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.

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.

为了在业务流程中取得成功，组织需要的数据不仅要达到手头任务的适当质量水平，而且还应被认为可用于业务。但是，许多研究人员将数据的潜在可用性基于其质量。在使用数据之前，收到有关数据可用性的建议将使组织受益。我们建议通过决策过程来支持有关数据可用性的建议，该决策过程包括基于业务规则的上下文相关的数据质量评估。理想情况下，此建议将自动生成。决策模型和表示法（DMN）可以基于对业务规则的评估来评估数据质量，并为利益相关者（例如，

该提案的主要贡献涉及设计和启用DMN驱动的机制和指导方法（DMN4DQ），以支持自动生成基于数据质量的数据可用性的基于决策的建议。此外，通过实际数据集的应用来执行提案的验证。