A Proactive Approach for BI

A framework to develop proactive BI is presented and discussed in this chapter. In a proactive process, analysis develops as a scientific investigation or research, where analysts must discover an initial set of hypotheses and then test or evaluate the hypotheses. The steps of the framework are presented and ways to perform each step are discussed. Data mining techniques are associated to this proactive paradigm, exploring how they can be applied in each step of the framework. Although there are not yet experiments that validate the correctness of this framework, the objective of the chapter is to focus attention on the differences in the paradigms, discuss ways to perform BI in a proactive way, and alert analysts and executives for practical experiences with the proactive paradigm.

ASD-BI

ASD-BI is an agile “marriage” between business intelligence and data mining. It is one of the first attempts to apply an Adaptive Software Development (ASD) agile method to business intelligence systems. The ASD-BI methodology's main characteristics are adaptive to environment changes, enhance knowledge capturing and sharing, and help in implementing and achieving an organization's strategy. The focus of the chapter is to demonstrate how agile methods would enhance the integration of data mining in business intelligence systems. The chapter presents ASD-BI main characteristics and provides two case studies, one on higher education and the other on (Bibliomining). The main result of the chapter is that applying agile methodologies for integrating business intelligence and data mining systems would increase transfer of tacit knowledge and raise the strategic dimension of using the knowledge discovery process.

The Role of Data Mining for Business Intelligence in Knowledge Management

This chapter introduces the role of Data Mining (DM) for Business Intelligence (BI) in Knowledge Management (KM), thus explaining the concept of KM, BI, and DM; the relationships among KM, BI, and DM; the practical applications of KM, BI, and DM; and the emerging trends toward practical results in KM, BI, and DM. In order to solve existing BI problems, this chapter also describes practical applications of KM, BI, and DM (in the fields of marketing, business, manufacturing, and human resources) and the emerging trends in KM, BI, and DM (in terms of larger databases, high dimensionality, over-fitting, evaluation of statistical significance, change of data and knowledge, missing data, relationships among DM fields, understandability of patterns, integration of other DM systems, and users' knowledge and interaction). Applying DM for BI in the KM environments will enhance organizational performance and achieve business goals in the digital age.

A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction

Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.

Data Quality for Data Mining in Business Intelligence Applications

Data Quality (DQ) in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in Business Intelligence (BI) applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI System has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of DQ definition and measurement for data mining for BI, analyzes the gaps therein, besides reviewing proposed solutions and providing a direction for future research and practice in this area.

Pre-Triage Decision Support Improvement in Maternity Care by Means of Data Mining

A triage system aims to make a correct characterization of the condition of patients. Because conventional triage systems like Manchester Triage System (MTS) are not suitable for maternity care, a decision model for pre-triaging patients in emergency (URG) and consultation (ARGO) classes was built and incorporated into a Decision Support System (DSS) implemented in Centro Materno Infantil do Norte (CMIN). Complementarily, DSS produces several indicators to support clinical and management decisions. A recent data analysis revealed a bias in the classification of URG cases. Frequently, cases classified as URG correspond to ARGO. This misclassification has been studied by means of Data Mining (DM) techniques in order to improve the pre-triage model and to discover knowledge for developing a new triage system based on waiting times and on a 5-scale of classes. This chapter presents a kind of sensitivity analysis combining input variables in six scenarios and considering four different DM techniques. CRISP-DM methodology was used to conduct the project.

3rd Order Analytics Demand Planning

It is commonly proposed that a greater number of individuals should have access to enterprise-level data, and that they should be able to analyze it readily and individually with data mining tools. Although the authors support greater use of Predictive Analytics by the enterprise, they favor more ready access to predictions, not to raw data. Forecasting is among the more difficult analytical challenges. Despite the importance of accurate forecasts, organizations often resort to the subjective judgment of a business analyst. Forecasts are also among the most widely used analytics, broadly distributed to the organization. The authors propose an approach that centralizes the forecasting activity using Predictive Analytics but preserves the wide distribution of the resulting forecast using Business Intelligence technology.

Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence

Business Intelligence (BI) refers to an organization's capability to gather and analyze data about business operations and transactions in order to evaluate its performance. The abundance of information both within the enterprise and outside of it has necessitated a change in traditional Business Intelligence practices. There is a need to exploit heterogeneous resources. Text data like news, analyst reports, etc. helps in better interpretation of business data. In this chapter, the authors present a futuristic BI framework that facilitates acquisition, indexing, and analysis of heterogeneous data for extracting business intelligence. It enables integration of unstructured text data and structured business data seamlessly to generate insights. The authors propose methods that can help in extraction of events or significant happenings from both unstructured and structured data, correlate the events, and thereafter reason to generate insights. The insights extracted could be validated as cause-effect pairs based on the statistical significance of co-occurrence of events.

Analyzing Customer Behavior Using Online Analytical Mining (OLAM)

Online Analytical Mining (OLAM) is an architecture integrating data mining into OLAP. With this integration, data mining algorithms can be performed with OLAP abilities. OLAM enables users to choose a particular portion of data and analyze them with data mining models. Previous studies have provided examples of OLAM applications with the motivation to improve technical performance. This chapter reviews the capabilities of OLAM and discusses the well-known concept encompassing the analysis of customer behavior. The underlying motivation of this chapter is to present the opportunities for the development of OLAM to support the customer behavior analysis. Three main directions of the advancement in OLAM are proposed for future research.

Data Mining and Business Intelligence

Data Mining (DM) is being applied with success in Business Intelligence (BI) environments, and several examples of applications can be found. BI and DM have different roots and, as a consequence, have significantly different characteristics. DM came up from scientific environments; thus, it is not business oriented. DM tools still demand heavy work in order to obtain the intended results. On the contrary, BI is rooted in industry and business. As a result, BI tools are user-friendly. This chapter reflects on this difference from a historical perspective. Starting with a separated historical perspective of each one, BI and DM, the author then discusses how they converged into the current situation, when DM is used, and integrated, in BI environments with success.