Enterprise Data Architecture

Definition - What does Enterprise Data Architecture mean?

Enterprise data architecture refers to a collection of master blueprints designed to align IT programs and information assets with business strategy, guiding integration, quality enhancement and successful data delivery. Enterprise data architecture belongs to the overall enterprise architecture. The information systems architecture of an enterprise has several interrelated aspects, such as hardware, applications, business processes, technology choices, networks and data. The enterprise data architecture is a model with different layers, which provide a strong foundation for strategic endeavors, such as: A data strategy that outlines the objectives of business for the improvement of data collection and data use Improvements in the business process Decisions on the potential future of new and modified solutions Data warehousing, integration and reporting initiatives

Techopedia explains Enterprise Data Architecture

Enterprise data architecture is explained mainly by models at four different levels: High-Level Data Models The highest level constitutes a collection of high-level data models that describe business data through a conceptual viewpoint, which is independent of any present realization by real systems. The high-level data model (HLDM) consists of: A standard UML class model of the primary data items (such as the business entities) and the relationships between them A superset of business features, such as meaning descriptions (semantics), universal constraints and standardized formatting (syntax)Realization Overviews The second level describes the relationships between the real vital data objects of the present or planned systems and the conceptual units of the high-level data model. This shows the way in which conceptual units are realized by actual ones. Source and Consumer Models The third layer demonstrates the correlations between various realizations of the same data item, diverse data elements' organizational custodians and the way in which modifications are circulated around different systems. Transportation and Transformation Models The model at the last layer explains the way in which data in the implementation systems are changed when moved among systems. They include: Attribute structure and physical class of system interfaces. This model also depicts the realization of the high level data model within the interface mechanisms, including a backbone or an enterprise application integration (EAI) hub. Realization correlation among the several physical data structures Transformation rules at the attribute level, which are documented using the Object Constraint Language (OCL).Interface driver, timing rules, and constraint modeled using sequence diagrams or interaction. The advantages of using enterprise data architecture are as follows: Helps to get a better understanding of data as it is frequently used by the business, and is a vital factor for developing and implementing governance that supports a data strategy. Guides developments across the system, such as common reporting, EAI, and data warehousing initiatives.
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