Much biodiversity data is collected worldwide, but it remains a major challenge to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to harmonize and standardize biodiversity data from disparate sources for capturing the minimum set of critical variables required to study, report and management of biodiversity change. Here, we assess the challenges for building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g., repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source datasets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify eleven key workflow steps that will lead to an operationalization of building EBV data products within and across research infrastructures. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing monitoring projects and citizen science efforts, including eBird, the Tropical Ecology Assessment and Monitoring, the Living Planet Index, and the Baltic Sea zooplankton monitoring. This demonstrates that the identified workflow steps are applicable to both the terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. All identified workflow steps depend on clear and accessible metadata, and we provide an overview of current standards and identify existing gaps and opportunities. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source datasets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating new methods and technologies for data collection such as sensor networks, DNA-based techniques, and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows, and the production process/cycle as well as approaching technical interoperability among research infrastructures, (v) achieving semantic interoperability by adopting standards and tools for capturing consistent metadata, and (vi) ensuring legal interoperability by endorsing open data or data that are free from restrictions on access, use modification, and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.