The transformation of the energy system is creating new challenges for distribution system operators. The rapid expansion of renewable energy sources, the electrification of heating and mobility, and changing consumption patterns are increasing the complexity of future grid planning. At the same time, uncertainty regarding the temporal and spatial development of future loads and generation continues to grow. Reliable forecasts and their integration into digital planning processes are therefore becoming increasingly important for cost-efficient and future-oriented grid development. To address these challenges, Syna pursues a data-driven approach that consolidates forecast data from various internal and external sources and makes it available for long-term grid planning. The forecasts are processed, regionalized, and linked to existing grid structures, creating a consistent data foundation that enables the analysis of future developments across different voltage levels and network areas. A key element of this approach is the automated integration of processed forecast data with existing grid connection points and network models. Based on this foundation, the Intelligent Grid Platform (IGP) from envelio is used to perform comprehensive grid studies for multiple target years. The resulting scenario analyses enable a systematic assessment of potential grid developments and support the identification of future bottlenecks and expansion requirements. This presentation provides an overview of the underlying process—from the collection and preparation of forecast data, through its spatial allocation within the distribution network, to the execution of large-scale grid studies. It highlights how digital platforms can facilitate the efficient integration of forecast data into existing planning processes while enabling a consistent comparison of different future scenarios. The contribution is aimed at distribution system operators, planning engineers, and GIS professionals interested in the digitalization of grid planning processes and the integration of geospatial data, forecasting models, and power system simulations.