...SPECIAL ISSUE: BUSINESS INTELLIGENCE RESEARCH BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords:...
Words: 16335 - Pages: 66
...SPECIAL ISSUE: BUSINESS INTELLIGENCE RESEARCH BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research...
Words: 16335 - Pages: 66
...Data Mining Data Mining THE BUSINESS SCHOOL, KASHMIR UNIVERSITY 5/18/2014 THE BUSINESS SCHOOL, KASHMIR UNIVERSITY 5/18/2014 Umer Rashid Roll No 55 Umer Rashid Roll No 55 Abstract: Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. CRM: In today’s competitive scenario in corporate world, “Customer Retention” strategy in Customer Relationship Management (CRM) is an increasingly pressed issue. Data mining techniques play a vital role in better CRM. This paper attempts to bring a new perspective by focusing the issue of data mining Applications, opportunities and challenges in CRM. It covers the topic such as customer retention, customer services, risk assessment, fraud detection and some of the data mining tools which are widely used in CRM. Supply Chain Management (SCM) environments are often dynamic markets providing a plethora of Information, either complete or incomplete. It is, therefore, evident that such environments demand...
Words: 2588 - Pages: 11
...Report – Webcast 8/13/14 on Data Mining SAS (Statistical Analysis System) was originally developed as a project to analyze agriculture from 1966-1976 at North Carolina State University. As demand for such software grew, SAS Institute was founded in 1976. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and they provide more advanced options through the SAS programming language. On August 13 2014, SAS sponsored a web seminar titled “Analytically Speaking” the topic of the webcast was data mining techniques. Michael Berry and Gordon Linoff were the featured speakers, they have written a leading introductory book (on data mining) titled “Data Mining Techniques”. They discussed a lot of the current data mining landscape, including new methods, new types of data and the importance of using the right analysis for your problem (as good analysis is wasted doing the wrong thing). They also briefly discussed using ‘found data’ – text data, social data and device data. Michael Berry is the Business Intelligence Director at TripAdvisor and co-founder of Data Miners Inc. Gordon Linoff is co-founder of Data Miners Inc. and a consultant to financial, media and pharmaceutical companies. Data mining is the analysis step of the “KDD” (Knowledge Discovery in Databases). Data mining is an interdisciplinary sub-field...
Words: 818 - Pages: 4
...Report – Webcast 8/13/14 on Data Mining SAS (Statistical Analysis System) was originally developed as a project to analyze agriculture from 1966-1976 at North Carolina State University. As demand for such software grew, SAS Institute was founded in 1976. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and they provide more advanced options through the SAS programming language. On August 13 2014, SAS sponsored a web seminar titled “Analytically Speaking” the topic of the webcast was data mining techniques. Michael Berry and Gordon Linoff were the featured speakers, they have written a leading introductory book (on data mining) titled “Data Mining Techniques”. They discussed a lot of the current data mining landscape, including new methods, new types of data and the importance of using the right analysis for your problem (as good analysis is wasted doing the wrong thing). They also briefly discussed using ‘found data’ – text data, social data and device data. Michael Berry is the Business Intelligence Director at TripAdvisor and co-founder of Data Miners Inc. Gordon Linoff is co-founder of Data Miners Inc. and a consultant to financial, media and pharmaceutical companies. Data mining is the analysis step of the “KDD” (Knowledge Discovery in Databases). Data mining is an interdisciplinary sub-field...
Words: 818 - Pages: 4
...FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Management Information Systems CHAPTER 6: FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Learning Objectives • Describe how the problems of managing data resources in a traditional file environment are solved by a database management system • Describe the capabilities and value of a database management system • Apply important database design principles • Evaluate tools and technologies for accessing information from databases to improve business performance and decision making • Assess the role of information policy, data administration, and data quality assurance in the management of a firm’s data resources 2 © Pearson Education 2012 Management Information Systems CHAPTER 6: FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Organizing Data in a Traditional File Environment • File organization concepts – – – – Database: Group of related files File: Group of records of same type Record: Group of related fields Field: Group of characters as word(s) or number • Describes an entity (person, place, thing on which we store information) • Attribute: Each characteristic, or quality, describing entity – E.g., Attributes Date or Grade belong to entity COURSE 3 © Pearson Education 2012 Management Information Systems CHAPTER 6: FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Organizing Data in a Traditional...
