1001 making it essential to document why decisions were taken, under which assumptions, and based on which datasets. The absence of structured mechanisms to capture and retain this information can undermine due diligence, complicate regulatory engagement, and weaken organizational learning (Morgenstern et al., 2016). These technical and governance challenges demonstrate that the successful adoption of circular economy strategies in tailings management depends not only on innovation, but on robust engineering practice, disciplined risk management, and integrated governance frameworks. Addressing these challenges requires tools and systems capable of supporting multidisciplinary data integration, traceable decision‑making, and lifecycle‑based risk evaluation. 4. DIGITALIZATION AND INTEGRATED DATA SUPPORTING RISK-INFORMED CIRCULAR DECISION-MAKING The growing technical and governance complexity associated with circular strategies in tailings management has highlighted the limitations of traditional, document‑centered and fragmented data practices. Circular economy interventions require decisions that integrate geotechnical, operational, environmental, and governance considerations across the full lifecycle of tailings facilities. In this context, digitalization and integrated data systems have been increasingly discussed in the literature as enabling structures that support consistency, traceability, and risk‑informed decision‑making, rather than as solutions in themselves (Morgenstern et al., 2016; Franks et al., 2021). A structured and integrated data environment allows heterogeneous information generated over time, such as site investigations, monitoring records, design documentation, operational changes, risk assessments, and regulatory commitments, to be consolidated within a coherent framework. When appropriately organized, such environments facilitate the transition from fragmented information handling toward a knowledge‑based management approach, in which data are contextualized and made accessible to support engineering evaluation and governance processes (ICMM, 2020; Kinnunen et al., 2022). Within the context of circular economy strategies, this integration is particularly relevant for risk‑informed evaluation of alternatives. Decisions related to tailings reprocessing, material reuse, or changes in disposal technologies require the simultaneous consideration of material properties, deposition history, hydrogeological conditions, stability mechanisms, and regulatory constraints. The literature consistently emphasizes that the absence of integrated data structures increases the likelihood of incomplete assessments and inconsistent decisions, particularly when modifications are introduced incrementally over time (Vick, 1990; Morgenstern et al., 2016). Recent developments in digital information management have focused less on automation and more on structure, continuity, and decision traceability. Advancements such as standardized data models, version control, change‑management records, and lifecycle‑based documentation support transparency and accountability, which are fundamental to both circular economy initiatives and tailings governance frameworks (Franks et al., 2021). These characteristics are especially important where tailings strategies evolve through multiple design, operational, and closure phases. The growing application of data analytics and artificial intelligence (AI) techniques in mining and infrastructure management has further contributed to improving efficiency in handling complex and large datasets. Academic studies describe the use of AI‑based methods to support data validation, trend recognition in monitoring systems, classification of technical documentation, and synthesis of information for reporting purposes (Bugayev et al., 2022; Kinnunen et al., 2022). These applications are primarily associated with efficiency gains in data organization and documentation workflows, reducing the time required for information consolidation while improving consistency and data quality. Importantly, the literature also cautions that digitalization does not replace engineering judgment, nor does it define acceptable risk thresholds or decision outcomes. Integrated systems and analytical methods function as facilitators, providing a structured and auditable information base that enables competent
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