Expanding Business Intelligence Power with System Dynamics

This chapter explores the opportunities to expand the forecasting and business understanding capabilities of Business Intelligence (BI) tools with the support of the system dynamics approach. System dynamics tools can enhance the insights provided by BI applications — specifically by using data-mining techniques, through simulation and modeling of real world under a “systems thinking” approach, improving forecasts, and contributing to a better understanding of the business dynamics of any organization. Since there is not enough diffusion and understanding in the business world about system dynamics concepts and advantages, this chapter is intended to motivate further research and the development of better and more powerful applications for BI.

Data Mining and Knowledge Discovery

In today’s business world, the use of computers for everyday business processes and data recording has become virtually ubiquitous. With the advent of this electronic age comes one priceless by-product — data. As more and more executives are discovering each day, companies can harness data to gain valuable insights into their customer base. Data mining is the process used to take these immense streams of data and reduce them to useful knowledge. Data mining has limitless applications, including sales and marketing, customer support, knowledge-base development, not to mention fraud detection for virtually any field, etc. “Data mining,” a bit of a misnomer, refers to mining the data to find the gems hidden inside the data, and as such it is the most often-used reference to this process. It is important to note, however, that data mining is only one part of the Knowledge Discovery in Databases process, albeit it is the workhorse. In this chapter, we provide a concise description of the Knowledge Discovery process, from domain analysis and data selection, to data preprocessing and transformation, to the data mining itself, and finally the interpretation and evaluation of the results as applied to the domain. We describe the different flavors of data mining, including association rules, classification and prediction, clustering and outlier analysis, customer profiling, and how each of these can be used in practice to improve a business’ understanding of its customers. We introduce the reader to some of today’s hot data mining resources, and then for those that are interested, at the end of the chapter we provide a concise technical overview of how each data-mining technology works.

Understanding Decision-Making in Data Warehousing and Related Decision Support Systems

Many businesses have made or are making significant investments in data warehouses that reportedly support a myriad of decision support systems (DSS). Due to the newness of data warehousing and related DSS (DW-DSS), the nature of the decision support provided to DW-DSS users and the related impact on decision performance have not been investigated in an applied setting. An explanatory case study was undertaken at a financial services organization that implemented a particular type of DW-DSS, a Customer Relationship Management (CRM) system. The DSS-decision performance model has provided some theoretical guidance for this exploration. The case study results show that the decision-making support provided by these systems is limited and that an extended version of the DSS-decision performance model may better describe the factors that influence individual decision-making performance.

Data Mining and Business Intelligence

Most businesses generate, are surrounded by, and are even overwhelmed by data — much of it never used to its full potential for gaining insights into one’s own business, customers, competition, and overall business environment. By using a technique known as data mining, it is possible to extract critical and useful patterns, associations, relationships, and, ultimately, useful knowledge from the raw data available to businesses. This chapter explores data mining and its benefits and capabilities as a key tool for obtaining vital business intelligence information. The chapter includes an overview of data mining, followed by its evolution, methods, technologies, applications, and future.

Enterprise Information Management

Although capacity and functionality of information management systems increased remarkably in the last years, the information and knowledge supply in most enterprises is still not sufficient. Using the framework of enterprise information management, organizations are able to align their existing data warehouse, business intelligence, knowledge management, and other information systems to their business processes and requirements. This means a consolidation on one hand and continuous processes to manage change on the other to improve these systems’ sustainability and to decrease costs the same time.

Intelligent Agents for Competitive Advantage

This chapter presents a high-level model for employing intelligent agents in business management processes, much like has been successfully accomplished in complex telecommunications networks, in order to gain competitive advantage by timely, rapidly, and effectively using key, unfiltered measurements to improve cycle-time decision making. The importance of automated, timely, unfiltered (versus “end of period” filtered) reports is highlighted, as are some management issues relative to the pressures that may result concerning an organization’s employees who must now take action in near real time. Furthermore, the authors hope that understanding the underlying assumptions and theoretical constructs through the use of employing intelligent agents in business management processes as a sub element of, or tool within Business Intelligence (BI), will not only inform researchers of a better design for studying information systems, but also assist in the understanding of intricate relationships between different factors.

Transforming Textual Patterns into Knowledge

Business Intelligence (BI) can benefit greatly from the bulk of knowledge that stays hidden in the large amount of textual information existing in the organizational environment. Text Mining (TM) is a technology that provides the support to extract patterns from texts. After interpreting these patterns, a business analyst can reach useful insights to improve the organizational knowledge. Although text represents the largest part of the available information in a company, just a small part of all Knowledge Discovery (KD) applications are in TM. By means of a case study, this chapter shows an alternative to how TM can contribute to BI. Also, a discussion on future trends and some conclusions are presented that support the effectiveness of TM as source of relevant knowledge.

Text Mining in Business Intelligence

As the demand for more effective Business Intelligence (BI) techniques increases, BI practitioners find they must expand the scope of their data to include unstructured text. To exploit those information resources, techniques such as text mining are essential. This chapter describes three fundamental techniques for text mining in business intelligence: term extraction, information extraction, and link analysis. Term extraction, the most basic technique, identifies key terms and logical entities, such as the names of organizations, locations, dates, and monetary amounts. Information extraction builds on terms extracted from text to identify basic relationships, such as the roles of different companies in a merger or the promotion of a chemical reaction by an enzyme. Link analysis combines multiple relationships to form multistep models of complex processes such as metabolic pathways. The discussion of each technique includes an outline of the basic steps involved, characteristics of appropriate applications, and an overview of its limitations.

Reducing Risk in Information Search Activities

This chapter proposes that the initial perceptions of uncertainty and risk relating to decision making are unlikely to be modified irrespective of the quantity or quality of the information transmitted and processed by the decision maker. It argues that initial risk perceptions and decisions are fairly robust even when confronted with contradictory information. The chapter begins by offering definitions of the key terms such as risk, uncertainty, and the components of the digital economy. The authors then provide an overview of risk assessment and associated management processes before moving onto an examination of the contribution of intelligence and information to risk resolution. A case scenario provides a practical illustration of the issues raised.

An Intelligent Knowledge-Based Multi-Agent Architecture for Collaboration (IKMAC) in B2B e-Marketplaces

This chapter presents an Intelligent Knowledge-Based Multi-Agent Architecture for Collaboration (IKMAC) in B2B e-Marketplaces. IKMAC is built upon existing bodies of knowledge in intelligent agents, knowledge management, e-business, XML, and web service standards. This chapter focuses on the translation of data, information, and knowledge into XML documents by software agents, thereby creating the foundation for knowledge representation and exchange by intelligent agents that support collaborative work between business partners. The realization of the proposed architecture is explained through an infomediary-based e-Marketplace prototype in which agents facilitate collaboration by exchanging their knowledge using XML and related sets of standards. Use of such systems will provide collaborating partners with intelligent knowledge management (KM) capabilities for seamless and transparent exchange of dynamic supply and demand information.