Datasupports

Datasupports is a broad term used in information management to describe the set of artifacts, processes, and technologies that provide data with context, quality, and governance to enable trustworthy use. It covers metadata, governance frameworks, and tooling designed to improve data discoverability, reliability, and security.

Key data-supporting components include:

- Metadata repositories and data catalogs

- Data dictionaries and schemas

- Data lineage and provenance tracking

- Master data management and reference data

- Data quality controls, profiling, and validation rules

- Access control, data security, and privacy mechanisms

- Data models, ontologies, and semantic layers

- Data stewardship and governance processes

Datasupports underpin data integration, analytics, reporting, regulatory compliance, and data democratization by enabling discoverability, traceability, and quality across data pipelines. They facilitate consistent meanings across systems, support auditability, and help align data usage with organizational policies.

In practice, datasupports are developed and maintained by roles across the data team, including data engineers who build pipelines, data architects who design models, and data stewards who manage quality and policy. They are implemented within data platforms such as data warehouses, data lakes, and cloud services, and integrated with ETL or ELT processes and business intelligence tools.

Standards and frameworks commonly referenced in datasupports include DAMA-DMBOK, ISO/IEC 8000, and data governance or data quality best practices. By providing structure, governance, and traceability, datasupports aim to improve data reliability and enable informed decision-making across an organization.