Abstract
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 goaloriented 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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016 |
| Publisher | IEEE |
| Number of pages | 8 |
| ISBN (Electronic) | 978-1-5090-2622-7 |
| DOIs | |
| Publication status | Published - 6 Oct 2016 |
| Event | IEEE BigData Congress 2016: 5th IEEE International Congress on Big Data - InterContinental Mark Hopkins, San Francisco, United States Duration: 27 Jun 2016 → 2 Jul 2016 http://www.ieeebigdata.org/2016/about.html |
Conference
| Conference | IEEE BigData Congress 2016 |
|---|---|
| Abbreviated title | BigData Congress 2016 |
| Country/Territory | United States |
| City | San Francisco |
| Period | 27/06/16 → 2/07/16 |
| Internet address |