CryspIQ® Data Methodology
Learn how the CryspIQ® methodology turns fragmented enterprise data into a governed, reusable model that reduces duplication and improves reporting trust.
Data Model
Learn how CryspIQ® decomposes source records into typed fact stores, shared dimensions and dated relationships, then reassembles them with a link key.
CryspIQ® Platform Architecture
Learn how source data reaches the CryspIQ® enterprise data model, where quality and security are applied, how data is consumed and what Azure requires.
CryspIQ® Platform Features
Explore CryspIQ® features for sources, mapping, operations, data quality, consumption and security across Lite, Professional and Enterprise plans.
CryspIQ® Platform Plug-ins
Learn how to build and connect CryspIQ® plug-ins to extend your enterprise data model with custom source integrations and transformation logic.
Lumen AI
Learn how the CryspIQ® Lumen AI layer supports data mapping, answers questions in business language against a consistent enterprise data structure, and lets assistants like Microsoft Copilot ask questions of enterprise data without writing SQL.
Co-Existence
CryspIQ® does not replace Snowflake, Databricks, Microsoft Fabric, Synapse or AWS. It can map from the raw or staging layer they already populate, as well as directly from source systems, and supplies the enterprise model they expect you to build — with no migration, and benefits arriving productivity first, time to value next, cloud cost savings last.
Comparisons
Every data approach answers a question. Kimball, Inmon, Data Vault, data lakes, Snowflake, Databricks, Microsoft Fabric, semantic layers, data virtualisation and MDM each answer a different one. CryspIQ® asks "Can I trust the answer?" Compare them, and see how they work together.
Compare CryspIQ®
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