Core Viewpoint - The article challenges the conventional belief in "data-driven" decision-making, suggesting that true insights come from well-formed hypotheses rather than merely analyzing data [6][7][12]. Group 1: Problems with Data-Driven Approaches - Companies often rely on data that leads to the same conclusions, resulting in a disconnect with actual user needs [2][4]. - Despite extensive user research investments, companies frequently guess what users want, indicating a failure in truly understanding user behavior [4][5]. - The article introduces the concept of "fire turkey scientists," illustrating how reliance on past data can lead to erroneous conclusions, similar to a turkey expecting to be fed based on previous experiences [15][16][17]. Group 2: The Limitations of Data Analysis - The article emphasizes that data analysis often focuses on "components" rather than the "truth" of user experiences, using the "orange juice theory" as a metaphor [20][22]. - Team A, which analyzes the components of orange juice, may fail to recreate the authentic experience that Team B aims for, highlighting the difference between data and genuine understanding [22][24]. - Understanding user behavior requires going beyond what happened (data) to why it happened (insight), which is crucial for effective business decisions [26][27]. Group 3: Transitioning to New Problem-Solving Paradigms - The article introduces the concept of "Wicked Problems," which are complex and lack straightforward solutions, contrasting with "Tame Problems" that can be easily solved [28][30]. - Traditional data-driven methods fail to address these complex problems, necessitating new approaches [32]. - The article proposes "simulation" as a new method for understanding user behavior, exemplified by the Atypica experiment, which aims to create realistic user simulations rather than relying on past data [33][35]. Group 4: Atypica and AI Simulation - Atypica seeks to simulate real users to provide insights into future behaviors, moving away from merely analyzing historical data [33][34]. - The potential of AI simulation is highlighted, suggesting it could significantly enhance the speed and cost-effectiveness of understanding user needs [36]. - The article invites readers to explore how to establish a "hypothesis-driven" decision-making process instead of being overly reliant on data [39].