Visualization Requirements for Business Intelligence Analytics: A Goal-Based, Iterative Framework

Abstract

Information visualization plays a key role in business intelligence analytics. With ever larger amounts of data that need to be interpreted, using the right visualizations is crucial in order to understand the underlying patterns and results obtained by analysis algorithms. Despite its importance, defining the right visualization is still a challenging task. Business users are rarely experts in information visualization, and they may not exactly know the most adequate visualization tools or patterns for their goals. Consequently, misinterpreted graphs and wrong results can be obtained, leading to missed opportunities and significant losses for companies. The main problem underneath is a lack of tools and methodologies that allow non-expert users to define their visualization and data analysis goals in business terms. In order to tackle this problem, we present an iterative goaloriented approach based on the i* language for the automatic derivation of data visualizations. Our approach links non-expert user requirements to the data to be analyzed, choosing the most suited visualization techniques in a semi-automatic way. The great advantage of our proposal is that we provide nonexpert users with the best suited visualizations according to their information needs and their data with little effort and without requiring expertise in information visualization.

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“…Questions like the aforementioned condition crucial design decisions [56], and crucial design decisions need to be driven by the final consumers of dashboards, the users. That is why including the user in this meta-model is essential, because they will be using dashboards to reach insights, to support their decision-making processes or to exploit datasets.…”

Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production

“…Figure 2 summarizes the process followed in Phase 1, which defines visualizations. Firstly, users create a User Requirements Model aided by a sequence of guidelines published in [10]. This model guides non-expert users to capture their analytical needs.…”

“…Furthermore, through this User Requirements Model, users define, among others, which elements of the data source they wish to represent in the visualizations. Complementary to this model, a Data Profiling Model [10] is obtained by analyzing the features of the data sources to be visualized in a semi-automatic manner.…”

“…This element helps users to choose which visualization goal best suits their needs. In [10], we propose other alternatives to make the definition of model elements easier for non-expert users.…”

“…In previous works [10,11], we defined a model that helped users to specify and achieve their goals. It extracted the characteristics of the data sources and automatically derived the best type of visualizations according to the defined context.…”