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A data steward is a role that ensures that data governance processes are followed and that guidelines are enforced, and recommends improvements to data governance processes. Data governance involves the coordination of people, processes, and information technology necessary to ensure consistent and proper management of an organization's data ...
It has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing master data throughout an organization to ensure a common understanding, consistency, accuracy and control, [4] in the ongoing maintenance and application use of that data. Processes commonly seen in ...
However, data has to be of high quality to be used as a business asset for creating a competitive advantage. Therefore, data governance is a critical element of data collection and analysis since it determines the quality of data while integrity constraints guarantee the reliability of information collected from data sources.
Data stewards have a specialist role that utilizes an organization's data governance processes, policies, guidelines and responsibilities for administering an organizations' entire data in compliance with policy and/or regulatory obligations.
Information governance, or IG, is the overall strategy for information at an organization. Information governance balances the risk that information presents with the value that information provides. Information governance helps with legal compliance, operational transparency, and reducing expenditures associated with legal discovery. An ...
Managing stakeholder requirements is a critical, and ongoing, process based in an understanding of workflow, data dependencies and the tolerance of the organization for operational disruption. Many organizations use formal processes such as service level agreements to specify requirements and establish EDM program objectives.
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.
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. [1] The purpose of these statistics may be to: Find out whether existing data can be easily used for other purposes