Enterprise Data Architecture (EDA)
Definition - What does Enterprise Data Architecture (EDA) mean?
Enterprise data architecture (EDA) refers to a collection of master blueprints designed to align IT programs and information assets with business strategy. EDA is used to guide integration, quality enhancement and successful data delivery.
EDA is part of the overall enterprise architecture, which has several integrated aspects, including hardware, applications, business processes, technology choices, networks and data.
Techopedia explains Enterprise Data Architecture (EDA)
- 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
- High-Level Data Model (HLDM): Constitutes a collection of HLDMs that describe business data through a conceptual viewpoint independent of any present realization by real systems. The HLDM consists of a standard UML class model of the primary data items and their relationships; a superset of business features, such as semantics, universal constraints and syntax.
- Realization overviews: Describes the relationships between the real vital data objects of the present or planned systems and the conceptual units of the HLDM. This shows the way in which conceptual units are realized by actual units.
- Source and consumer models: Demonstrates the correlations between various realizations of the same data items, diverse organizational custodians of data elements and the way in which modifications are circulated around different systems.
- Transportation and transformation models: Explains the way in which data in the implementation systems changes when moved among systems. They include attribute structure and physical class of system interfaces. This model also depicts the realization of the HLDM within the interface mechanisms, including a backbone or an enterprise application integration (EAI) hub.
- Helps gain a better understanding of data
- Is a vital factor for developing and implementing governance that supports a data strategy
- Guides developments across systems, such as common reporting, enterprise application integration (EAI) and data warehousing initiatives.
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