Advertisement
| business intelligence systems examples: Real-time Strategy and Business Intelligence Marko Kohtamäki, 2017-07-05 This book discusses and conceptualizes practices on real-time strategy, focusing on the interplay between strategy and business intelligence. Combining strategic practices and business intelligence systems, the authors demonstrate how managerial practices can be developed in the age of digitization. Also developing the concept of strategic agility, the book provides perspectives from a range of disciplines including strategic practices and decision making, customer relationship management, human resource management, competitive intelligence, supplier network management and business intelligence systems. Presenting managerial frameworks and guidelines, Real-time Strategy and Business Intelligence explores how to improve utilization of business intelligence systems in real-time decision making. Providing practical and future-oriented insights backed by examples and best practices, the authors present a clearly conceptualized theoretical framework. |
| business intelligence systems examples: Effective Business Intelligence Systems Robert J. Thierauf, 2001-06-30 One step above knowledge management systems are business intelligence systems. Their purpose is to give decision makers a better understanding of their organization's operations, and thus another way to outmaneuver the competition, by helping to find and extract the meaningful relationships, trends, and correlations that underlie the organization's operations and ultimately contribute to its success. Thierauf also shows that by tying critical success factors and key performance indicators into business intelligence systems, an organization's most important financial ratios can also be improved. Comprehensive and readable, Thierauf's book will advance the knowledge and skills of all information systems providers and users. It will also be useful as a text in upper-level courses covering a wide range of topics essential to an understanding of executive business systems generally, and specifically their creation and management. The theme underlying Thierauf's unique text is that a thorough understanding of a company's operations is crucial if the company is to be moved to a higher level of competitive advantage. Although data warehousing, data mining, the Internet, the World Wide Web, and other electronic aids have been in place for at least a decade, it is the remarkable and unique capability of business intelligence systems to utilize them that has in turn revolutionized the ability of decision makers to find, accumulate, organize, and access a wider range of information than was ever before possible. Effective business intelligence systems give decision makers a means to keep their fingers on the pulse of their businesses every step of the way. From this it follows that they are thus able to develop new, more workable means to cope with the competition successfully. Comprehensive and readable, Thierauf's book will advance the knowledge and skills of all information systems providers and users. It will also be useful as a text in upper-level courses covering a wide range of topics essential to an understanding of executive business systems generally, and specifically their creation and management. |
| business intelligence systems examples: Business Intelligence Carlo Vercellis, 2011-08-10 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. |
| business intelligence systems examples: Data Virtualization for Business Intelligence Systems Rick van der Lans, 2012-07-25 Annotation In this book, Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. |
| business intelligence systems examples: Business Intelligence Jerzy Surma, 2011-03-06 This book is about using business intelligence as a management information system for supporting managerial decision making. It concentrates primarily on practical business issues and demonstrates how to apply data warehousing and data analytics to support business decision making. This book progresses through a logical sequence, starting with data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery, and finally the actual use of discovered knowledge. All examples are based on the most recent achievements in business intelligence. Finally this book outlines an overview of a methodology that takes into account the complexity of developing applications in an integrated business intelligence environment. This book is written for managers, business consultants, and undergraduate and postgraduates students in business administration. |
| business intelligence systems examples: Business Intelligence Guidebook Rick Sherman, 2014-11-04 Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources. |
| business intelligence systems examples: Decision Support Systems for Business Intelligence Vicki L. Sauter, 2014-08-21 Praise for the First Edition This is the most usable decision support systems text. [i]t is far better than any other text in the field —Computing Reviews Computer-based systems known as decision support systems (DSS) play a vital role in helping professionals across various fields of practice understand what information is needed, when it is needed, and in what form in order to make smart and valuable business decisions. Providing a unique combination of theory, applications, and technology, Decision Support Systems for Business Intelligence, Second Edition supplies readers with the hands-on approach that is needed to understand the implications of theory to DSS design as well as the skills needed to construct a DSS. This new edition reflects numerous advances in the field as well as the latest related technological developments. By addressing all topics on three levels—general theory, implications for DSS design, and code development—the author presents an integrated analysis of what every DSS designer needs to know. This Second Edition features: Expanded coverage of data mining with new examples Newly added discussion of business intelligence and transnational corporations Discussion of the increased capabilities of databases and the significant growth of user interfaces and models