Business intelligence model for unstructured data management

Abstract

Business Intelligence plays an important role in the organization for collecting, integrating, analyzing and transforming data to be useful for effective decision making process. Nowadays, organizations are flooded with various kinds of unstructured data such as e-mail, images, reports, maps, charts, publications. An effective and efficient business model of these data could help in decision making. Currently, there is no study done on the business intelligence model for managing unstructured data that can fulfil the organization needs. Therefore, the purpose of this paper is to improve the organization's business intelligence process through the exploitation of unstructured data that is owned by the organization. In this study, unstructured data are classified, enriched and complemented with diversity of data through the process of creating metadata for each unstructured data. Four main processes are proposed to transform unstructured data to structured data which are extraction, classification, storage and mapping of data classes. Each process and its activities are combined to produce an effective and efficient business intelligence model for unstructured data management. This model helps in generating new data and information that is more comprehensive and collective to help business intelligence through advanced analysis, decision-making process and planning new research areas. Output from this study is to make unstructured data as renewable assets that is easily accessible and used as a reference and foundation in business intelligence and decision making process.