Data Warehousing Requirements Collection and Definition

Defining data warehouse requirements is widely recognized as one of the most important steps in the larger data warehouse system development process. This paper examines the potential risks and pitfalls within the data warehouse requirement collection and definition process. A real scenario of a large-scale data warehouse implementation is given, and details of this project, which ultimately failed due to inadequate requirement collection and definition process, are described. The presented case underscores and illustrates the impact of the requirement collection and definition process on the data warehouse implementation, while the case is analyzed within the context of the existing approaches, methodologies, and best practices for prevention and avoidance of typical data warehouse requirement errors and oversights.

Enterprise Information System and Data Mining

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

Do Users Go Both Ways?

A challenging task for a data warehouse team is identifying users by their information needs and skills, and then providing the BI (Business Intelligence) tools that support each group to do their job effectively and efficiently. Recent studies have shown that the BI market place is saturated with a bewildering array of capabilities, functions and software suites. The current lack of consistent interpretation of Business Intelligence has created some confusion in the market place. This paper defines a framework to identify different user groups in an organization and map their needs and requirements to the different functionalities offered by different BI tool vendors. Through literature review, clear definitions of users were created and a set of BI tools that identifies functional needs was established. From that information, a questionnaire was developed that probed for the relationships between user types, tools, functions and other perceived values. Responses from 154 professionals were then used to develop a road map for the data warehouse project team in BI tool selection.

Data Mining for Health Care Professionals

In this paper, the authors discuss a data mining course that was offered for a cohort of health care professionals employed by a hospital consortium as an elective in a synchronous online MBA program. The students learned to use data mining to analyze data on two platforms, Enterprise Miner, SAS (2008) and XLMiner (an EXCEL add-in). The final assignment for the semester was for the students to analyze a data set from their place of employment. This paper describes the projects and resulting benefits to the companies for which the students worked.

The Role of Culture in Business Intelligence

Global business intelligence will struggle to live up to its potential if it fails to take into account, and accurately interpret, cultural differences. This paper supports this assertion by considering the concept of culture, explaining its importance in the business intelligence process, especially in foreign markets, and demonstrating that attention to culture is currently inadequate in most international business intelligence efforts. Without a tool capable of modeling social interaction in disparate cultures, BI efforts will under perform when extended to the global arena. The Cultural Simulation Modeler is examined as a means of enhancing essential cultural awareness. The core components of the modeler are explained, as are the limitations of automated information gathering and analysis systems.

Business Plus Intelligence Plus Technology Equals Business Intelligence

In this article the authors will show how the parallel developments of information technology at the operational business level and decision support concepts progressed through the decades of the twentieth century with only minimal success at strategic application. They will posit that the twin technological developments of the world-wide-web and very inexpensive mass storage provided the environment to facilitate the convergence of business operations and decision support into the strategic application of business intelligence.

Business Intelligence 2.0

Business Intelligence 2.0 is an umbrella term used to refer to a collection of tools that help organizations extend their BI capabilities using Internet platforms. BI 2.0 tools can enable the automatic discovery of distributed software services and data stores, greatly increasing the range of market options for an organization. The development cycle for these tools is still in its early stage, and much work remains. However, some technologies and standards are already well understood in order to make a significant impact. This paper provides an overview of the eXtensible Markup Language (XML) and related technologies supporting the deployment of web services and service-oriented architectures (SOA). The author summarizes the critical importance of these technologies to the emergence of BI 2.0 tools. This paper also explores the current state of Internet-enabled BI activities and strategic considerations for firms considering BI 2.0 options.

Strategies for Document Management

Keyword search has failed to adequately meet the needs of enterprise users. This is largely due to the size of document stores, the distribution of word frequencies, and the indeterminate nature of languages. The authors argue a different approach needs to be taken, and draw on the successes of dimensional data modeling and subject indexing to propose a solution. They test our solution by performing search queries on a large research database. By incorporating readily available subject indexes into the search process, they obtain order of magnitude improvements in the performance of search queries. Their performance measure is the ratio of the number of documents returned without using subject indexes to the number of documents returned when subject indexes are used. The authors explain why the observed tenfold improvement in search performance on our research database can be expected to occur for searches on a wide variety of enterprise document stores.

What is Business Intelligence?

There has been growing corporate interest in business intelligence (BI) as a path to reduced costs, improved service quality, and better decision-making processes. However, while BI has existed for years, it has difficulties reaching what specialists in the field consider its full potential. In this paper, the authors examine disparities in how the constructs of business intelligence are defined and understood, which may impede an understanding of what BI represents to business leaders and researchers. The main objective of this study is to clearly understand this emerging concept of BI. In this regard, the authors analyze articles from the scientific and professional literature to have a comprehensive understanding of business intelligence as both a product and a process. This research proposes a global overview of the conceptual foundations of BI, which can help companies understand their BI initiative and leverage them to the strategic level.

Historical Data Analysis through Data Mining from an Outsourcing Perspective

The process of historical data analysis through data mining has proven valuable for the industrial environment. There are many models available that describe the in-house process of data mining. However, many companies either do not have in-house skills or do not wish to invest in performing in-house data mining. This paper investigates the applicability of two well-established data mining process models in an outsourcing context. The authors observe that both models cannot properly accommodate several key aspects in this context; therefore, this paper proposes the Three-phases method, which consists of data retrieval, data mining and results implementation within an organization. Each element is presented as a visual method fragment, and the model is validated through expert interviews and an extensive case study at a large Dutch staffing company. Both validation techniques substantiate the authors’ claim that the Three-phases model accurately describes the data mining process from an outsourcing perspective.