| Title |
DMN4DQ: When data quality meets DMN |
| Authors |
Valencia-Parra, Alvaro , PARODY NÚÑEZ, MARÍA LUISA, Jesus Varela-Vaca, Angel , Caballero, Ismael , Teresa Gomez-Lopez, Maria |
| External publication |
No |
| Means |
Decis. Support Syst. |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
1 |
| SJR Quartile |
1 |
| JCR Impact |
6.96900 |
| SJR Impact |
1.97300 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096616438&doi=10.1016%2fj.dss.2020.113450&partnerID=40&md5=26373dbf83475170b1fc360401f425b1 |
| Publication date |
01/02/2021 |
| ISI |
000609029700010 |
| Scopus Id |
2-s2.0-85096616438 |
| DOI |
10.1016/j.dss.2020.113450 |
| Abstract |
To succeed in their business processes, organizations need data that not only attains suitable levels of quality for the task at hand, but that can also be considered as usable for the business. However, many researchers ground the potential usability of the data on its quality. Organizations would benefit from receiving recommendations on the usability of the data before its use. We propose that the recommendation on the usability of the data be supported by a decision process, which includes a context-dependent data-quality assessment based on business rules. Ideally, this recommendation would be generated automatically. Decision Model and Notation (DMN) enables the assessment of data quality based on the evaluation of business rules, and also, provides stakeholders (e.g., data stewards) with sound support for the automation of the whole process of generation of a recommendation regarding usability based on data quality.\n The main contribution of the proposal involves designing and enabling both DMN-driven mechanisms and a guiding methodology (DMN4DQ) to support the automatic generation of a decision-based recommendation on the potential usability of a data record in terms of its level of data quality. Furthermore, the validation of the proposal is performed through the application of a real dataset. |
| Keywords |
Data usability; Data quality; Decision model and notation; Data quality rule; Data quality assessment; Data quality measurement |
| Universidad Loyola members |
|
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