Modern conservation practices rely more and more on science-based information, which consequently demands data management schemes that are able to serve this purpose. South Africa has committed to achieving Aichi Target 19; which is to increase knowledge, the science base and technologies relating to biodiversity, including the application and sharing of these by 2020. Although significant strides have been made, the status quo for conservation data management leaves much to be desired. This status quo is, for instance, characterised by a lack of human and technology resources, non-standardisation, poor taxonomic integrity and data quality control. Examples of these at are: spreadsheets populated at reserve level (only added to databases after several steps), unverifiable species identifications by field staff, lacking taxon expert quality control, outdated protocols that do not adapt to changing needs, poor integration and consolidation across multiple databases, data types and scale, and data sharing agreements that are not optimally utilised. This influences whether data can easily be shared or not, and whether biodiversity information is fit for use in decision-making. Consequently, we experience a lack of timely and accurate reporting, legal risks, cumbersome data flow requiring unnecessary capacity and resources, and fragmented management information (at the site, taxon and ecosystem levels). Improved implementation of data management, therefore, requires modernisation, integration (internal and external), and skill enhancement and transfer. The utilisation of advancing data science technologies will afford us the ability to analyse and apply techniques to larger, consolidated datasets, ultimately providing valuable information for conservation in novel ways.