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Data custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules. [ 1 ] [ 2 ] Simply put, Data Stewards are responsible for what is stored in a data field, while data custodians are responsible for the technical environment and database structure.
A data steward may share some responsibilities with a data custodian, such as the awareness, accessibility, release, appropriate use, security and management of data. [1] A data steward would also participate in the development and implementation of data assets.
Effective EDM usually includes the creation, documentation and enforcement of operating policies and procedures associated with change management, (i.e. data model, business glossary, master data shared domains, data cleansing and normalization), data stewardship, security constraints and dependency rules. In many cases, these policies and ...
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 ...
The Data Owner is responsible for the requirements for data definition, data quality, data security, etc. as well as for compliance with data governance and data management procedures. The Data Owner should also be funding improvement projects in case of deviations from the requirements.
Data sovereignty is the idea that data is subject to the laws and governing structures of the nation where they are collected. In other words, a country is able to control and access the data that is generated in its territories. [1]
Current stewards are confirmed during each election. Stewards are subject to the stewards' policy; further documentation may be found in the steward handbook. The Wikimedia Foundation created the position of steward when it recognized the need to separate user rights management from software development and systems administration.
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.