Words: 2709 - Pages: 11
...Business Intelligence – According to CIO, “Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting.” Verizon’s Business Intelligence plays an import role in customer’s experience of the company. It also enhances the customer service experience, which IT processes in place to capture front-line feedback from customers at all touch points and turn that feedback into real, business-impacting changes. IT plays a critical role in a company’s success when it is aligned with business’ overarching goals. Enterprise resource planning – Enterprise resource planning is a software which attempts to integrate all departments and functions across a company onto a single computer system that can serve all those different departments’ particular needs. Verizon is not a name readily associated with top ERP vendors, but may change as a result of new SAP partnership. The conversion just happened two months ago and we are still detailing with the changes and problems that resulted in conversion. Customer Relationship Management – CRM (Customer Relationship Management) refers to the methodologies and tools that help businesses manage customer relationships in an organized way. CRM differs for the small and big companies. Personally, I do feel that it is good software for...
Words: 418 - Pages: 2
...cu MOVEIN BUSINESS INTELLIGENCE AND ANALYTICS REPORT me nt ap A Business Plan on the Role of Business Intelligence and Analytics for MoveIn Pty Ltd Th ink sw Do TABLE OF CONTENTS Executive Summary ........................................................................................................................ 2 1 -‐ Introduction .............................................................................................................................. 3 2 -‐ Role of Business Intelligence ..................................................................................................... 3 2.1 -‐ Business Intelligence -‐ Overview ............................................................................................... 3 2.2 -‐ Business Intelligence Tools ........................................................................................................ 4 2.2.1 -‐ On-‐line Analytical Processing .............................................................................................. 4 2.2.2 -‐ Data Mining ........................................................................................................................ 5 ...
Words: 4854 - Pages: 20
...Informação. Data Warehouse, SQL Server Business Intelligence Development Studio. Conceitos de CRM e Data Mining. Tabelas Dinâmicas no MS Excel. 417 slides. Sistemas de Informação Ricardo Campos (ricardo.campos@ipt.pt) © Ricardo Campos [ h t t p : / / w w w . c c c . i p t . p t / ~ r i c a r d o ] Sistemas de Informação Autoria Esta apresentação foi desenvolvida por Ricardo Campos, docente do Instituto Politécnico de Tomar. Encontra-se disponível na página web do autor no link Publications ao abrigo da seguinte licença: Mais detalhes em: http://creativecommons.org/licenses/by-nc/3.0/deed.pt O seu uso, de parte ou da totalidade, pressupõe a utilização da seguinte referência: Campos, Ricardo. (2008). Apresentação de Sistemas de Informação. Data Warehouse, SQL Server Business Intelligence Development Studio. Conceitos de CRM e Data Mining. Tabelas Dinâmicas no MS Excel. 417 slides. A sua disponibilização em formato PPT pode ser feita mediante solicitação (email: ricardo.campos@ipt.pt) © Ricardo Campos [ h t t p : / / w w w . c c c . i p t . p t / ~ r i c a r d o ] Sistemas de Informação Ricardo Campos [http://www.ccc.ipt.pt/~ricardo/] 1 Campos, Ricardo. (2008). Apresentação de Sistemas de Informação. Data Warehouse, SQL Server Business Intelligence Development Studio. Conceitos de CRM e Data Mining. Tabelas Dinâmicas no MS Excel. 417 slides. Bibliografia Recursos: Ralph Kimball, Laura Reeves, Margy Ross, Warren Thornthwaite The Data Warehouse Lifecycle...
Words: 35397 - Pages: 142
...Data mining is an iterative process of selecting, exploring and modeling large amounts of data to identify meaningful, logical patterns and relationships among key variables. Data mining is used to uncover trends, predict future events and assess the merits of various courses of action. When employing, predictive analytics and data mining can make marketing more efficient. There are many techniques and methods, including business intelligence data collection. Predictive analytics is using business intelligence data for forecasting and modeling. It is a way to use predictive analysis data to predict future patterns. It is used widely in the insurance, medical and credit industries. Assessment of credit, and assignment of a credit score is probably the most widely known use of predictive analytics. Using events of the past, managers are able to estimate the likelihood of future events. Data mining aids predictive analysis by providing a record of the past that can be analyzed and used to predict which customers are most likely to renew, purchase, or purchase related products and services. Business intelligence data mining is important to your marketing campaigns. Proper data mining algorithms and predictive modeling can narrow your target audience and allow you to tailor your ads to each online customer as he or she navigates your site. Your marketing team will have the opportunity to develop multiple advertisements based on the past clicks of your visitors. Predictive...
