Valencia Parra, ÁlvaroParody Núñez, María LuisaVarela Vaca, Ángel JesúsCaballero, IsmaelGómez López, María Teresa2020-06-082020-06-082019Valencia Parra, Á., Parody Núñez, M.L., Varela Vaca, Á.J., Caballero, I. y Gómez López, M.T. (2019). DMN for Data Quality Measurement and Assessment. En BPM 2019: International Conference on Business Process Management (362-374), Vienna, Austria: Springer.978-3-030-37452-5https://hdl.handle.net/11441/97520Data Quality assessment is aimed at evaluating the suitability of a dataset for an intended task. The extensive literature on data quality describes the various methodologies for assessing data quality by means of data profiling techniques of the whole datasets. Our investigations are aimed to provide solutions to the need of automatically assessing the level of quality of the records of a dataset, where data profiling tools do not provide an adequate level of information. As most of the times, it is easier to describe when a record has quality enough than calculating a qualitative indicator, we propose a semi-automatically business rule-guided data quality assessment methodology for every record. This involves first listing the business rules that describe the data (data requirements), then those describing how to produce measures (business rules for data quality measurements), and finally, those defining how to assess the level of data quality of a data set (business rules for data quality assessment). The main contribution of this paper is the adoption of the OMG standard DMN (Decision Model and Notation) to support the data quality requirement description and their automatic assessment by using the existing DMN engines.application/pdf13engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Data qualityDecision Model and NotationData quality measurementData quality assessmentinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1007/978-3-030-37453-2_30