Papers - Strong et al. - Data quality in context
11 important questions on Papers - Strong et al. - Data quality in context
What is, according to the paper, the main issue that Strong et al try to resolve?
What do Strong et al describe 'High Quality' data as?
What are, based on the definition of High Quality data, the four categories of HQD?
- Intrinsic
- Accessibility
- Contextual
- Representational
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When can we speak of a DQ problem, according to Strong et al?
What are the DQ dimensions of the Intrinsic DQ category?
What are the DQ dimensions of the Contextual DQ category?
What are the DQ dimensions of the Representational DQ category?
What are the two main causes of intrinsic DQ problems?
- Mismatches among sources of the same data
- Judgment or subjectivity in the data production process (coded data is considered as lower quality than raw data)
Explain how data custodians can become part of the DQ problem pattern of accessiblity through one of the key dimensions of this category.
Representational DQ dimensions can be the underlying causes of accessibility DQ problem patterns, explain.
Strong et al. Identify three roles in data manufacturing systems (production and storage of data), with the central actor being the data production process. What is this process and what are the three roles in the data manufacturing system?
Three roles are:
- Data Producers (groups, people, other sources that generate data)
- Data Custodians (people who provide and manage computing resources for storing and processing data)
- Data Consumers (people or groups who use data)
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