Words: 1136 - Pages: 5
...Chapter 12 Business Intelligence and Decision Support Systems Goals of the Chapter The primary objective of this chapter is to recognize the importance of data, the management issues that relate to it, and its life cycle. Other objectives include relating data management to multimedia and document management, explaining the concept of data warehousing, data mining, analytical processing, and knowledge discovery management. An Overview Section 12.1 – The Need for Business Intelligence – The section serves as an overview of Business Intelligence and its use in business. It discusses the problems associated with disparate data stores where data are not integrated into a single reporting system. The section discusses the technologies involved in Business Intelligence and the vendors involved. It also talks about predictive analytics, alerts and decision support. Section 12.2 – BI Architecture, Reporting and Performance Management – This section discusses the modes of data extraction and integration into a standardized, usable and trustworthy one. It also discusses the different types of reporting systems available to organizations, data mining, query and analysis. The section provides an insight into Business Performance Management (BPM) as a way for business managers to know if their organizations are achieving their strategic goals Section 12.3 – Data, Text and Web Mining and BI Search – This section discusses data mining technology, tools, and techniques. Information types...
Words: 5712 - Pages: 23
...computing, middleware, and industry standards as relating to the enterprise data repository. Data warehousing, data mining, and data marts are covered from an enterprise perspective. Policies Faculty and students will be held responsible for understanding and adhering to all policies contained within the following two documents: • • University policies: You must be logged into the student website to view this document. Instructor policies: This document is posted in the Course Materials forum. University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently changed modalities, read the policies governing your current class modality. Course Materials Coronel, C., Morris, S., & Rob, P. (2011). Database systems: Design, implementation and management (9th ed.). Mason, OH: Cengage Learning. Eckerson, W. W. (2011). Performance dashboards: Measuring, monitoring, and managing your business (2nd ed.). Hoboken, NJ: John Wiley & Sons, Inc. Hoffer, J. A., Ramesh, V., & Topi, H. (2011). Modern database management (10th ed.). Upper Saddle River, NJ: Pearson. Linoff, G. S., & Berry, M. J. A. (2011). Data mining techniques: For marketing, sales, and customer relationship management (3rd ed.). Indianapolis, IN: Wiley Publishing, Inc. Ponniah, P. (2010). Data warehousing: Fundamentals for IT professionals (2nd ed.). Hoboken, NJ:...
Words: 2603 - Pages: 11
...in well-designed data stores coupled with business friendly software tool that provide knowledge workers timely access, effective analysis and intuitive presentation of the right information, enabling them to take right actions or make decisions". White (2005) it defined BIS as information systems that provide information and improve its quality that supports decision making and achieves business goals. It divided BIS into two parts: 1) data warehouse 2) access to data, data analysis and reporting. KalKaota &Robinson, (1999) business intelligence systems infrastructure components that support the quality of decision making: 1. Key information technology related to store data (Extraction, transforming...
Words: 1108 - Pages: 5
...1. BI definition and how it adds value to business Business intelligence is a set of theories, methodologies, processes, architectures and technologies that transform row data into meaningful information for business processes. The most important functions of BI are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. BI can be applied in the following business processes, in order to add business value: * Measurement - create hierarchy of performance metrics in order to inform managers about the progress toward the goals * Analytics - build quantitative processes for a business to arrive at knowledge discovery * Reporting - build the infrastructure for strategic reporting * Knowledge management - identifies, creates, represents and distributes insights that are true business knowledge. Who uses BI? Business intelligence is used by decision makers throughout the firm. At senior managerial levels, it is the input to strategic and tactical decisions. At lower managerial levels, it helps individuals to do their day-to-day job. According to Gartner, BI supports strategic decision making in the following areas: * Corporate performance management * Customer relationship optimization, business activity monitoring, traditional decision support * Supporting of BI applications for...
Words: 1365 - Pages: 6
...(a) What is Data Mining? What is OLAP? How is data mining different from OLAP? Data mining: Data mining is essential utilized today by organizations with an in number buyer center retail, budgetary, correspondence, and promoting associations. It empowers these organizations to figure out relationships around "interior" components, for example, value, item positioning, or staff abilities, and "outer" variables, for example, financial pointers, rivalry, and client demographics. Also, it empowers them to figure out the effect on deals, client fulfillment, and corporate benefits. At long last, it empowers them to "penetrate down" into synopsis data to view part transactional information. OLAP: OLAP stays for Online Analytical Processing and is designing used to accumulate, regulate and process multidimensional data and outfit brisk access to this data for demonstrative purposes. OLAP is for the most part used inside business reporting for promoting, deals, human possession organization and diverse business fields. OLAP mulls over brisk execution of complex database requests continuously. OLAP energizes complex data sees through data turning, complex data transforming, and data showing. OLAP manages dimensional information, which takes into account much quicker execution of complex database inquiries contrasted with social database administration frameworks. The information structure that OLAP make from the social information is called OLAP block. OLAP solid shapes might be...
Words: 671 - Pages: 3
