Business Intelligence (BI) enables an organization to collect and analyse internal and external business data to generate knowledge and business value, and provide decision support at the strategic, tactical, and operational levels. Typically, enterprises rely on complex Information Technology (IT) systems that manage all data coming from operational databases running within the organization and provide fixed user interfaces through which knowledge workers can access information for analysis and assessment. The consolidation of data coming from many sources as a result of managerial and operational business processes is itself a statically defined business process and knowledge workers have little to no control over the characteristics of the presentable data to which they have access.

There are two main reasons that dictate the reassessment of this stiff approach in context of modern business environments. The first reason is that the service-oriented nature of today’s business combined with the increasing volume of available data make it impossible for an organization to pro-actively design efficient data management processes that are specific to its internal operational scope. Thus, the existence of diversely structured heterogeneous data deriving from multiple internal and external sources suggest the definition of dynamic models and processes for its collection, cleaning, transformation, integration, analysis, monitoring and so on. The second reason is that enterprises can benefit significantly from analysing the behaviour of their business processes fostering their optimization. Such analysis is conducted by knowledge workers and business analysts who lack knowledge about underlying infrastructure of IT systems and related technologies. In this respect, they should be provided with dynamic user-centered tools that can facilitate ad hoc processing of analytical queries with minimal IT intervention.

This project aims at defining models, techniques and tools to support the alignment of user requirements with the runtime characteristics of business processes for data management. Thus, the first step is studying conceptual models for process modelling in context of data augmentation and data warehousing. After obtaining a deep understanding of data features and schematic variations, models will be defined that will describe data utility as a result of operations within the BI-related business processes. These models will facilitate reasoning about and evaluation of alternative business processes through quantitative analysis of the processes using concrete metrics. The ultimate goal of this project is the development of a user-centered framework that will automate the process of business process model selection for BI-related data processing. Some of the key requirements for the design of this framework are usability, efficiency and effectiveness.

Main Advisor at Universitat Politècnica de Catalunya (UPC)
Co-advisor at Technische Universität Dresden (TUD)