Incompleteness and inconsistent situations are common in most rule-based decision support systems (DSS). However, most rule inference methods do not provide procedures to specifically tackle and/or analyze them. This research presents a single approach for both incompleteness and inconsistency issues with a simple yet effective method. During the rule activation step, data incompleteness and inconsistency may be seen as paired situations, since the former appears due to lack of information while the latter can be represented as an excess of heterogeneous information activated. To effectively take advantage of this fact, this research presents a Dynamic Rule Activation (DRA) method, which searches for a balance between both incomplete and inconsistent situations to improve the overall performance of the DSS. Although DRA is designed as a flexible method, able to work with most similarity measures, in this research it is applied in the context of Extended Belief Rule-Bases (E-BRBs). The case studies illustrated in this research demonstrate how the use of DRA can improve the accuracy of E-BRB based decision support models. In this regard, the RIMER+ model and the simple weighted average of the activated rules were tested with and without using DRA as pre-processing method.