The article examines the methodological challenge that arises when organizations transition from “management by reports” to management based on digital traces of end-to-end (cross-functional and intersystem) processes. It demonstrates that the abundance of event data (ERP/CRM/Service Desk logs, integration and user logs, equipment transactions etc.) does not in itself guarantee its suitability for management decisions: systematic measurement flaws – incomplete logging, discontinuities in the end-to-end identity of a case, inconsistency in status semantics, low temporal resolution, and definition drift after releases – can lead not just to “noise” but to persistently biased management conclusions. Particular emphasis is placed on the fact that event data quality requirements differ for two modes of use: descriptive-control (process reconstruction, KPIs, deviation monitoring, process mining) and causal-explanatory (assessing the effect of management interventions based on observational data). The methodological framework of the study is constructed as a synthesis of process mining, data quality management standards and practices, and causal inference theory (potential outcomes, causal graphs, quasi-experimental designs). A two-loop model of event data quality is introduced: “measurement consistency” (how accurately the data reflect the trajectory of a case over time) and “causal suitability” (the extent to which the data allow for the unambiguous determination of an intervention, outcome, time windows, and the validity of identification assumptions). An operationalization of causal suitability is proposed as a set of indices (observation unit integrity, temporal suitability, meaning stability, intervention observability, context sufficiency for confounding control, and resource environment observability) linking event log defects to the risk of false effect estimates. The need for accounting metadata (status and rule versioning, time stamping, and change traceability) is further substantiated, as is the need for process-semantic constraints (process axioms) as a quality control mechanism critical specifically for causal inference. The practical result is a framework for organizational event data quality management (event log passport, intervention registry, meaning versioning, and a quality index panel), enabling the proactive determination of which management decisions and causal assessments are, in principle, permissible based on existing digital traces.