datafusiestap

Datafusiestap is a term used in data integration and analytics to describe the step in a data processing pipeline where data from multiple sources are merged into a unified dataset. The term is not universally standardized and may appear as a product- or organization-specific label for the data fusion stage within ETL or ELT workflows.

In practice, the datafusiestap encompasses schema alignment, deduplication, entity resolution, value reconciliation, normalization, and enrichment. It aims to produce a canonical, queryable dataset along with metadata and provenance that support traceability and governance.

Core activities include schema mapping across sources, detection and merging of duplicate or conflicting records, applying data quality rules, and generating master records or canonical representations. The output typically consists of a merged dataset, a schema model, and lineage information.

Techniques commonly used in datafusiestap include deterministic and probabilistic record linkage, machine learning-based entity matching, schema matching, and canonicalization. Organizations may integrate this step with Master Data Management, data quality, and data governance practices to improve consistency and trust.

Applications span customer data platforms, supplier and product data integration, sensor and IoT data fusion, and cross-domain analytics. Challenges include data privacy, scalability with large datasets, handling streaming sources, and maintaining audit trails for compliance.

See also: data fusion, entity resolution, master data management.