Business User-oriented Recommender System of Data

Companies nowadays are increasingly dependent on data. In an environment that is more dynamic than ever, they are looking for tools to leverage those data and obtain valuable information in a rapid and flexible way. One way to achieve this is by using Data-Driven Decision Support Systems (Data-Driven DSS). In this project, I focus on one such type of DSS, namely the Self-Service Business Intelligence (SSBI) Systems. These systems are designed specifically to avoid the involvement of the IT department when creating business reports by empowering businesspeople in the production of their own reports, thereby reducing the time-to-release of a given report and improving the responsiveness of companies. Business decision-makers, when developing their own reports, however face barriers. These challenges are related to the current self-service features that are not sufficiently adapted to their business needs and their lack of technical knowledge. The objective of my project is to build a framework based on Artificial Intelligence (AI) techniques such as Natural Language Processing techniques, Semantic and Recommender Systems to solve one of the main challenges faced by businesspeople, namely: the data picking within technical and large current databases. These AI systems offer a number of benefits that are strongly linked to the problems encountered by business users in the data picking process. This paper introduces the three main research questions of my thesis and positions them in the current literature. It then elaborates on the different theoretical, methodological and empirical contributions I plan to advance as part of my project.