Improving Data Warehouse and Business Information Quality
By P. Mouncey. 2001
391 citations recorded by the source corpus.
Subfields: Information Systems, Management Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/5869dfb969746a0ee6fd5431d4551b25ac9184af
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Carlo Batini
- Cinzia Cappiello
- Chiara Francalanci
- Andrea Maurino
- Marco Torchiano
- Raimondo Iemma
- Antonio Vetro
- Lorenzo Canova
In Management Information Systems
- Carlo Batini
- Cinzia Cappiello
- Chiara Francalanci
- Andrea Maurino
- Marco Torchiano
- Raimondo Iemma
- Antonio Vetro
- Lorenzo Canova
In Management Science and Operations Research
- Carlo Batini
- Cinzia Cappiello
- Chiara Francalanci
- Andrea Maurino
- Marco Torchiano
- Raimondo Iemma
- Antonio Vetro
- Lorenzo Canova
Citations in the corpus
- Methodologies for data quality assessment and improvement (2009, methodology): “The costs of poor quality can be classi.ed as follows [English 1999]: (1) process costs, such as the costs associated with the re-execution of the whole process due to data errors; (2) opportunity costs due to lost and missed revenues.”
- Open data quality measurement framework: Definition and application to Open Government Data (2016, methodology): “The metric “derivation integrity” in the TIMQ framework calculates the same thing but in a broader way, it is defined as “Percentage of correct calculations of derived data according to the Integrity derivation formula or calculation definition” (Batini et al., 2009) and (English, 1999).”