Data Architecture in enterprise architecture is the design of data for use in defining the target state and the subsequent planning needed to achieve the target state. It is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture.[1]
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A data architecture describes the architecture of the data structures used by a business and/or its applications. There are descriptions of data in storage and data in motion; descriptions of data stores, data groups and data items; and mappings of those data artifacts to data qualities, applications, locations etc.
Essential to realizing the target state, Data Architecture describes how data is processed, stored, and utilized in a given system. It provides criteria for data processing operations that make it possible to design data flows and also control the flow of data in the system.
The Data Architect is typically responsible for defining the target state, aligning during development and then following up to ensure enhancements are done in the spirit of the original blueprint.
During the definition of the target state, the Data Architecture breaks a subject down to the atomic level and then builds it back up to the desired form. The Data Architect breaks the subject down by going through 3 traditional architectural processes:
The "data" column of the Zachman Framework for enterprise architecture –
| Layer | View | Data (What) | Stakeholder | |
| 1 | Scope/Contextual | List of things important to the business (subject areas) | Planner | |
| 2 | Business Model/Conceptual | Semantic model or Conceptual/Enterprise Data Model | Owner | |
| 3 | System Model/Logical | Enterprise/Logical Data Model | Designer | |
| 4 | Technology Model/Physical | Physical Data Model | Builder | |
| 5 | Detailed Representations/ out-of-context | Data Definition | Subcontractor |
In this second, broader sense, data architecture includes a complete analysis of the relationships between an organization's functions, available technologies, and data types.
Data architecture should be defined in the planning phase of the design of a new data processing and storage system. The major types and sources of data necessary to support an enterprise should be identified in a manner that is complete, consistent, and understandable. The primary requirement at this stage is to define all of the relevant data entities, not to specify computer hardware items. A data entity is any real or abstracted thing about which an organization or individual wishes to store data.
Physical data architecture of an information system is part of a technology plan. As its name implies, the technology plan is focused on the actual tangible elements to be used in the implementation of the data architecture design. Physical data architecture encompasses database architecture. Database architecture is a schema of the actual database technology that will support the designed data architecture.
There are certain elements that must be defined as the data architecture schema of an organization is designed. For example, the administrative structure that will be established in order to manage the data resources must be described. Also, the methodologies that will be employed to store the data must be defined. In addition, a description of the database technology to be employed must be generated, as well as a description of the processes that will manipulate the data. It is also important to design interfaces to the data by other systems, as well as a design for the infrastructure that will support common data operations (i.e. emergency procedures, data imports, data backups, external transfers of data).
Without the guidance of a properly implemented data architecture design, common data operations might be implemented in different ways, rendering it difficult to understand and control the flow of data within such systems. This sort of fragmentation is highly undesirable due to the potential increased cost, and the data disconnects involved. These sorts of difficulties may be encountered with rapidly growing enterprises and also enterprises that service different lines of business (e.g. insurance products).
Properly executed, the data architecture phase of information system planning forces an organization to specify and delineate both internal and external information flows. These are patterns that the organization may not have previously taken the time to conceptualize. It is therefore possible at this stage to identify costly information shortfalls, disconnects between departments, and disconnects between organizational systems that may not have been evident before the data architecture analysis.[2]
Various constraints and influences will have an effect on data architecture design. These include enterprise requirements, technology drivers, economics, business policies and data processing needs.
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