Datasheet : Universal Data Models

Accelerate your data model design efforts

Developed by renowned author and modeling expert Len Silverston for ER/Studio, these time-proven templates have been implemented by hundreds of companies that have benefitted from a complete set of data models that ensures quality, reduces development time and facilitates standardization of existing data models and definitions. 

Key Benefits

Achieve High-Quality Models

New data models can be laid out and categorized into subject data areas with ER/Studio’s easy-to-use format, improving the quality of existing data models. All of the Universal Data Model templates contain a full set of entities, relationships, attributes and detailed definitions. The models are also a great resource for enterprise data modeling, master data management, data warehouse, data governance, data quality, big data programs, canonical/semantic models, ontologies, and any effort where data models can help.

Reduce Development Time

The Universal Data Model templates arm users with a powerful set of data models and data warehouse designs that jump-start database development projects, reducing data model development and maintenance time by an average of 60%. They include proven models for common business functions such as ordering and managing products, handling shipments, invoicing, accounting and budgeting, managing human resources, contact management and project management. Users can save even more time with the industry-specific extensions for Healthcare, Telecommunications, Manufacturing, Banking, Investments & Financial Services, Insurance, Professional Services and Travel.

Improve Consistency and Standardization

Standardization of data models is often seen as a never achieved nice-to-have, rather than an essential ingredient of a data management and data quality program.The Universal Data Models enable users to be consistent with other organizations and improve quality assurance by referencing hundreds of integrated, “best practice” models. UDM customers can refer to a standard, proven source for models that are easily able to handle conflicting opinions regarding data modeling.

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