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Properly executed, the data architecture phase of information system planning forces an organization to precisely specify and describe 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.<ref>{{cite book|last=Mittal|first=Prashant|title=Author|year=2009|publisher=Global India Publications|___location=pg 256|isbn=978-93-8022-820-4|pages=314|url=https://books.google.com/books?id=BpkhYDj4tm0C&dq=inauthor:%22PRASHANT+MITTAL%22&source=gbs_navlinks_s}}</ref>
Data Architecture is a foundation Discipline in implementing a Data Management Framework. Data Architects are key in the implementation of an integrated Data Management Framework. '''A Data Management Framework''' (DMF) is a system of thinking, terminology, documentation, resources and insights which allows users to view data related concepts and information in their own context, and in the broader context of the framework, thereby enabling them to integrate their conversations and work. There are a number of DMFs available. A Framework which maps, Data Management against the Business, Enterprise Architecture, Environments, Time, Cost, Effort and has an order of implementation that includes the delivery of Enterprise Information Management can be seen in the image to the right [[File:MDDMF - The Multi Dimensional (V4.0) Data Management Framework (A3 Electronic) 20181020.pdf|thumb|Integrated Data and Information Management Framework. mapping to Business, IT and Enterprise Architecture, Useful for the C-Suite, Business Management, Data Governance, Stewards, Security, Quality, in total 22 Data Disciplines, including A.I.]]
== Constraints and influences ==
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