready data.
AI-Ready Data: Migration Playbook
A practical sequence for moving from scattered, unreliable data to governed data products that can support analytics, automation, and AI use cases.
Warehouse First Analytics: Reliability Field Note
A practical way to make dashboards, metrics, automation, and AI use the same trusted data foundation.
Legacy Reporting Migration: Reliability Field Note
How to move old reports into a modern data stack without breaking trust, losing definitions, or creating prettier confusion.
Ownership And Runbooks: Reliability Field Note
A practical note on turning unclear data responsibility into reliable operations for AI-ready data systems.