Abstract: Nowadays users are more willing to share their data, such as their personal profiles, preferences, and their interactions. Data analytics systems in turn, analyze this data and turn them into useful insights that can improve user experience and create long-lasting end-user relationships. Data visualization is among the most powerful tools utilized by analysts to understand and interpret these insights and translate them to effective decisions. However, the ability to quickly understand and interpret information is often affected by the data analysts’ unique characteristics and traits, which go deeper than traditional characteristics, such as demographics, business role and expertise. Even though business analytics tools have made a significant progress to deliver immersive data visualization environments for improving users' efficiency and effectiveness, they still do not consider the individual differences in the core process that influences the visualization structure, encoding, and readability. 

This talk will discuss how systems can leverage the users’ individual differences to deliver human-centered by-design adaptations for end-users. Such approaches can go beyond offering one-size-fits-all personalized experiences (e.g., grouping user data and finding similar patterns), implementing systems that can produce meaningful, relevant, and unique experiences, tailored for each user individually. The talk will also show a prototype adaptation engine that aims to improve the comprehension of data visualizations by delivering personalized content (visualization type and adaptation of visual elements), which in turn leads to improved accuracy and time-to-action efficiency. The proposed adaptation mechanism is evaluated using 45 professional business analysts from multiple industry sectors. The results suggest that individual differences can play an important role in the adaptation process of data visualizations enhancing analysts' comprehensibility and decision making.