Data architecture: Difference between revisions

Revision as of 14:50, 27 December 2021

Revision as of 20:39, 1 January 2022

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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.

; Enterprise requirements: These generally include such elements as economical and effective system expansion, acceptable performance levels (especially system access speed), [[Financial transaction|transaction]] reliability, and transparent [[data management]]. In addition, the [[Data conversion|conversion]] of raw data such as transaction [[Record (computer science)|records]] and [[image]] [[Computer file|files]]s into more useful [[information]] forms through such features as [[data warehouse]]s is also a common organizational [[requirement]], since this enables managerial decision making and other organizational processes. One of the architecture techniques is the split between managing [[transaction data]] and (master) [[reference data]]. Another is splitting [[Automatic identification and data capture|data capture systems]] from data retrieval systems (as done in a data warehouse).

; Technology drivers: These are usually suggested by the completed data architecture and database architecture designs. In addition, some technology drivers will derive from existing organizational integration frameworks and standards, organizational economics, and existing site resources (e.g. previously purchased [[software licensing]]). In many cases, the integration of multiple legacy systems requires the use of [[data virtualization]] technologies.