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. The authors propose an operating-regime-based data processing framework for industrial process decision making. The framework was designed to increase the quality and take advantage of available process data use to make informed offline strategic business operation decisions, i.e., environmental, cost and energy analysis, optimization, fault detection, debottlenecking, etc. The approach was synthesized from best practices derived from the available framework and improved upon its predecessor by putting forward the combination of process expertise and data-driven approaches. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) signal processing, (3) steady-state operating periods detection, (4) data reconciliation and (5) operating regime detection and identification. The proposed framework is applied to the brownstock washing department of a dissolving pulp mill. Over a 5-month period, the process was found to be in steady-state 32% of the time. Twenty (20) distinct operating regimes were identified. Further processing with the help of data reconciliation techniques, principal component analysis and k-means clustering showed that the main drivers explaining the operating regimes are the pulp level in tanks, its density, and the shower wash water flow rate. Additionally, it was concluded that the top four persistently problematic sensors across the steady-state spans that would need to be verified are three flow meters (06FIC137, 06FIC152, and 06FIC433), and one consistency sensor (06NIC423). This information was relayed to process experts contacts at the plant for further investigation.