Application of Business Intelligence Techniques in China Telecom

In this paper, we have developed a data warehouse and an OLAP( on-line analytical processing ) based framework that has been used for customer profiling and comparison. The system architecture for OLAP and data warehouse based calling behavior profiling and multilevel multidimensional pattern analysis is introduced. First, the method how customer profiles can be represented as data cubes is described, then, the architecture of a profiling engine is presented, finally, the process of using the engine to compute profiles and calling patterns is discussed and an application case of China telecom is studied

Business Intelligence: The Role of the Internet in Marketing Research and Business Decision-Making

The purpose of this paper is to point out the determinants of the business intelligence discipline, as applied in marketing practice. The paper examines the role of the Internet in marketing research and its implications on the business decision-making processes. Although companies conduct a variety of research methods in an offline environment, the paper aims to stress the importance of Web opportunities in conducting the Web segmentation and collecting customer data. Due to the existence of different perceptions concerning the role of the Internet, this paper tries to emphasize its effort of an interactive channel that serves the function of not only an informational nature, but as a powerful research tool as well. Several data collection and analysis methods/techniques are discussed that would help companies to take advantage of a Web as a significant corporate resource

Tutorial: Business Intelligence – Past, Present, and Future

Business intelligence (BI) is a broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions. This tutorial discusses some of the early, landmark contributions to BI; describes a comprehensive, generic BI environment; and discusses four impor-tant BI trends: scalability, pervasive BI, operational BI, and the BI based organization. It also identifies BI resources that are available for faculty and students

Tutorial: Big Data Analytics: Concepts, Technologies, and Applications

We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social media, machines, log files, video, text, image, RFID, and GPS. These sources have strained the capabilities of traditional relational database management systems and spawned a host of new technologies, approaches, and platforms. The potential value of big data analytics is great and is clearly established by a growing number of studies. The keys to success with big data analytics include a clear business need, strong committed sponsorship, alignment between the business and IT strategies, a fact-based decision-making culture, a strong data infrastructure, the right analytical tools, and people skilled in the use of analytics. Because of the paradigm shift in the kinds of data being analyzed and how this data is used, big data can be considered to be a new, fourth generation of decision support data management. Though the business value from big data is great, especially for online companies like Google and Facebook, how it is being used is raising significant privacy concerns

Recent Developments in Data Warehousing

Data warehousing is a strategic business and IT initiative in many organizations today. Data warehouses can be developed in two alternative ways -- the data mart and the enterprise-wide data warehouse strategies -- and each has advantages and disadvantages. To create a data warehouse, data must be extracted from source systems, transformed, and loaded to an appropriate data store. Depending on the business requirements, either relational or multidimensional database technology can be used for the data stores. To provide a multidimensional view of the data using a relational database, a star schema data model is used. Online analytical processing can be performed on both kinds of database technology. Metadata about the data in the warehouse is important for IT and end users. A variety of data access tools and applications can be used with a data warehouse - SQL queries, management reporting systems, managed query environments, DSS/EIS, enterprise intelligence portals, data mining, and customer relationship management. A data warehouse can be used to support a variety of users - executive, managers, analysts, operational personnel, customers, and suppliers. Data warehousing concepts are brought to life through a case study of Harrah\u27s Entertainment, a firm that became a leader in the gaming industry with its CRM business strategy supported by data warehousing