Multidimensional enterprise performance characteristics (enterprise operational data, financial transactions records) are stored in the company’s database (warehouse), and their volume and variety are huge. Financial transaction data are directly and indirectly related to value chain processes, various physical objects of activity, and their attributes. There are data mining (DM) and process mining (PM) methods for analyzing enterprise operational data and identifying deficiencies in business process management. There is a need to find new user experience (UX)-driven methods for user interface with the specification of DM and PM tools on the level of business process management concepts. The paper presents the UX design-based approach to designing the user interface (UI) of process mining and data mining systems and is based on a conceptual semantic model named financial data space (FDS). The peculiarity of FDS is that it can include the characteristics of financial data and other UX-related characteristics (events, environmental and internal changes, business location) that may have an impact on changes in the values of financial objects (FO). The presented multidimensional financial data model helps increase the possibility of uncovering management weaknesses by identifying anomalies in large amounts of financial data. The prototypes of components of the financial data analysis system are described and developed using the process mining tool. The presented method of a multidimensional representation of financial data and transformation into a PM project is a user-friendly solution that allows to increase the analytical capabilities of the auditor’s work with large amounts of data, providing a more flexible view of the financial indicators of the company’s activity.

Despite widespread adoption, traditional Agile project management practices often fail to ensure successful delivery of enterprise-scale software projects. One key limitation lies in the absence of a conceptually defined structure for the various types of Agile activities and their interactions. As a result, Agile methodologies typically lack formal indicators for evaluating the semantic content and progress status of project activities. Although widely used tools for Agile project management, such as Atlassian Jira, capture operational data, project status assessment interpretation remains largely subjective—relying on the experience and judgment of managers and team members rather than on a formal knowledge model or well-defined semantic attributes. As Agile project activities continue to grow in complexity, there is a pressing need for a modeling approach that captures their causal structure in order to describe the essential characteristics of the processes and ensure systematic monitoring and evaluation of the project. The complexity of the corresponding model must correlate with the causality of processes to avoid losing essential properties and to reveal the content of causal interactions. To address these gaps, this paper introduces a causal Agile process model that formalizes the internal structure and transformation pathways of Agile activity types. To our knowledge, it is the first framework to integrate a recursive, causally grounded structure into Agile management, enabling both semantic clarity and quantitative evaluation of project complexity and progress. The aim of the article is, first, to describe conceptually different Agile activity types from a causal modeling perspective, its internal structure and information transformations, and, second, to formally define the causal Agile management model and its characteristics. Each Agile activity type (e.g., theme, initiative, epic, user story) is modeled using the management transaction (MT) framework—an internal model of activity that comprises a closed-loop causal relationship among management function (F), process (P), state attribute (A), and control (V) informational flows. Using this framework, the internal structure of Agile activity types is normalized and the different roles of activities in internal MT interactions are defined. An important feature of this model is its recursive structure, formed through a hierarchy of MTs. Additionally, the paper presents classifications of vertical and horizontal causal interactions, uncovering theoretically grounded patterns of information exchange among Agile activities. These classifications support the derivation of quantitative indicators for assessing project complexity and progress at a given point in time, offering insights into activity specification completeness at hierarchical levels and overall project content completeness. Examples of complexity indicator calculations applied to real-world enterprise application system (EAS) projects are included. Finally, the paper describes enhancements to the Jira tool, including a causal Agile management repository and a prototype user interface. An experimental case study involving four Nordic EAS projects (using Scrum at the team level and SAFe at the program level) demonstrates that the Jira tool, when supplemented with causal analysis, can reveal missing links between themes and initiatives and align interdependencies between teams in real time. The causal Agile approach reduced the total number of requirements by an average of 13% and the number of change requests by 14%, indicating a significant improvement in project coordination and quality.

There are different audit methods for analysing operational facts and identifying deficiencies in business process management. However, financial data and transactions indirectly relate to physical objects and their attributes, resulting in distinct characteristics for their analysis and auditing methods. It is important to find new methods for finding a process extraction solution based on the financial audit data space to increase the efficiency of the auditors' work by reducing their time costs and increasing the possibility of detecting financial anomalies in large amounts of financial data. The research presents discussed different financial data space (FDS) methods, and process mining (PM) that include related classifications of financial data and external and internal factors that can influence data behavior (value changes) and the peculiarity of FDS is that it includes not only the characteristics of financial data but also other operational characteristics (events, environmental and internal changes, business location) that may be related to the change in financial objects (FO) values. The authors of the paper discuss the prototypes of specific elements in financial data analysis that were developed using the Process Mining software tool Disco and present the conclusions of the effectiveness of the financial processes cube defined to increase the analytical possibilities of the auditor's work with large amounts of data, introducing more flexible functionality for the analysis of financial activities.

This article presents a causal modeling approach for analyzing the processes of an academic institution. Academic processes consist of activities that are considered self-managed systems and are defined as management transactions (MTs). The purpose of this article is to present a method of causal modeling of organizational processes, which helps to determine the internal model of the current process under consideration, its activities, and the processes’ causal dependencies in the management hierarchy of the institution, as well as horizontal and vertical coordination interactions and their content. Internal models of the identified activities were created, corresponding to the MT framework. In the second step, based on the causal model, a taxonomy of characteristics is presented, which helps to systematize the process quality assessment and ensures the completeness of the characteristics and indicators. Predefined structures of characteristic types are the basis of activity content description templates. Based on the proposed method, two causal models are created: the “to-be” causal model of the target study process (based on expert knowledge) and the “as-is” documented (existing) model of the study process used to evaluate the study process’s quality. The principles and examples of comparing the created “to-be” causal model with the existing study process monitoring method are presented, enabling the detection of the shortcomings in the existing method for assessing academic performance. Causal modeling allows for the rethinking of existing interactions and the identification of necessary interactions to improve the quality of studies. The comparison based on causal modeling allows for a systematic analysis of regulations and the consistent identification of new characteristics (indicators) that evaluate relevant aspects of academic processes and activities.