Foundations

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.

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.

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.

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.

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.

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.