GOMA: Supporting Big Data Analytics with a Goal-Oriented Approach

The real value of Big Data lies in its hidden insights, but the current focus of the Big Data community is on the technologies for mining insights from massive data, rather than the data itself. The biggest challenge facing industries is not how to identify the right data, but instead, it is how to use insights obtained from Big Data to improve the business. To address this challenge, we propose GOMA, a goal-oriented modeling approach to Big Data analytics. Powered by Big Data insights, GOMA uses a goal-oriented approach to capture business goals, reason about business situations, and guide decision-making processes. GOMA provides a systematic approach for integrating two types of the resulting insight from data analytics to goal-oriented reasoning and decision-making processes: descriptive insights are the ones that describe the current state (e.g., the current customer retention rate) and predictive insights are the ones that predict likely future phenomena by inference from the data (e.g., customers who are likely to defect). To aid in the description and illustration of the GOMA approach, a retail banking churning scenario is used as a running example throughout this paper.