Emphasis on analytics to encourage DSS builders to utilize sufficient modeling support in their systems A thoroughly updated section on data warehousing including architecture, data adjustment, and data scrubbing Explanations and implications of DSS differences across cultures and the challenges associated with transnational systems Each chapter discusses various aspects of DSS that exist in real-world applications, and one main example of a DSS to facilitate car purchases is used throughout the entire book. Screenshots from JavaScript® and Adobe® ColdFusion are presented to demonstrate the use of popular software packages that carry out the discussed techniques, and a related Web site houses all of the book's figures along with demo versions of decision support packages, additional examples, and links to developments in the field. Decision Support Systems for Business Intelligence, Second Edition is an excellent book for courses on information systems, decision support systems, and data mining at the advanced undergraduate and graduate levels. It also serves as a practical reference for professionals working in the fields of business, statistics, engineering, and computer technology. |
| business intelligence systems examples: Business Intelligence Tools for Small Companies Albert Nogués, Juan Valladares, 2017-05-25 Learn how to transition from Excel-based business intelligence (BI) analysis to enterprise stacks of open-source BI tools. Select and implement the best free and freemium open-source BI tools for your company’s needs and design, implement, and integrate BI automation across the full stack using agile methodologies. Business Intelligence Tools for Small Companies provides hands-on demonstrations of open-source tools suitable for the BI requirements of small businesses. The authors draw on their deep experience as BI consultants, developers, and administrators to guide you through the extract-transform-load/data warehousing (ETL/DWH) sequence of extracting data from an enterprise resource planning (ERP) database freely available on the Internet, transforming the data, manipulating them, and loading them into a relational database. The authors demonstrate how to extract, report, and dashboard key performance indicators (KPIs) in a visually appealing format from the relational database management system (RDBMS). They model the selection and implementation of free and freemium tools such as Pentaho Data Integrator and Talend for ELT, Oracle XE and MySQL/MariaDB for RDBMS, and Qliksense, Power BI, and MicroStrategy Desktop for reporting. This richly illustrated guide models the deployment of a small company BI stack on an inexpensive cloud platform such as AWS. What You'll Learn You will learn how to manage, integrate, and automate the processes of BI by selecting and implementing tools to: Implement and manage the business intelligence/data warehousing (BI/DWH) infrastructure Extract data from any enterprise resource planning (ERP) tool Process and integrate BI data using open-source extract-transform-load (ETL) tools Query, report, and analyze BI data using open-source visualization and dashboard tools Use a MOLAP tool to define next year's budget, integrating real data with target scenarios Deploy BI solutions and big data experiments inexpensively on cloud platforms Who This Book Is For Engineers, DBAs, analysts, consultants, and managers at small companies with limited resources but whose BI requirements have outgrown the limitations of Excel spreadsheets; personnel in mid-sized companies with established BI systems who are exploring technological updates and more cost-efficient solutions |
| business intelligence systems examples: Data Science and Its Applications Aakanksha Sharaff, G R Sinha, 2021-08-18 The term data being mostly used, experimented, analyzed, and researched, Data Science and its Applications finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications. |
| business intelligence systems examples: Handbook of Research on Applied AI for International Business and Marketing Applications Christiansen, Bryan, Škrinjari?, Tihana, 2020-09-25 Artificial intelligence (AI) describes machines/computers that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. As businesses have evolved to include more automation of processes, it has become more vital to understand AI and its various applications. Additionally, it is important for workers in the marketing industry to understand how to coincide with and utilize these techniques to enhance and make their work more efficient. The Handbook of Research on Applied AI for International Business and Marketing Applications is a critical scholarly publication that provides comprehensive research on artificial intelligence applications within the context of international business. Highlighting a wide range of topics such as diversification, risk management, and artificial intelligence, this book is ideal for marketers, business professionals, academicians, practitioners, researchers, and students. |
| business intelligence systems examples: Data Virtualization for Business Intelligence Systems Rick van der Lans, 2012-07-25 Data virtualization can help you accomplish your goals with more flexibility and agility. Learn what it is and how and why it should be used with Data Virtualization for Business Intelligence Systems. In this book, expert author Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. You'll learn the difference is between this new form of data integration and older forms, such as ETL and replication, and gain a clear understanding of how data virtualization really works. Data Virtualization for Business Intelligence Systems outlines the advantages and disadvantages of data virtualization and illustrates how data virtualization should be applied in data warehouse environments. You'll come away with a comprehensive understanding of how data virtualization will make data warehouse environments more flexible and how it make developing operational BI applications easier. Van der Lans also describes the relationship between data virtualization and related topics, such as master data management, governance, and information management, so you come away with a big-picture understanding as well as all the practical know-how you need to virtualize your data. - First independent book on data virtualization that explains in a product-independent way how data virtualization technology works. - Illustrates concepts using examples developed with commercially available products. - Shows you how to solve common data integration challenges such as data quality, system interference, and overall performance by following practical guidelines on using data virtualization. - Apply data virtualization right away with three chapters full of practical implementation guidance. - Understand the big picture of data virtualization and its relationship with data governance and information management. |
