Intended audience: developers administrators
AO Platform: 4.5
Data Governance & Ontologies
How does the platform handle data lineage, metadata management, and governance for AI models trained on enterprise data?
Our platform can track all the data changes within its semantic layer. The platform does not track data changes in the data engineering pipelines. We train the ML models (used for Quick Insights) on the customer’s data. These models are periodically refreshed to account for data drift.
What roles do ontologies and taxonomies play in your semantic modeling, and can business users extend them without deep technical skills?
Ontology is the foundation of our Semantic model. The richer the Ontology, the richer the semantic model and conversational analytics. We are in the process of enabling agent-based Ontology discovery and enrichment. Once this is available, business users can enrich and extend the ontology conversationally. We are also in the process of enabling version management of the ontologies.
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How do you ensure compliance with security and privacy standards like GDPR, SOC 2, and HIPAA?
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Encryption: All data is encrypted both at rest (AES-256) and in transit (TLS 1.2+).
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Access controls: Role-based access control (RBAC) with integration into enterprise identity providers (SSO, SAML, OAuth). Policies can be enforced at the row/column/MSO level.
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Audit logging: Full activity tracking (queries, API calls, data access events) for monitoring, investigation, and compliance reporting.
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Data Minimization: Only required data is ingested or virtualized. PII can be controlled using RBAC and external systems.
We are Soc2 Type 2 certified.
Are there built-in tools for data quality checks and version control for semantic models and ontologies?
We are not a data quality platform. However, our customers have utilized us to crowdsource the data issues. We also have annotation capabilities, where end users can tag a chart or visual and annotate for data issues, which can be workflowed to data engineering teams.
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