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Difference between revisions of "Data Asset Framework (DAF)"

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== What is Data Asset Framework (DAF)? ==
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'''The Data Asset Framework (DAF)''' provides a toolkit for organizations to identify their digital assets and assess how they are managed. Previously known as the Data Audit Framework, this tool guides the user through a DAF assessment. It is primarily useful for institutions, departments, or research groups starting to think about data management, and who need to prepare a register of their data assets.<ref>[https://www.dataone.org/software-tools/data-asset-framework-daf Defining Data Asset Framework (DAF)]</ref>
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== Data Asset Framework (DAF) Methodology<ref>[http://www.dcc.ac.uk/resources/tools/data-asset-framework Data Asset Framework (DAF) Methodology]</ref> ==
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DAF recommends a four-stage process:
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*Stage 1 is for planning, defining the purpose and scope of the survey and conducting preliminary research.
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*Stage 2 is about identifying what data assets exist and classifying them to determine where to focus efforts for more in-depth analysis.
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*Stage 3 is where the information life cycle is considered to understand researchers’ workflows and identify weaknesses in data creation and curation practices.
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*Stage 4 pulls together the information collected and provides recommendations for improving data management.
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== See Also ==
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*[[Data]]
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*[[IT Governance]]
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== References ==
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<references/>
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Latest revision as of 16:23, 14 March 2023

What is Data Asset Framework (DAF)?

The Data Asset Framework (DAF) provides a toolkit for organizations to identify their digital assets and assess how they are managed. Previously known as the Data Audit Framework, this tool guides the user through a DAF assessment. It is primarily useful for institutions, departments, or research groups starting to think about data management, and who need to prepare a register of their data assets.[1]


Data Asset Framework (DAF) Methodology[2]

DAF recommends a four-stage process:

  • Stage 1 is for planning, defining the purpose and scope of the survey and conducting preliminary research.
  • Stage 2 is about identifying what data assets exist and classifying them to determine where to focus efforts for more in-depth analysis.
  • Stage 3 is where the information life cycle is considered to understand researchers’ workflows and identify weaknesses in data creation and curation practices.
  • Stage 4 pulls together the information collected and provides recommendations for improving data management.


See Also


References