www.ijird.com November, 2014 (Special Issue) Vol 3 Issue 12 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH & DEVELOPMENT Page 254 Big Data-Compelling Organizations beyond Data Warehousing 1. Introduction Data refers to the fact figures. Organizations are getting larger and amassing with ever increasing amounts of data. Historic data encodes useful information about working of an organization. However, data is scattered across multiple sources, in multiple formats. The process of consolidating data in a centralized location is possible through data warehousing. Google’s chief executive, Eric Schmidt, believes the world creates 5 Exabytes of data every two days. That’s roughly the same amount created between the dawn of civilization and 2003. Today the volume of data generated is increasing exponentially whereas the cost of data storage is falling. 2. Data Warehousing The data warehouse is the central repository where data from various systems and sources is collected. It is time variant since the data is collected over many time periods [7] for use in comparisons, trend analysis and forecasting [4]. The development methodology of data warehouse in different cases has been reported in various literatures [9],[11],[13]. When the organizational structure is high with a huge database, it is advisable to create appropriate number of data marts for independent functional divisions and then integrate all of them to get the total data warehouse. According to Craig (1997), data marts store subsets of information about product sales and other topics and may speed up the process of getting critical decision-support data to end users. But the proliferation of data marts can be a hassle for the warehousing staff that has to keep everything in synchronization. Seeking a way out of that trap, some companies are building virtual data marts that share a database but are presented to users as separate entities. Virtual data warehouses are best suited for applications that are of limited scope or duration [10]. Information from a customer database can be used to identify needs of different groups of customers. This knowledge can help shopping centers to improve marketing communications and customer satisfaction [3]. Data Warehousing along with the concepts of Knowledge Management and Data Mining helps in strategizing customer relationship management (CRM) in order to support the organization’s decision-making process to retain long term and profitable relationships with its customers [2]. The design of the CRM data warehouse model directly impacts an organization’s ability to readily perform analyses that are specific to CRM. Subsequently, the design of the CRM data warehouse model contributes to the success or failure of CRM. In fact, statistics indicate that between 50% and 80% of CRM initiatives fail due to inappropriate or incomplete CRM processes and poor selection of technologies [14],[16]. ISSN 2278 – 0211 (Online) Mrunal Joshi Assistant Professor, IES Management College and Research Centre (IESMCRC), Mumbai, India Pradip K. Biswas Associate Professor, National Institute of Industrial Engineering (NITIE), Mumbai, India Abstract: Data refers to the raw facts scattered across the organization. It serves the basis for generating information which leads to organizational decisions. The data is consolidated and stored in a data warehouse across the organizations over a period of time for trend analysis, comparisons and forecasting, allowing the enterprise data warehouse, enjoy the benefit of a strategic system. However, in past few years internet clicks are generating the volume of data which is precious for innovation. With comprehensive performance data available, companies can identify and focus on the high return improvement opportunities in business leading the improved performance in revenue. Storage and analysis of this data is imposing a challenge in traditional data warehousing technique. This paper is an attempt to examine the importance of data warehousing as well Big Data for an organization. The paper also attempts to explore what can be a way beyond data warehousing for effective management of Big Data. Keywords: Data Warehousing (DW), Big Data, Logical Data Warehousing (LDW), Organizational Performance, Emerging Trends in DW