The development of a research, teaching, or application of competitive (economic) intelligence requires a strategic and transverse vision in regards to related issues. It is essential to integrate the role of culture when interpreting results, either from the training of a specialist or in respect to a country or region. The authors of this book, members of an expert group supported by the CNRS in France, bring all of their talents together to create a comprehensive book that does just this and more.

Cognition-driven decision support system (DSS) has been recognized as a paradigm in the research and development of business intelligence (BI). Cognitive decision support aims to help managers in their decision making from human cognitive aspects, such as thinking, sensing, understanding and predicting, and fully reuse their experience. Among these cognitive aspects, decision makers’ situation awareness (SA) and mental models are considered to be two important prerequisites for decision making, particularly in ill-structured and dynamic decision situations with uncertainties, time pressure and high personal stake. In today’s business domain, decision making is becoming increasingly complex. To make a successful decision, managers’ SA about their business environments becomes a critical factor. This book presents theoretical models as well practical techniques of cognitiondriven DSS. It first introduces some important concepts of cognition orientation in decision making process and some techniques in related research areas including DSS, data warehouse and BI, offering readers a preliminary for moving forward in this book. It then proposes a cognition-driven decision process (CDDP) model which incorporates SA and experience (mental models) as its central components. The goal of the CDDP model is to facilitate cognitive decision support to managers on the basis of BI systems. It also presents relevant techniques developed to support the implementation of the CDDP model in a BI environment. Key issues addressed of a typical business decision cycle in the CDDP model include: natural language interface for a manager’s SA input, extraction of SA semantics, construction of data warehouse queries based on the manger’s SA and experience, situation information retrieval from data warehouse, how the manager perceives situation information and update SA, how the manager’s SA leads to a final decision. Finally, a cognition-driven DSS, FACETS, and two illustrative applications of this system are discussed.

A practical introduction to the necessity of competitive intelligence for smarter business decisions-from a leading CI expert and speaker In Competitive Intelligence Advantage, Seena Sharp, founder of one of the first Competitive Intelligence firms in the US, provides her expert analysis on the issues and benefits of CI for today's businesses. CI is critical for making smarter business decisions and reducing risks when formulating strategies, leading to more profits and fewer mistakes. This is a practical guide that explains what CI is, why data is not intelligence, why competitor intelligence is a weak sibling to competitive intelligence, when to use it, how to find the most useful information and turn it into actual intelligence, and how to present findings in the most convincing manner. Importantly, Sharp argues that businesses would benefit from shifting their perspective on CI from viewing it as a cost to viewing it as an investment that saves money and provides immediate value. Author Seena Sharp is a noted CI expert who established Sharp Market Intelligence in 1979 Addresses all the most common myths and misconceptions about CI Includes more than sixty examples of when to use CI Completely explains the ins and outs of CI, and why your company will act faster and more aggressively with CI Competitive intelligence is a management tool that is misunderstood and underestimated, yet results in numerous benefits. If you are a senior level executive or operate a business-and you aren't tapping the power of CI to improve your decision making-you are missing a potent advantage.

Competitive intelligence (CI) is about gathering and analyzing environmental information for strategic purposes. This work shows that to select the right information and communication technology (ICT) tools for CI, an organization needs to understand the role of ICT in the CI-process.

The work addresses to specialists in informatics, with preoccupations in development of Business Intelligence systems, and also to beneficiaries of such systems, constituting an important scientific contribution. Experts in the field contribute with new ideas and concepts regarding the development of Business Intelligence applications and their adoption in organizations. This book presents both an overview of Business Intelligence and an in-depth analysis of current applications and future directions for this technology. The book covers a large area, including methods, concepts, and case studies related to: constructing an enterprise business intelligence maturity model, developing an agile architecture framework that leverages the strengths of business intelligence, decision management and service orientation, adding semantics to Business Intelligence, towards business intelligence over unified structured and unstructured data using XML, density-based clustering and anomaly detection, data mining based on neural networks.

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.

In his research, Martin Kowalczyk empirically investigates the challenges of designing and establishing successful decision support with Business Intelligence and Analytics (BI&A). The results from his work elucidate organizational and individual perspectives of BI&A support in decision processes. The organizational perspective considers the processual aspects of decision making and addresses process phases, roles and their interactions. The individual perspective reflects upon decision making of human individuals including their cognition and behaviors involved in decision making. The support of managerial decision making with BI&A gains increasing priority for many businesses in their desire to achieve better decision outcomes and improved organizational performance.