Business Intelligence

2012 ◽  
pp. 1-13 ◽  
Author(s):  
Mouhib Alnoukari ◽  
Humam Alhammami Alhawasli ◽  
Hatem Abd Alnafea ◽  
Amjad Jalal Zamreek
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Data Warehousing ◽  
Historical Overview ◽  
Data Marts ◽  
Main Components

This chapter attempts to define the knowledge body of Business Intelligence. It provides an overview of the context we have been working in. The chapter starts with a historical overview of Business Intelligence stating its different stages and progressions. Then, the authors present an overview of what Business Intelligence is, its architecture and goals, and its main components including: data mining, data warehousing, and data marts. Finally, the Business Intelligence ‘marriage’ with knowledge management is discussed in details. The authors hope to contribute to the recent discussions about Business Intelligence goals, concepts, architecture, and components.

A Conceptual Framework for Data Mining and Knowledge Management

2011 ◽  
pp. 1-15 ◽  
Author(s):  
Shamsul I. Chowdhury
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Base ◽  
Business Processes ◽  
Data Warehousing ◽  
Case Based Reasoning ◽  
Integrated Framework ◽  
Large Databases ◽  
Main Components ◽  
Mining Tools

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

A Conceptual Framework for Data Mining and Knowledge Management

2010 ◽  
pp. 418-432
Author(s):  
Shamsul I. Chowdhury
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Base ◽  
Business Processes ◽  
Data Warehousing ◽  
Case Based Reasoning ◽  
Integrated Framework ◽  
Large Databases ◽  
Main Components ◽  
Mining Tools

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

The Role of Data Mining for Business Intelligence in Knowledge Management

2015 ◽  
pp. 12-33 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Human Resources ◽  
Organizational Performance ◽  
Business Intelligence ◽  
Statistical Significance ◽  
Practical Applications ◽  
Business Goals ◽  
Emerging Trends

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.

Introduction to Data Mining and its Applications to Manufacturing

2008 ◽  
pp. 146-168 ◽  
Author(s):  
Jose D. Montero
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Computational Intelligence ◽  
Data Warehousing ◽  
Great Promise ◽  
Main Stream ◽  
Manufacturing Companies ◽  
Using Data

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.

Introduction to Data Mining and its Applications to Manufacturing

2011 ◽  
pp. 144-166
Author(s):  
Jose D. Montero
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Computational Intelligence ◽  
Data Warehousing ◽  
Great Promise ◽  
Main Stream ◽  
Manufacturing Companies ◽  
Using Data

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

2016 ◽  
Vol 5 (1) ◽  
pp. 57-69 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Literature Review ◽  
Human Resources ◽  
Business Intelligence ◽  
Core Competencies ◽  
Huge Amount ◽  
Practical Applications ◽  
Business Information ◽  
Business Opportunities

This article reviews the literature in the search for the multifaceted applications of data mining (DM), business intelligence (BI), and knowledge management (KM). The literature review highlights the overviews of DM, BI, and KM; the practical applications of DM, BI, and KM; and the prospects of DM, BI, and KM in terms of marketing, business, human resources, and manufacturing. DM plays a key role in organizing huge amount of data and condensing it into valuable information. BI involves the delivery and integration of relevant and useful business information in an organization. KM allows companies to manage a system of core competencies in order to maximize business opportunities and minimize the risk of losing business opportunities. The findings present valuable insights and further understanding of the way in which DM, BI, and KM efforts should be focused.

scholarly journals Impact of Business Intelligence on Technical Creativity: A Case Study on AlHekma Pharmaceutical Company

2016 ◽  
Vol 12 (28) ◽  
pp. 502 ◽  
Author(s):  
Hani J. Irtaimeh ◽  
Abdallah Mishael Obeidat ◽  
Shadi. H Abualloush ◽  
Amineh. A Khaddam
Keyword(s):  
Data Mining ◽  
Pharmaceutical Company ◽  
Business Intelligence ◽  
Data Warehousing ◽  
Modern Technology ◽  
Technical Creativity ◽  
Study Population ◽  
Analytical Processing ◽  
The Impact

Business Intelligence, through its dimensions (data warehousing, data mining, direct analytical processing), helps the members of an organization to perceive and interpret their role in the organization’s creativity. For this reason, we may assume that Business Intelligence has an impact on Technical Creativity, and that matching of Business Intelligence and Technical Creativity will improve and achieve excellence in an organization. The aim of this study is to explore the impact of business intelligence dimensions (data warehousing, data mining, direct analytical processing) on Technical Creativity in AlHekma Pharmaceutical Company as a case study. For this purpose, a questionnaire was developed to collect data from the study population which consists of 50 employees. This is aimed at testing the hypotheses and achieving the objectives of the study. The most important results that the study achieved were that there was a statistically significant impact of business intelligence with its dimensions (data warehousing, data mining, and direct analytical processing) in technical creativity. The most important recommendations of the study were the necessity of organizations dependence on modern technology in order to develop their works. Thus, this is because this technology is recognized by its high accuracy on a completion of the work, as well as deepening the concept of technical creativity which gives them a competitive advantage in the marke

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

2018 ◽  
pp. 810-825 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Literature Review ◽  
Human Resources ◽  
Business Intelligence ◽  
Core Competencies ◽  
Huge Amount ◽  
Practical Applications ◽  
Business Information ◽  
Business Opportunities

This article reviews the literature in the search for the multifaceted applications of data mining (DM), business intelligence (BI), and knowledge management (KM). The literature review highlights the overviews of DM, BI, and KM; the practical applications of DM, BI, and KM; and the prospects of DM, BI, and KM in terms of marketing, business, human resources, and manufacturing. DM plays a key role in organizing huge amount of data and condensing it into valuable information. BI involves the delivery and integration of relevant and useful business information in an organization. KM allows companies to manage a system of core competencies in order to maximize business opportunities and minimize the risk of losing business opportunities. The findings present valuable insights and further understanding of the way in which DM, BI, and KM efforts should be focused.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

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