| business intelligence systems examples: Fundamentals of Business Intelligence Wilfried Grossmann, Stefanie Rinderle-Ma, 2015-06-02 This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided. The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples. |
| business intelligence systems examples: The Profit Impact of Business Intelligence Steve Williams, Nancy Williams, 2010-07-27 The Profit Impact of Business Intelligence presents an A-to-Z approach for getting the most business intelligence (BI) from a company's data assets or data warehouse. BI is not just a technology or methodology, it is a powerful new management approach that – when done right – can deliver knowledge, efficiency, better decisions, and profit to almost any organization that uses it. When BI first came on the scene, it promised a lot but often failed to deliver. The missing element was the business-centric focus explained in this book. It shows how you can achieve the promise of BI by connecting it to your organization's strategic goals, culture, and strengths while correcting your BI weaknesses. It provides a practical, process-oriented guide to achieve the full promise of BI; shows how world-class companies used BI to become leaders in their industries; helps senior business and IT executives understand the strategic impact of BI and how they can ensure a strong payoff from their BI investments; and identifies the most common mistakes organizations make in implementing BI. The book also includes a helpful glossary of BI terms; a BI readiness assessment for your organization; and Web links and extensive references for more information. - A practical, process-oriented book that will help organizations realize the promise of BI - Written by Nancy and Steve Williams, veteran consultants and instructors with hands-on, in the trenches experience in government and corporate business intelligence applications - Will help senior business and IT executives understand the strategic impact of BI and how they can help ensure a strong payoff on BI investments |
| business intelligence systems examples: Business Intelligence and Performance Management Peter Rausch, Alaa F. Sheta, Aladdin Ayesh, 2013-02-15 During the 21st century business environments have become more complex and dynamic than ever before. Companies operate in a world of change influenced by globalisation, volatile markets, legal changes and technical progress. As a result, they have to handle growing volumes of data and therefore require fast storage, reliable data access, intelligent retrieval of information and automated decision-making mechanisms, all provided at the highest level of service quality. Successful enterprises are aware of these challenges and efficiently respond to the dynamic environment in which their business operates. Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges and provide techniques to enable effective business change. The important aspects of both topics are discussed within this state-of-the-art volume. It covers the strategic support, business applications, methodologies and technologies from the field, and explores the benefits, issues and challenges of each. Issues are analysed from many different perspectives, ranging from strategic management to data technologies, and the different subjects are complimented and illustrated by numerous examples of industrial applications. Contributions are authored by leading academics and practitioners representing various universities, research centres and companies worldwide. Their experience covers multiple disciplines and industries, including finance, construction, logistics, and public services, amongst others. Business Intelligence and Performance Management is a valuable source of reference for graduates approaching MSc or PhD programs and for professionals in industry researching in the fields of BI and PM for industrial application. |
| business intelligence systems examples: Business Intelligence and Analytics Ramesh Sharda, Efraim Turban, Dursun Delen, 2014-02-28 Decision Support and Business Intelligence Systems provides the only comprehensive, up-to-date guide to today's revolutionary management support system technologies, and showcases how they can be used for better decision-making. The 10th edition focuses on Business Intelligence (BI) and analytics for enterprise decision support in a more streamlined book. |
| business intelligence systems examples: Smart Intelligent Computing and Applications Suresh Chandra Satapathy, Vikrant Bhateja, Swagatam Das, 2018-11-04 The proceedings covers advanced and multi-disciplinary research on design of smart computing and informatics. The theme of the book broadly focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solution to varied problems in society, environment and industries. The volume publishes quality work pertaining to the scope of the conference which is extended towards deployment of emerging computational and knowledge transfer approaches, optimizing solutions in varied disciplines of science, technology and healthcare. |
| business intelligence systems examples: Adaptive Business Intelligence Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, Constantin Chiriac, 2006-12-02 Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address fundamental questions: What is likely to happen in the future? What is the best course of action? Adaptive Business Intelligence explores elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The book explains the application of numerous prediction and optimization techniques, and shows how these concepts can be used to develop adaptive systems. Coverage includes linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling. |
