The modern business advantage is the data discovery and management tools implementation. Such tools are the key to create a basic level of enterprise data analytics. Automated tools facilitate data knowledge collection, but they will never replace «fundamental» knowledge.

Data management best practices, related to data modeling and architecture, support this knowledge. A mature data model is a complete understandable corporate information expressed in business language and organized into a structured model. Physical models in the form of snapshots of features and dataset structures are created for datasets subordinate to data architects. The challenge for architects is to match the physical layer with the logical layer, as a result of which they gain valuable knowledge about the data assets content and meaning. Data architecture provides a valuable knowledge base by combining collected semantic, logical and physical models with corporate information.

Data architects with semantic, logical, and physical models’ knowledge can provide quality measurements. Later organizations can use them as a part of their corporate data collection process. This knowledge expands automated tools capabilities, helps to concretize undiscovered details connected to data sets (business terms and corresponding definitions), helps to compare business terms with the structure, classification, characteristics of data sets.

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