Data architect
A data architect is a person responsible for ensuring that the data assets of an organisation are supported by an architecture supporting the organization in achieving its strategic goals. The architecture should cover databases, data integration and the means to get to the data. Usually the data architect achieves his/her goals via setting enterprise data standards.
The definition of an IT architecture used in ANSI/IEEE Std 1471-2000 is: The fundamental organization of a system, embodied in its components, their relationships to each other and the environment, and the principles governing its design and evolution., where the data architect primarily focuses on the aspects related to data.
In TOGAF (the Open Group Architecture Framework) [1], architecture has two meanings depending upon its contextual usage:
- A formal description of a system, or a detailed plan of the system at component level to guide its implementation
- The structure of components, their inter-relationships, and the principles and guidelines governing their design and evolution over time.
Translating this to Data architecture helps defining the role of the Data Architect as the one responsible for developing and maintaining a formal description of the data and data structures - this can include data definitions, data models, data flow diagrams, etc. (in short metadata). Data architecture includes topics such as metadata management, business semantics, data modeling and metadata workflow management.
A Data Architect's job frequently includes the set up a metadata registry and allows domain-specific stakeholders to maintain their own data elements. Data Architects also strictly and meticulously enforce standards and integrity within an Enterprise Data Model. Such rigor is necessary because in large enterprises the data model usually outlives all other IT/IS work products[citation needed]:
- Data and Data Model - 25 years+
- Systems - 10-20 years
- Applications - 3-10 years
- Code/Classes - 1-5 years
A Data Architect may have experience in one or more of the following technologies:
- Data dictionaries
- Data warehousing
- Enterprise application integration
- Metadata registry
- Relational Databases
- Semantics
- Structured Query Language (SQL)
- XML, including schema definitions and transformations.