The Transformation of Decision-Making Leadership in Data-Driven Organizations: An Interpretive Study
Keywords:
Data-driven decision-making, decision leadership, digital transformation, analytics capability, interpretive study, organizational leadership, TehranAbstract
This study aimed to interpret how leadership decision-making is transformed when organizations shift from experience-based and authority-centered decision practices toward data-driven, analytics-supported, and evidence-mediated forms of managerial judgment. This qualitative interpretive study was conducted among senior managers, middle managers, data analysts, digital transformation specialists, and organizational development experts working in data-driven organizations in Tehran. Participants were selected through purposive sampling based on their direct involvement in strategic, operational, or analytical decision-making processes. Data were collected through semi-structured interviews with 21 participants, and theoretical saturation was reached after the eighteenth interview, with three additional interviews conducted to confirm conceptual adequacy. Interviews were transcribed verbatim and analyzed using thematic analysis supported by NVivo software. Coding proceeded through open coding, category development, theme refinement, and interpretive integration. The analysis produced five main categories: evidence-mediated leadership judgment, hybrid human–analytics decision architecture, redistribution of decision authority, data culture as a leadership practice, and ethical reflexivity in data-driven decisions. Participants described the transformation of leadership not as the replacement of managerial judgment by data, but as the reconstruction of judgment through evidence, dashboards, predictive indicators, and cross-functional interpretation. Data-driven decision-making changed who participates in decisions, how authority is justified, how risk is discussed, and how leaders balance speed, accountability, and contextual understanding. The findings suggest that decision-making leadership in data-driven organizations evolves from individual authority toward interpretive orchestration, where leaders are expected to translate data into meaning, question algorithmic outputs, create collective analytical capacity, and preserve ethical responsibility. Effective data-driven leadership therefore depends not only on technological infrastructure, but also on cultural readiness, interpretive competence, transparency, and human accountability.
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