The impact of improved big data collection and analytical tools used to assess and monitor the state of the planet's biodiversity is profound. Yet there is growing concern that the growth of digital approaches in conservation research practice may fail to reflect or support the Indigenous and local knowledge systems that support local stewardship practices. Methods of supporting Indigenous, citizen science, and multiple evidence-based approaches are also growing in scope and impact, introducing opportunities and challenges for equitable and effective conservation science collaborations. However, less attention has been given to what this means for conservation science data practices. Here we present insights from a review of 223 published reports and papers to build on the growing critique of data practices in conservation science and to showcase published insights and experiences that identify the opportunities and challenges of implementing data justice principles in conservation science. We showcase new approaches and areas of research expertise that have been used and required when considering if and how data and data analytics can work with, rather than assimilate, diverse and situated Indigenous and local knowledge governance systems and practices throughout each stage of data planning, collection, management, processing, analysis and translation. Findings from this review also highlight that while there has been progress in highlighting an overarching data justice agenda in conservation literature, there remain significant gaps in understanding if and how data justice is or can be applied in research practice to minimise risk and enhance the likelihood of equitable and effective outcomes for local communities and for conservation.