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Agile Data Integration (nazwa własna)
Ontology tackles data acquisition, data correlation and data migration through agile graph and semantic technology that makes complex projects more manageable and predictable, seamlessly handles mid-project changes and delivers results quicker at lower cost.
Ontology addresses the same business challenges as traditional data integration technologies, but we approach the problem from a fundamentally different perspective. Inspired by the way that search engines make insightful information available without the need to integrate the Internet, we set about building a solution to data integration problems without the integration. We call our approach Search, don't integrate.
Ontology's semantic search approach couples graph technology with semantic models to link and search data from different systems. Unlike traditional, schema-based data integration, this approach allows us to tackle some of the most complex data integration challenges and to fully embrace Agile or incremental project management methodologies. The result is a radically different approach that makes projects more manageable and predictable, handles mid-project changes in requirements easily and delivers results quicker at lower cost.
Data Integration Scenarios
Ontology 4 addresses data acquisition, data correlation and data migration problems. We acquire data for Business Intelligence and Data Warehousing initiatives, correlate and consolidate data for MDM projects and seamlessly combine and align data from multiple sources for system migration and data conversion projects.
Ontology 4's unique approach allows it to address the type of complex data integration challenges that cause traditional, schema-based solutions to struggle. Ontology's flexibility in handling changes in the type and number of data sources, or in output requirements makes it suitable for projects where the project parameters are not known upfront or are expected to change.
Ontology 4 thrives in situations where there is a high degree of misalignment between the data in different sources. The flexibility of Ontology 4's graph-based semantic models makes it easy to deal with a large number of incoming data sources in virtually any format. Where traditional relational keys are not able to link data, Ontology is able is able to use semantic inference to identify the same entity in different data sources.
Ontology is able to model the sort of complex, multi-tier, multi-layer network relationships across multiple data sources that are difficult or impossible to map in the relational world. When the data that describes these graphs is fragmented across multiple systems, described with low quality or lacks the relational linkages, Ontology comes into its own. And as Ontology starts by examining data and not the schemas of the data sources being linked, it is able to determine project feasibility and data quality issues early, mitigating the risk that may otherwise delay or derail projects.