Le développement des systèmes d’information numériques et plus particulièrement les systèmes interactifs d’aide à la décision (SIAD) orientés données (Application Analytics) rencontre des échecs divers.La plupart des études montrent que ces échecs de projets de développement de SIAD relève de la phase d’analyse des besoins et des exigences. Les exigences qu'un système doit satisfaire sont insuffisamment définies à partir de besoins réels des utilisateurs finaux.D’un point de vue théorique, l’analyse de l’état de l’art, mais également du contexte industriel particulier, conduit donc à porter une attention particulière à cette phase et à élaborer une approche collaborative d’analyse des besoins et des exigences dirigée par les problèmes.Un système d’aide à la décision est avant tout un système d’aide à la résolution de problèmes et le développement de ce type d’artefact ne peut donc se faire sans avoir convenablement identifié en amont les problèmes de décision auxquels font face les utilisateurs décideurs, afin d’en déduire les exigences et le type de SIAD.Cette approche, par un renversement de la primauté implicite de la solution technique par rapport à la typologie des problèmes de décision, a été explicitée et mise en œuvre pour le développement d’une Application Analytics qui a permis d’atteindre l’objectif attendu : un système efficace et qui satisfasse d’un triple point de vue technique, fonctionnel et ergonomique, ses différents utilisateurs finaux. / The design of digital information systems, especially interactive Data-Driven Decision Support System (DSS) (Analytics Application) often misses its target.Most of studies have proven that the sources of most DSS design failures are rooted in the analysis step of the users’ needs and requirements a system has to meet and comply with. From a theoretical point of view, the analysis of the state of art combined with the analysis of specific industrial contexts, leads to focus on this critical step, and consequently to develop a collaborative problem-driven requirements engineering approach.A DSS, first and foremost, is a problem solving support system. It implies that developing such an artefact cannot be performed without an adequate upstream identification of end-users’ decision problems, prior to defining the decision makers’ requirements and the appropriate type of DSS.Characterized by the reversal of the implicit primacy of technical solution versus the typology of decision problems, this approach has been elaborated and implemented to design an Analytics Application. As a result, it allowed to reach the expected objective: An effective system that meets the different end-users’ expectations from a technical, functional and ergonomic standpoint.

Background: Big data analytics has been referred to as a hype the past decade, making manyorganizations adopt data-driven processes to stay competitive in their industries. Many of theorganizations adopting big data analytics use agile methodologies where the most importantoutcome is to maximize business value. Multiple scholars argue that big data analytics lead toincreased business value, however, there is a theoretical gap within the literature about how agileorganizations can capture this business value in a practically relevant way. Purpose: Building on a combined definition that capturing business value means being able todefine-, communicate- and measure it, the purpose of this thesis is to explore how agileorganizations capture business value from big data analytics, as well as find out what aspects ofvalue are relevant when defining it. Method: This study follows an abductive research approach by having a foundation in theorythrough the use of a qualitative research design. A single case study of Nike Inc. was conducted togenerate the primary data for this thesis where nine participants from different domains within theorganization were interviewed and the results were analysed with a thematic content analysis. Findings: The findings indicate that, in order for agile organizations to capture business valuegenerated from big data analytics, they need to (1) define the value through a synthezised valuemap, (2) establish a common language with the help of a business translator and agile methods,and (3), measure the business value before-, during- and after the development by usingindividually idenified KPIs derived from the business value definition.