“According to the Data Management Body of Knowledge (DMBOK), Data Architecture ‘includes specifications used to describe existing state, define data requirements, guide data integration, and control data assets as put forth in a data strategy.’ Data Architecture bridges business strategy and technical execution”
Data Architecture is as much a business decision as it is a technical one, as new business models and entirely new ways of working are driven by data and information.
Data Architecture can be synthesized into the following components:
• Data Architecture Outcomes: Models, definitions, and data flows on various levels, usually referred as Data Architecture artifacts.
• Data Architecture Activities: Forms, deploys, and fulfills Data Architecture intentions.
• Data Architecture Behaviors: Collaborations, mindsets, and skills among the various roles that affect the enterprise’s Data Architecture.”
Realizing the target state
If data is the most important asset of an organization, then the understanding and planning of data assets is crucial to the future success of an organization. Essential to realizing the future shape of the data, Data Architecture is the process of defining how data is stored, used, and processed. The Data Architect is responsible for defining the target state, alignment during development to the target state and then minor follow up to ensure enhancements are done in the spirit of the original design. 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 architectural processes:
ConceptualRepresents all business entities.
LogicalRepresents the logic of how entities are related.
PhysicalThe actual data stored on the storage media, the realization of the data for a specific function (storage, history, integration, updates, search, reporting, analytics, etc).