Acquiring data from operational systems for loading into data warehouses, business intelligence and analytics systems.
Ontology 4 reduces the timescales and cost of acquiring data from operational systems, enterprise applications as well as enterprise documents. It links and transforms data from multiple sources for loading into Data Warehouses, BI and Analytics.
Ontology is able to accept source data in virtually any format, and it's graph-based, semantic model technology means it can address the most complex of data integration challenges. It thrives in complex environments where there are many different sources and types of incoming data that feature difficult to link data, and unlike traditional schema-based approaches to data integration, it easily handles late changes in project requirements.
Ontology's inherently agile technology allows data acquisition 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.