Data management systems are increasingly used in industrial processes. However, data collected as part of industrial process operations, such as sensor or measurement instruments data, contain various sources of errors that can hamper process analysis and decision-making. Therefore, in order to take full advantage of collected and stored data and to increase data quality, an operating regime-based data-processing framework for industrial process decision-making is proposed in this paper. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) data management and (3) operating regime detection and identification. All steps are based on the combination of process knowledge and data-driven approaches. The proposed framework is applied to data from a brownstock washing department of a dissolving pulp mill, and employed in a case study presented in Part II of this publication, where, considering an activity-based costing analysis, the optimal way to operate the department is identified.