A Goal-Oriented Big Data Analytics Framework for Aligning with Business

G. Park, L. Chung, L. Zhao, S. Supakkul

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Big data analytics is the hottest new technology which helps turn hidden insights in big data into business value to support a better decision-making. However, current big data analytics has many challenges to do it since there is a big gap between big data analytics and business. This is mainly because lack of business context around the data, lack of expertise to connect the dots, and implicit business objectives. In this paper, we present IRIS - a big data analytics framework for aligning with business in a goal-oriented approach. It is composed of ontology for a business context model, analytics methods for connecting big data with business, an action process for collaborative work and an assistant tool utilizing Spark. In this framework, problems of the current process and solutions for the future process are hypothesized in an explicit business context model and validated them by using diverse analytics methods implemented on top of Spark libraries. Also, a goal-oriented approach enables to explore and select alternatives among potential problems and solutions. A business process for clearance pricing decision is used to show how big data analytics can be turned into business value by using our framework which align big data to business goals, as well as for an initial understanding of the applicability of IRIS.
Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2017
DOIs
Publication statusPublished - Apr 2017

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