Dataarbeid

Dataarbeid is a Norwegian term for the practical, end-to-end work of making data usable for decision making, analysis, and operations. It encompasses activities from data collection and storage to processing, quality assurance, governance, privacy, and sharing across an organization.

Core activities include acquiring data, ingesting it into systems, cleaning and transforming it, integrating data from multiple sources, and creating metadata and data models. Data pipelines, ETL (extract, transform, load) or ELT (load, transform, load), and data orchestration are common approaches to automate these tasks.

Roles involved in dataarbeid include data engineers, data scientists, data stewards, data owners, and IT professionals. Organizations implement data governance frameworks, data quality metrics, and data lineage tracking to ensure reliability, compliance with privacy laws, and responsible data use.

Technological environments for dataarbeid typically include databases, data warehouses, data lakes, cloud platforms, and business intelligence tools. Data visualization and reporting are often the final outputs used by analysts and decision makers.

Common challenges are data silos, inconsistent definitions, poor data quality, privacy and security concerns, and limited resources. Successful dataarbeid relies on clear ownership, standardized data definitions, documentation, and ongoing monitoring of data quality and governance.