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The modern business environment is empowered by the abundant availability of data and plethora of sophisticated data analysis tools to identify and quickly address market needs. While these tools have evolved significantly during the last years, offering trailblazing data exploration experiences with stunning multi-modal visualizations, they mistreat the importance of individualized, user-centred delivery of information/insights. As a result, users may require much more effort and time to reach decisions that have implications on both the short-term and long-term success of sustainability of an organization. This paper highlights the need for user-centred/persona-driven data exploration through adaptive data visualizations and personalized support to an end-to-end business process. It proposes an extended human-centred persona and discusses preliminary exploratory results in relation to the formulation of the contextual characteristics of a business environment, i.e., business tasks, visualizations and data.

The business data analytics domain exhibits a particularly diversified and demanding field of interaction for the end-users. It entails complex tasks and actions expressed by multidimensional data visualization and exploration contents that users with different business roles, skills and experiences need to understand and make decisions so to meet their goals. Many times this engagement is proven to be overwhelming for professionals, highlighting the need for adaptive and personalized solutions that would consider their level of expertise towards an enhanced user experience and quality of outcomes. However, measuring adequately the perceived expertise of individuals using standardized means is still an open challenge in the community. As most of the current approaches employ participatory research design practices that are time consuming, costly, difficult to replicate or to produce comparable, unbiased, results for informed interpretations. Hence, this paper proposes a systematic alternative for capturing expertise through a Perceived Expertise Tool (PET) that is devised based on grounded theoretical perspectives and psychometric properties. Preliminary evaluation with 54 professionals in the data analytics domain showed the accepted internal consistency and validity of PET as well as its significant correlation with other affiliated theoretical and domain-specific concepts. Such findings may suggest a good basis for the standardized modeling of users' perceived expertise that could lead to effective adaptation and personalization.

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