Data Cataloging

Secure, organize and govern all your data

Anzo provides a newer, smarter approach to the data catalog by leveraging graph models encoding a Semantic Layer that describes the data in its business context, while capturing all types of disparate data, spanning all enterprise data sources and data sets. Regardless of whether data is to be sourced from applications, data warehouses or existing Hadoop platforms, the Anzo catalog provides a comprehensive overlay that links the detailed source schema information and contexts from any and all of these to the business meaning provided by its Semantic Layer. The same facilities also apply to knowledge graph data sets that are extracted from an organizations unstructured textual sources like documents and emails.


Anzo users may easily facet, browse and discover the multi-dimensional data sets of interest and understand their context in a secure and governed environment. Granular access to the catalog and all data sets it makes available is governed by a robust role and group based security mechanism. Organizations may augment their catalog with their own metadata structures to include useful business level concepts and metrics for data quality, privacy, regulation, confidentiality, data currency, stewardship, and more. Anzo interrogates all enterprise data sources to capture complete context from the incoming profiled data and from upstream and downstream data schemas.

Collaborative catalog features such as tagging and threaded discussions support communities of interest and the exchange of information surrounding data sets, mappings, models and even Hi-Res analytic dashboards.

Anzo graph models are represented using open data standards including World Wide Web Consortium (W3C) standards Web Ontology Language (OWL), Resource Description Framework (RDF), and the RDF Query Language (SPARQL). The use of open data standards throughout the Anzo product makes it simple to augment or import and export any aspect the metadata used to implement the catalog as well as its supporting metadata artifacts like schemas, semantic models and mappings. Graph data models have been proven to provide superior flexibility, pushing the modeling, integration, linking and analytics decisions closer to end users. Graphs are uniquely well-suited to leverage rapidly evolving standards and vocabularies in multiple industries, including Financial Industry Business Ontology (FIBO), Clinical Data Interchange Standards Consortium (CDISC), and others.

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