Analytics in Practice
Data Team làm gì?
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.
·5 min read
Explore all articles on data analytics, AI, and data governance.
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.
Data quality is best defined as fitness for use and must be expressed as measurable requirements, not a vague idea of “clean data.” Using common dimensions—accuracy, completeness, consistency, timeliness, validity, and uniqueness—organizations can implement governance, controls, and monitoring that make data reliable for reporting, operations, and analytics.
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.
A first data pipeline succeeds when it is treated as a managed data product with clear consumers, service levels, security controls, and measurable data quality. This article outlines a practical reference architecture, common operational realities (schema change, backfills, monitoring), and best practices from data management, architecture, and analytics engineering disciplines.
Good data is “fit for use”: it meets explicit, measurable quality requirements for a specific business context. Organizations typically define these requirements using common data quality dimensions (accuracy, completeness, consistency, timeliness, validity, and uniqueness), then operationalize them with governance ownership, automated validation, and continuous monitoring across the data lifecycle.
An ETL mental model treats data pipelines as staged, governed movement of data from sources to curated, consumable products. By combining clear layer boundaries, data contracts, and quality gates across accuracy, completeness, consistency, timeliness, validity, and uniqueness, teams can build pipelines that are repeatable, observable, and fit for defined business use cases.
A data warehouse is a dedicated analytical system that integrates data from multiple operational sources, preserves history, and enables consistent reporting and BI at scale. It reduces risk to OLTP performance while providing governed definitions, quality controls, and repeatable transformations for enterprise analytics.
Data types span both technical storage formats (e.g., SQL types) and semantic domains that define meaning, rules, and valid operations in analytics. Defining reusable domains, mapping them consistently across platforms, and enforcing them with constraints and automated tests reduces type drift, improves data quality, and supports reliable reporting.
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.
Data modeling defines how data is structured, related, and constrained so it can be stored, integrated, and used reliably. This article introduces core modeling concepts, the conceptual/logical/physical levels, and common approaches such as normalized modeling, dimensional modeling, and Data Vault, with practical guidance for building governable analytics-ready datasets.
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.
Good data quality is best defined as fitness for use: measurable requirements that ensure data supports a specific decision or process. Organizations typically specify quality using dimensions such as accuracy, completeness, consistency, timeliness, validity, and uniqueness, then operationalize them through rules, thresholds, monitoring, and accountable ownership.
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.
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.
Effective data governance does not require heavy committees and paperwork; it requires clear decision rights, accountable owners, and controls embedded into daily data delivery. By defining measurable data quality expectations and automating checks and monitoring, organizations can improve trust and compliance while minimizing process overhead.
Privacy by Design embeds privacy requirements into the architecture, governance, and lifecycle controls of data systems so privacy protections are built in rather than added later. It is operationalized through enforceable practices such as minimization, purpose-bound access, secure processing, controlled sharing, and automated retention and deletion aligned with frameworks like DAMA-DMBOK, TOGAF, NIST, and ISO/IEC 27701.
Many data catalogs fail because the underlying metadata management practice is weak: metadata becomes stale, definitions don’t match business language, trust signals are missing, and the catalog is not embedded in daily workflows. A sustainable catalog treats metadata as a managed asset with automation, stewardship, governance decision rights, and measurable outcomes like discovery success and fitness-for-use signals.
Metadata management operationalizes “data about data” so people can discover, understand, trust, and govern data assets. A sustainable approach connects technical, business, and operational metadata through standards, tooling, and stewardship workflows embedded in the data delivery lifecycle.
Putting an ML model into production requires far more than exporting a trained artifact: it demands consistent feature computation, rigorous versioning and lineage, safe deployment practices, and continuous monitoring across data, model behavior, and system health. Treating ML as a governed lifecycle (data + model + operations) is essential to maintain reliability as data and business conditions change.
Data lineage tracking documents how data originates, transforms, and is consumed across systems. It underpins governance, data quality management, and change impact analysis by connecting technical metadata (pipelines, schemas, code) with business context (owners, definitions, metrics).
Feature engineering is the disciplined process of turning raw, time-dependent data into reliable model inputs that are correct at prediction time. Effective practice combines standard transformation patterns, domain-driven definitions, and strong controls for data quality, leakage prevention, and training/serving consistency. Operationalizing features requires governance, documentation, versioning, and monitoring—often supported by (but not replaced by) a feature store.
Model bias in machine learning is a lifecycle issue that can enter through problem framing, data collection, labeling, feature design, optimization objectives, and deployment feedback loops. Effective practice combines subgroup measurement using complementary fairness and performance metrics with targeted mitigations and strong governance, documentation, and ongoing monitoring.
MLOps is the discipline of operating machine learning systems end to end, combining DevOps-style automation with data management controls such as governance, metadata, and data quality. It focuses on repeatability, traceability, controlled deployment, and production monitoring so models remain reliable as data and business conditions change.
Model monitoring is an operational control that tracks data quality, feature and prediction stability, model performance, and business outcomes to detect drift and prevent silent degradation. A robust approach combines statistical drift measures with governance, versioning, and an incident-ready workflow for rollback or retraining.