Everything on this blog is organized along the data lifecycle, from how data is collected to how it gets activated. Here are the six areas and the topics they cover.
Collection
How data is captured at the source: event design, tracking implementation, server‑side collection and identity.
- Event Data: Event schemas, taxonomies, tracking plans, naming conventions.
- Tracking Implementation: Tag managers, web/app SDKs, dataLayer, QA and debugging.
- Server-Side Tracking: Server‑side tagging, Conversions API, edge collection, first‑party endpoints.
- Identity Resolution: Identifiers, stitching, cross‑device, user deduplication.
- First-party & Zero-party Data: Owned and declared data, direct collection strategies.
Platform
Where data lands and gets shaped: pipelines, warehouses, modeling and orchestration.
- Integration & Pipelines: Ingestion, ELT, connectors, CDC, pipeline reliability.
- Modeling: dbt, dimensional modeling, staging / intermediate / mart layers.
- Warehouse & Lakehouse: Analytical storage, cost, performance, partitioning, open formats.
- Orchestration: Scheduling, data CI/CD, dependency graphs, deployment.
- Real-time & Streaming: Low latency, event‑driven architectures, change data capture.
Trust
What makes data reliable and lawful to use: quality, governance, privacy and security.
- Quality & Observability: Testing, anomaly detection, freshness, monitoring, alerting.
- Governance: Ownership, catalogs, lineage, data contracts, documentation.
- Privacy & Consent: Privacy by design, consent management, consent rates, modeling the missing.
- Regulation & Security: GDPR, DMA, ePrivacy, PII handling, encryption, access management.
Analysis
Turning data into answers: methods, product and marketing measurement, experiments, metrics and BI.
- Analytical Methods: Exploration, applied statistics, SQL craft, analytical reasoning.
- Product Analytics: Funnels, retention, cohorts, in‑app behavior.
- Marketing Measurement: Attribution, marketing mix modeling, incrementality, geo experiments.
- Experimentation: A/B testing, feature flags, causal inference, statistical power.
- Metrics & Semantic Layer: Metric definition, standardization, semantic layer, metric store.
- Visualization & BI: Dashboards, self‑service, data storytelling, adoption.
Activation
Putting data to work in tools and decisions: reverse ETL, CDPs, predictive models and personalization.
- Reverse ETL & Sync: Reverse ETL, syndication, operational audiences, downstream sync.
- Customer Data Platform: Segmentation, unified audiences, customer profiles.
- Predictive Modeling: Scoring, churn, LTV, propensity, forecasting, model deployment.
- Personalization: Targeting, recommendations, dynamic content, decisioning.
AI & Agents
Applying language models to data work: text‑to‑SQL, agents, LLM engineering and AI governance.
- Agents & Automation: Autonomous agents applied to data workflows, tool use, multi‑step tasks.
- Text-to-SQL & Copilots: Conversational interfaces over data, analytics assistants, natural‑language querying.
- LLM Engineering: RAG, embeddings, evaluation, prompt design, cost, productionization.
- AI Governance: Model traceability, hallucinations, trust, auditability, AI Act.