Agile conversion of data from multiple sources to support data migration and consolidation efforts
Ontology 4 handles data migration or conversion from one or multiple input data sources, and decreases the timescales and cost of legacy replacement and consolidation efforts. Ontology's ability to link data in virtually any data format across misaligned data sources, stems from its flexible graph-based semantic models. Unlike traditional approaches, Ontology 4 de-couples the rigid schema link between source and destination data formats and places a semantic model in between. This decoupling, together with detailed fallout reporting allows correct data mapping and transformation logic to be evolved through a series of incremental stages which speeds up the migration process and improves accuracy. And because every Ontology project starts by exploring the data, the level of data quality and integrity is understood early in the process which reduces the risk of project overruns of failures.
Ontology's inherently agile technology allows data migration initiatives to be managed by Agile or incremental methodologies. Solutions are delivered using the Ontology Data Integration Process, via a series of 2 week Discover, Design, Develop, Test "sprints", which means that changes in requirements such as the adding of additional data sources, or the changing of output formats that can delay or derail traditional Data Integration projects, can be easily captured and accommodated at any time. This approach ensures that requirements are fully met, de-risks projects and reduces timescales and costs.
Read how Ontology is rethinking data acquisition, data correlation and data migration projects for a post-Google world in our Agile Data Integration white paper.