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Enterprise data architecture refers to a collection of master blueprints designed to align IT programs and information assets with business strategy. Data architecture is used to guide data integration, data quality enhancement and successful data delivery.
Enterprise data modeling is an essential component of strong enterprise data architecture, using subject, conceptual and enterprise logical models based on business concepts and requirements to guide and support the development of effective data operations. Application data models support database management and application development
Every organization, of every size and industry, needs an enterprise data architecture to become a more data-driven enterprise. In a digital economy, enterprises of every
Business analysis efforts should start with a conceptual data model based on use case content Successful business analysis requires understanding of business. One of the
Architecture is not what is tangible, that is the result of architecture. Architecture is the set of descriptive representations that are required to create an
When migrating data across platforms, it is critically important to have three full data models to support the effort, with the relevant metadata. Having these
Physical data models should follow the logical data model as closely as possible, while adding the optimizing items to conform to a specific database and
Logical and physical data modeling are essential components of every organization’s enterprise data architecture and should form the foundation of every database design. Standard techniques
Abstraction is a powerful design tactic for creating flexible, robust and scalable data warehouse data models Introduction I remember meandering through the large galleries of
Enterprise data modeling is an essential component of strong enterprise data architecture, with subject, conceptual and enterprise logical models based on business concepts and requirements
A well-modeled data dictionary can provide lasting value and ensure consistency of use throughout an organization for all data elements. Introduction The primary purpose organizations
Challenges to implementing data architecture with packaged applications can be overcome by addressing issues and challenges during design Introduction In a previous article I discussed
Every organization should follow a set of guidelines for implementing packaged applications to ensure architectural harmony and avoid numerous problems. Previously I discussed some issues
Metadata supports the conditions that uses of abstraction in data modeling, ensuring lasting and scalable data models When I write on abstraction, I like to
