Symbolic Data Analysis is an extension of Classical Data Analysis to more complex data types and tables through the application of certain conditions, where underlying concepts are vital for their further processing. Therefore, the assessment of the quality of Symbolic Data depends extensively on the quality of the collected classical data. However, even though various criteria and indicators have been established to assess quality in classsical statistics, the specificities of Symbolic Data construction challenge the efficacy of the classical quality assessment components. In this paper we initially refer to the quality dimensions that can be considered for the classical data and then emphasize on the extent that these can be applied to symbolic data, taking into account the peculiarities of symbolic approach.