dataanskaffelse

Dataanskaffelse is the process of obtaining data from various sources to be used for analysis, decision-making, or operations. It encompasses identifying data needs, sourcing data, acquiring rights to use it, and integrating it into a storage and analytics infrastructure. Data can come from internal systems, external partners, public datasets, open data portals, sensor networks, APIs, web scraping, or commercial data providers. The acquisition process typically includes defining data requirements, negotiating licenses or terms of use, and ensuring compliance with legal and ethical standards.

Once obtained, data is prepared through validation, cleaning, de-duplication, and normalization. Metadata and data lineage are tracked to support governance and reproducibility. Data is then stored in suitable repositories such as data warehouses or data lakes, often using ETL or ELT workflows and cataloging to facilitate discovery and governance. Data quality, provenance, timeliness, and completeness are key concerns; quality checks and validation rules are common.

Governance considerations include data ownership, access controls, retention policies, and privacy protections. In many jurisdictions, data collection and usage must comply with privacy and data protection laws (for example GDPR). Licensing terms and data rights for redistribution or commercial use must be respected.

Applications of dataanskaffelse include business intelligence, forecasting, research, and training of AI/ML models. Challenges include data heterogeneity, cost, integration complexity, security, and maintaining data quality over time. Best practices emphasize clear data ownership, documentation, data catalogs, quality assurance, and audit trails.