The domain of industrial automation faces challenges such as shortened product life cycles, a shortage of skilled labor, and increasing complexity. Addressing these issues necessitates innovative solutions, one of which is the Digital Twin — a virtual counterpart of a physical asset. Central to the quality of a Digital Twin is the data it harnesses. While current Digital Twins draw data primarily from their corresponding physical assets, future interconnected production environments promise an influx of additional data from external devices. However, it remains uncertain how existing Digital Twins incorporate and leverage such data. In this systematic literature review, drawing from a pool of 1107 unique publications, we deeply analyzed 141 works to shed light on the data utilization in industrial Digital Twins. We categorized these publications based on Digital Twin types and classified them against various criteria regarding different characteristics of data. Our findings reveal that the majority of Digital Twins predominantly rely on structured data sourced directly from their associated assets, often employing proprietary integration methods. Facing the trends toward agile and interconnected production ecosystems as well as an increasing amount of unstructured data, we assert that current Digital Twins are not equipped to meet forthcoming demands in the industrial domain. Consequently, we propose necessary adaptations to fully unleash the potential of Digital Twins and outline future research fields, including automated data integration and evaluation.