Datacentric

Datacentric, or data-centric, describes an approach to information systems in which data assets are the central design driver. In this paradigm, requirements, architecture, and governance are organized around data models, data quality, and data interoperability rather than around applications or processes alone.

Key principles include a single source of truth, explicit data ownership, standardized schemas, and metadata-driven governance. Data lineage and quality controls are essential to ensure trust and usability across domains. The goal is to treat data as a first-class asset that can be shared, reused, and analyzed consistently.

Practices associated with datacentric design include formal data modeling, metadata management, data integration, and the use of data platforms such as data catalogs, data lakes, or data mesh implementations. Interoperability is favored through open standards and well-defined APIs, enabling cross-system access and reuse of data assets.

Benefits of a datacentric approach include more consistent analytics, easier data reuse, and faster, more reliable decision-making. Challenges involve governance overhead, handling privacy and security requirements, and the organizational change required to elevate data from a byproduct of systems to a strategic asset.

In practice, datacentric strategies underpin enterprise data programs, data governance initiatives, and AI efforts that rely on clean, well-governed data. It is often contrasted with application-centric or process-centric approaches and is closely related to modern concepts such as data governance, data platforms, and data mesh.