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Solution Features Simple and secure integration of siloed data

Feature

Simple and secure integration of siloed data

Use our DISQOVER data ingestion engine to transparently merge and link your siloed data sources, including third-party and public data sources, while retaining data provenance and traceability.

Simplify data integration using data pipelines

DISQOVER’s data ingestion engine simplifies the import, transformation, and integration process for all your data sources. The extraction, transformation, and loading of data happens in one place, managed via a graphical web frontend. As a data scientist, you can design the data ingestion process as a visual pipeline, requiring only basic and non-specialized IT skills.

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Integrate internal data with third-party and public data sources

The DISQOVER data ingestion engine transforms various data sources into a single, densely linked knowledge graph, creating links between data entities and inferring new links based on existing ones. Aggregate data from an unlimited number of sources, including public and third-party data, thus integrating your own data with what’s available in the public domain. DISQOVER comes with 140+ integrated data sources that are ready to use.

No long or expensive tracks are needed to connect your data resources. Integrating your first databases in DISQOVER is done in collaboration with ONTOFORCE, and every subsequent integration can be established by you.

Easily run data audits and ensure traceability

The data provenance of information is a key aspect of DISQOVER and is prominently featured on the platform. The data ingestion engine has built-in tools to trace dependencies throughout the pipeline, keeping track of the data sources that are used as input and the data points produced as output.

This traceability facilitates a high degree of transparency, allowing domain experts and auditors to understand and assess the activities of the data ingestion process.

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Embedded quality control

The data ingestion engine has powerful built-in tools for data quality control. Data QC is process-oriented, which means that you can configure QC checks to verify the shape of the incoming source data. Real-world data is often imperfect and incomplete, and therefore you can define tolerance-based data quality checks, with the ability to set warning and error thresholds.

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