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A data architecture aims to set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration , for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems.
According to Forrester Research, solution architecture is one of the key components by which Enterprise Architecture delivers value to the organization. It entails artifacts such as a solution business context, a solution vision and requirements, solution options (e.g. through RFIs, RFPs or prototype development) and an agreed optimal solution with build and implementation plans ("road-map").
A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying and managing an organization's data architecture. Data architects define how the data will be stored, consumed, integrated and managed by different data entities and IT systems, as well as any applications using or ...
Modern software development processes are not restricted to the discrete ALM/SDLC steps managed by different teams using multiple tools from different locations. [citation needed] Real-time collaboration, access to the centralized data repository, cross-tool and cross-project visibility, better project monitoring and reporting are the key to developing quality software in less time.
Enterprise architecture regards the enterprise as a large and complex system or system of systems. [3] To manage the scale and complexity of this system, an architectural framework provides tools and approaches that help architects abstract from the level of detail at which builders work, to bring enterprise design tasks into focus and produce valuable architecture description documentation.
Applications architecture strategy involves ensuring the applications and the integration align with the growth strategy of the organization. If an organization is a manufacturing organization with fast growth plans through acquisitions, the applications architecture should be nimble enough to encompass inherited legacy systems as well as other large competing systems.
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Around the 1970s/1980s the term information engineering methodology (IEM) was created to describe database design and the use of software for data analysis and processing. [3] [4] These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of the operational processing needs of organizations for the 1980s.