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Data governance at the macro level involves regulating cross-border data flows among countries, which is more precisely termed international data governance.This field formed in the early 2000s [1] and consists of "norms, principles and rules governing various types of data."
A brief description of the 14 basic principles is given below. [4] Principle 1 Governance – A bank’s risk data aggregation capabilities and risk reporting practices should be subject to strong governance arrangements consistent with other principles and guidance established by the Basel Committee.
The Principles for a Data Economy – Data Rights and Transactions is a transatlantic legal project carried out jointly by the American Law Institute (ALI) and the European Law Institute (ELI). [1] The Principles for a Data Economy deals with a range of different legal questions that arise in the data economy. [2]
The principles identify the critical hallmarks of information governance, which Gartner describes as an accountability framework that "includes the processes, roles, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals."
In 1980, the OECD issued recommendations for protection of personal data in the form of eight principles. These were non-binding and in 1995, the European Union (EU) enacted a more binding form of governance, i.e. legislation, to protect personal data privacy in the form of the Data Protection Directive.
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 ...
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.
The FAIR principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in the volume, complexity, and rate of production of data.