Data, Design, and Computation: A New Design ParadigmBriefing Summary from NJ Studio (NJ Namju Lee) NJ Namju Lee emphasizes the central role of data in design, particularly in computational design. He argues for a shift from seeing data as separate input toward integrating it as a fundamental component of design thinking and practice. His lectures outline three interconnected pillars: Data – Data exists everywhere, in daily life and design. Anything measurable, recognizable, or computable (from geometry to emotions) can be considered data. Design data extends across scales (product, building, city, landscape) and domains (environmental, social, material, fabrication, energy, image, interaction, parameters).Methodology (Data Structures & Algorithms) – Spatial information in design requires structured ways of processing: graphs, matrices, tensors. Algorithms act as “recipes” to transform data within these structures. The combination of data + structure + algorithm forms the foundation of computational design.System (Computational Pipeline) – The design process itself can be reframed as a computational pipeline, allowing systematic exploration, iteration, simulation, and optimization. Designers can “package” their intuition and expertise into algorithms or programs, formalizing design knowledge into computational frameworks.Key Ideas & Applications Domain knowledge matters: Context (urban, landscape, architectural) shapes how data is collected, modeled, and interpreted. Data-driven design enables site analysis, performance simulation, and evidence-based evaluation. Optimization is a core application: finding the best solution under defined goals and constraints. Generative design uses rule-based or agent-based systems to explore multiple options and emergent possibilities. Visualization is essential for interpreting and communicating data-driven insights. Creativity from computation: Machine “errors” or unexpected outputs can inspire novel design directions. Mindset shift: Computational design is not just about coding but about reframing one’s own design process in computational terms. It requires openness, interdisciplinarity, and collaboration beyond traditional design boundaries. Takeaways for Designers Treat data as integral to every stage of design. Develop fluency in data structures, algorithms, and visualization. Translate design processes into computational pipelines. Leverage domain expertise to connect data with meaningful outcomes. Use data for simulation, optimization, and generative exploration. Balance precision with creativity by embracing computation as both a tool and a partner in design. NJ Lee presents computational design as both a methodology and a paradigm shift—a way to expand the boundaries of traditional practice. Through urban analysis, material modeling, structural exploration, or environmental simulation, data becomes not only evidence but also a driver of creativity and innovation.
