Analytics in Practice

Làm data là một hành trình dài, nhiều lúc mệt, nhiều lúc cô đơn, nhưng nếu làm đúng – bạn đang đứng ở trung tâm của những quyết định quan trọng nhất trong tổ chức.

Hành trình của tôi bắt đầu từ vai trò Data Analyst, nơi công việc không chỉ là viết SQL hay dựng dashboard, mà là chuyển dữ liệu thành quyết định.

A/B testing at scale requires standardized instrumentation, governed metric definitions, automated data quality checks, and a repeatable experimentation lifecycle. By treating experimentation as a managed data product—supported by a semantic layer, robust logging, and operational guardrails—organizations can run many concurrent tests while maintaining trustworthy decisions.

SQL performance tuning is a disciplined process for improving query latency, throughput, and predictability without changing results. It combines plan-based diagnosis, query rewrites, indexing and statistics management, and workload-aware modeling to meet measurable performance requirements.

Effective dashboards start with decision needs, governed metric definitions, and trustworthy data quality—not chart selection. By applying an information hierarchy, accessible visual design, and a semantic-layer-driven delivery lifecycle, dashboards become reliable decision-support products rather than collections of disconnected metrics.

Reliable data pipelines consistently deliver datasets that meet explicit requirements for data quality, timeliness, and correctness. Building them requires combining data quality dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness) with engineering practices such as testing, observability, idempotent processing, and governed change management.

Analytics translation is the structured process of turning a business decision into precise, governed metric definitions and implementable data requirements. When terms, grain, time rules, and lineage are implicit, teams deliver dashboards that are technically correct but semantically inconsistent, eroding trust.