Abstract
The requirements specified by the REDm-MED Project for data management plans will be assembled both from a ‘bottom-up’ elicitation process involving researchers and academics, and ‘topdown’ from funder requirements, institutional policy and known good practice. Data management lifecycle models provide a structure for considering the many operations that will need to be performed on a data record throughout its life; as such they can also act as a useful touchstone for ensuring the requirements specification has sufficient coverage of key data management operations
Author supplied keywords
- "data quality
- Big data analytics
- Challenger
- Data ownership
- Data quality
- Data quality management
- Data usage
- Economic analysis
- IT capability
- Incentives
- Information
- Organizational policies
- Resource-based view
- Vincennes
- completeness
- context
- correctness
- data
- data engineering
- data management
- data processing
- data quality
- data quality costs"
- data quality dimensions
- data reliability
- data semantics
- datab
- database design
- design
- information product
- ipmap
- life cycle
- life cycle inventory
- lifecycle
- management
- master data management
- metadata
- ontology
- process management
- programming languages
- quality
- quality control
- requirements analysis
- semantic heterogeneity
- sematic integrity
- ses
- software
- software engineering
- specifications
- uncertainty
- validation
- verification
Cite
CITATION STYLE
Aiken, P., Commonwealth, V., Ballou, D. P., Pazer, H. L., Wang, R. Y., Pazer, H. L., … Wesnaes, M. S. (1998). Review of Data Management Lifecycle Models. Communications of the ACM, 41(2), 4–11. Retrieved from http://portal.acm.org/citation.cfm?id=269022%5Cnhttp://portal.acm.org/citation.cfm?doid=269012.269022
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