Datalinian

Datalinian is a neologism used in data science and information culture to describe an approach to data handling and presentation that emphasizes provenance, quality, reproducibility, and interpretability.

As an adjective and a noun, datalinian can modify practices and artifacts, for example a datalinian practice or a datalinian dashboard. This usage highlights aims such as end-to-end data lineage, explicit metadata, robust validation, and transparent communication of uncertainty to stakeholders. Datalinian dashboards typically display data sources, transformation steps, versioned datasets, and audit trails to enable reproducibility.

The term emerged in the 2020s within informal discussions, blogs, and industry roundups. It lacks formal standardization, and definitions vary by organization. Critics note that, as a neologism, datalinian can be vague without concrete criteria.

Datalinian shares common ground with data governance, data provenance, reproducible research, and explainable AI. It complements user-centered data visualization and data storytelling by codifying accountability and clarity about data quality and analytical methods.

Datalinian remains an informal, context-dependent label; its value lies in encouraging clearer data provenance, reproducibility, and explainability in data products and decision-support tools. Related concepts frequently discussed alongside it include data governance, data provenance, reproducible research, explainable AI, and transparent data visualization.