Data Ingest & Storage
Data Ingest & Storage allows enterprise clients to ingest data from diverse sources, unifying and storing data into a common knowledge base that can be queried for insights and decision-making.
Data Ingest & Storage ingests unstructured text data, and detects underlying entities and relationships. As a result, data from different domains and formats are unified into a data model that is not only searchable and queryable but also is the foundation for subsequent analytics and modeling integrations.
Internal data can be linked to external data, including public-data repositories and licensed data feeds. External data is contextualized, when ingested and processed, to enrich the existing data models with information relevant to specific domains.
A highly-intuitive user interface promotes expert configuration for recurrent ingest from available data sources and mapping to datasets in QOMPLX’s underlying registry. This ensures that stakeholders’ knowledge-bases are kept up to date.
Containers are carried using several different modes of transport as they move from source to destination. Specialized commercial entities handle each mode and transition, and end-to-end transport requires containers to be tracked across diverse systems. Data Ingest and Storage can process and unify both streaming and batch data, giving users real-time insights on transport and transition times. Using Data Ingest and Storage reduces the unpredictability around delivery times and probability of damage, and ultimately sets the stage for optimal supply chain processes supported by informed decision-making.
Insurance companies track insured-risk across several parameters to prevent a concentration of risk building up within a portfolio. Insured clients have a variety of parameters including geographic regions, revenue-size bands and jurisdictions. Data Ingest & Storage can form a unified data-model across all of these variables, sourced from supplier and vendor contracts, policy wordings, and financial statements. This unified data model can then be searched, queried and/or aggregated to identify concentrations, and pro-actively price them right or reduce exposure over time.
Financial institutions obtain data on their customers from third party sources, structured or unstructured, for identity verification purposes. This third-party data needs to be transformed into a uniform structured format and then used to validate customer-provided information. QOMPLX's Data Ingest and Storage is a complete solution for this process. It can be configured to scrape customer-related information from external websites and transform this unstructured information into structured information. DIS also facilitates cross-verification from different sources, by first storing all gathered information into a unified data model. The resulting common knowledge-base has the benefit of providing insights on previously unseen correlations, and allowing for anomaly detections, leading to a more robustly-implemented identity verification.
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