It is intensively discussed in both science and practice how data quality (DQ) can be assured and improved. The growing relevance of DQ has revealed the need for adequate metrics because quantifying DQ is essential for planning quality measures in an economic manner. This paper analyses how DQ can be quantified with respect to the DQ dimension timeliness. Based on an existing approach, we design a new metric to quantify timeliness in a well-founded manner that considers so-called supplemental data (supplemental data are additional data attributes that allow drawing conclusions about the timeliness of the data attribute considered). In addition, it is possible to take the values of the metric into account when calculating expected values, an advantage that in turn leads to improved and comprehensible decision support. We evaluate the presented metric briefly with regard to requirements for designing DQ metrics from literature. Then, we illustrate the metric’s applicability as well as its practical benefit. In cooperation with a financial services provider, the metric was applied in the field of customer valuation in order to support the measurement of customer lifetime values.