Data quality management is a great challenge in today’s world due to increasing proliferation of abundant and heterogeneous datasets. All organizations that realize and maintain data intensive advanced applications should deal with data quality related problems on a daily basis. In these organization data quality related problems are registered in natural languages and subsequently the organizations rely on ad-hoc, non-systematic, and expensive solutions to categorize and resolve registered problems. In this contribution we present a formal description of an innovative data quality resolving architecture to semantically and dynamically map the descriptions of data quality related problems to data quality attributes. Through this mapping, we reduce complexity – as the dimensionality of data quality attributes is far smaller than that of the natural language space – and enable data analysts to directly use the methods and tools proposed in literature. Another challenge in data quality management is to choose appropriate solutions for addressing data quality problems due to lack of insight in the long-term or broader effects of candidate solutions. This difficulty becomes particularly prominent in flexible architectures where loosely linked data are integrated (e.g., data spaces or in open data settings). We present also a decision support framework for the solution choosing process to evaluate cost-benefit values of candidate solutions. The paper reports on a proof of concept tool of the proposed architecture and its evaluation.