Data Enrichment

SDP’s in-built NLP and ML-based engines allow the automation of

⦁ Named Entity Recognition

⦁ Concept Extraction using domain taxonomies or controlled vocabularies

⦁ Summarization

⦁ Classification to Taxonomy

⦁ Business relevancy terms

⦁ Creation of Domain Taxonomies or Ontologies

The above processes are automated, and UIs are available to review the outcomes by Subject Matter Experts and Data Stewards to ensure accuracy and validity. In addition, SDP offers a configurable workflow to manage the content from acquisition and ingestion to enrichment and delivery. In areas where we observe a high data accuracy and consistency, the platform can also be configured as an RPA process. In this case, only data with missing/invalid points (exceptions) are presented to the analyst to enrich for human augmentation if mandated. We develop ML models for project-specific requirements while using generic ML models like NERs and Summarizers to quickly onboard projects, as needed. The platform delivers accuracy at scale, with classification, detection, segmentation, and annotation tools that enable quick and accurate data labeling for any use case. We design and tailor the required ontology and instructions as per the use case.