Case studies of subjective data dimensions in business intelligence based on literature

Data quality is widely recognized as a decisive factor for the success of business intelligence systems, as it directly influences the reliability of insights, the effectiveness of decision-making, and the level of trust placed in analytical outcomes. Traditional approaches have emphasized technical aspects such as accuracy, completeness, and consistency. Recently, attention has shifted toward subjective, user-related dimensions of data quality, influenced by perception, trust, and understanding. This study responds to this development by defining and categorizing subjective dimensions of data quality and identifying the organizational and technical conditions affecting user perception and trust in business intelligence environments. A mixed-methods approach was employed, combining a structured literature review with five case studies conducted in financial and non-financial organizations. Data from the case studies were gathered through semi-structured interviews with practitioners responsible for designing and managing data solutions. The findings revealed four distinct categories of subjective data quality (data access, usability, processing, and evaluation), which together capture the ways in which users assess the relevance, interpretability, and value of data. Six critical success factors were identified as essential in shaping these perceptions: data governance, metadata management, knowledge and competence development, organizational culture, technological infrastructure, and stakeholder relationships. From these insights, five best practices were derived that support the enhancement of subjective data quality, such as developing business glossaries, comprehensive metadata catalogues, and transparent documentation of data lineage. The study concludes that subjective data quality is co-produced by technological infrastructures and human factors, and it proposes a multi-layered model that integrates these dimensions to guide the design of business intelligence systems that foster trust, understanding, and greater decision-making value.