| business intelligence systems examples: Artificial Intelligence in Practice Bernard Marr, 2019-04-15 Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce. |
| business intelligence systems examples: Agile Analytics Ken Collier, 2012 Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier's techniques offer optimal value whether your projects involve back-end data management, front-end business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right now--whether you're an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results--and have fun along the way. |
| business intelligence systems examples: Successful Business Intelligence: Secrets to Making BI a Killer App Cindi Howson, 2007-12-17 Praise for Successful Business Intelligence If you want to be an analytical competitor, you've got to go well beyond business intelligence technology. Cindi Howson has wrapped up the needed advice on technology, organization, strategy, and even culture in a neat package. It's required reading for quantitatively oriented strategists and the technologists who support them. --Thomas H. Davenport, President's Distinguished Professor, Babson College and co-author, Competing on Analytics When used strategically, business intelligence can help companies transform their organization to be more agile, more competitive, and more profitable. Successful Business Intelligence offers valuable guidance for companies looking to embark upon their first BI project as well as those hoping to maximize their current deployments. --John Schwarz, CEO, Business Objects A thoughtful, clearly written, and carefully researched examination of all facets of business intelligence that your organization needs to know to run its business more intelligently and exploit information to its fullest extent. --Wayne Eckerson, Director, TDWI Research Using real-world examples, Cindi Howson shows you how to use business intelligence to improve the performance, and the quality, of your company. --Bill Baker, Distinguished Engineer & GM, Business Intelligence Applications, Microsoft Corporation This book outlines the key steps to make BI an integral part of your company's culture and demonstrates how your company can use BI as a competitive differentiator. --Robert VanHees, CFO, Corporate Express Given the trend to expand the business analytics user base, organizations are faced with a number of challenges that affect the success rate of these projects. This insightful book provides practical advice on improving that success rate. --Dan Vesset, Vice President, Business Analytics Solution Research, IDC |
| business intelligence systems examples: Business Intelligence Success Factors Olivia Parr Rud, 2009-06-02 Over the last few decades, the growth of Business Intelligence has enabled companies to streamline many processes and expand into new markets on an unprecedented scale. New BI technologies are also enabling mass collaboration and innovation. However, implementation of these BI solutions often gives rise to new challenges. Business Intelligence Success Factors shows you how to turn those challenges into opportunities by mastering five key skills. Olivia Parr Rud shares insights gained from her two decades of experience in Business Intelligence to offer the latest practices that are emerging in organizational development. Written to help enhance your understanding of the current business climate and to provide the tools necessary to thrive in this new global economy, Business Intelligence Success Factors examines the components of chaos theory, complex adaptive systems, quantum physics, and evolutionary biology. A scientific framework for these new corporate issues helps explain why developing these key competencies are critical, given the speed of change, globalization, as well as advancements in technology and Business Intelligence. Divided into four cohesive parts, Business Intelligence Success Factors explores: The current business landscape as well as the latest scientific research: today's business realities and how and why they can lead to chaos New scientific models for viewing the global economy The five essential competencies—Communication, Collaboration, Innovation, Adaptability, and Leadership—that improve an organization's ability to leverage the new opportunities in a volatile global economy Profiles of several amazing leaders who are working to make a difference Cutting-edge research and case studies via invited contributors offering a wealth of knowledge and experience Move beyond mere survival to realize breakaway success in the global economy with the practical guidance found in Business Intelligence Success Factors. |
| business intelligence systems examples: E-Business Intelligence Bernard Liautaud, 2001 Publisher Fact Sheet How to leverage corporate information for reduced costs & increased profits. |
| business intelligence systems examples: Data-Driven Business Intelligence Systems for Socio-Technical Organizations Keikhosrokiani, Pantea, 2024-04-09 The convergence of modern technology and social dynamics have shaped the very fabric of today’s organizations, making the role of Business Intelligence (BI) profoundly significant. Data-Driven Business Intelligence Systems for Socio-Technical Organizations delves into the heart of this transformative realm, offering an academic exploration of the tools, strategies, and methodologies that propel enterprises toward data-driven decision-making excellence. Socio-technical organizations, with their intricate interplay between human and technological components, require a unique approach to BI. This book embarks on a comprehensive journey, revealing how BI tools empower these entities to decipher the complexities of their data landscape. From user behavior to social interactions, technological systems to environmental factors, this work sheds light on the multifaceted sources of information that inform organizational strategies. Decision-makers within socio-technical organizations leverage BI insights to discern patterns, spot trends, and uncover correlations that influence operations and the intricate social dynamics within their entities. Research covering real-time monitoring and predictive analytics equips these organizations to respond swiftly to demands and anticipate future trends, harnessing the full potential of data. The book delves into their design, development, and architectural nuances, illuminating these concepts through case studies. This book is ideal for business executives, entrepreneurs, data analysts, marketers, government officials, educators, and researchers. |
| business intelligence systems examples: Business Analytics for Managers Gert Laursen, Jesper Thorlund, 2010-07-13 While business analytics sounds like a complex subject, this book provides a clear and non-intimidating overview of the topic. Following its advice will ensure that your organization knows the analytics it needs to succeed, and uses them in the service of key strategies and business processes. You too can go beyond reporting!—Thomas H. Davenport, President's Distinguished Professor of IT and Management, Babson College; coauthor, Analytics at Work: Smarter Decisions, Better Results Deliver the right decision support to the right people at the right time Filled with examples and forward-thinking guidance from renowned BA leaders Gert Laursen and Jesper Thorlund, Business Analytics for Managers offers powerful techniques for making increasingly advanced use of information in order to survive any market conditions. Take a look inside and find: Proven guidance on developing an information strategy Tips for supporting your company's ability to innovate in the future by using analytics Practical insights for planning and implementing BA How to use information as a strategic asset Why BA is the next stepping-stone for companies in the information age today Discussion on BA's ever-increasing role Improve your business's decision making. Align your business processes with your business's objectives. Drive your company into a prosperous future. Taking BA from buzzword to enormous value-maker, Business Analytics for Managers helps you do it all with workable solutions that will add tremendous value to your business. |
| business intelligence systems examples: Business Intelligence: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2015-12-29 Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Business Intelligence: Concepts, Methodologies, Tools, and Applications presents a comprehensive examination of business data analytics along with case studies and practical applications for businesses in a variety of fields and corporate arenas. Focusing on topics and issues such as critical success factors, technology adaptation, agile development approaches, fuzzy logic tools, and best practices in business process management, this multivolume reference is of particular use to business analysts, investors, corporate managers, and entrepreneurs in a variety of prominent industries. |
| business intelligence systems examples: Cyclopaedia of Commercial and Business Anecdotes Richard Miller Devens, 1865 |
| business intelligence systems examples: Business Intelligence, Reprint Edition Stacia Misner, Michael Luckevich, Elizabeth Vitt, 2008-12-10 “This readable, practical book helps business people quickly understand what business intelligence is, how it works, where it's used, and why and when to use it—all illustrated by real case studies, not just theory.” Nigel Pendse Author of The OLAP Report www.olapreport.com So much information, so little time. All too often, business data is hard to get at and use—thus slowing decision-making to a crawl. This insightful book illustrates how organizations can make better, faster decisions about their customers, partners, and operations by turning mountains of data into valuable business information that’s always at the fingertips of decision makers. You’ll learn what’s involved in using business intelligence to bring together information, people, and technology to create successful business strategies—and how to execute those strategies with confidence. Topics covered include: THE BUSINESS INTELLIGENCE MINDSET: Discover the basics behind business intelligence, such as how it’s defined, why and how to use it in your organization, and what characteristics, components, and general architecture most business intelligence solutions share. THE CASE FOR BUSINESS INTELLIGENCE: Read how world leaders in finance, manufacturing, and retail have successfully implemented business intelligence solutions and see what benefits they have reaped. THE PRACTICE OF BUSINESS INTELLIGENCE: Find out what’s involved in implementing a business intelligence solution in your organization, including how to identify your business intelligence opportunities, what decisions you must make to get a business intelligence project going, and what to do to sustain the momentum so that you can continue to make sense of all the data you gather. |
| business intelligence systems examples: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
| business intelligence systems examples: Integration of Data Mining in Business Intelligence Systems Azevedo, Ana, 2014-09-30 Uncovering and analyzing data associated with the current business environment is essential in maintaining a competitive edge. As such, making informed decisions based on this data is crucial to managers across industries. Integration of Data Mining in Business Intelligence Systems investigates the incorporation of data mining into business technologies used in the decision making process. Emphasizing cutting-edge research and relevant concepts in data discovery and analysis, this book is a comprehensive reference source for policymakers, academicians, researchers, students, technology developers, and professionals interested in the application of data mining techniques and practices in business information systems. |
| business intelligence systems examples: Enterprise Resource Planning and Business Intelligence Systems for Information Quality Carlo Caserio, Sara Trucco, 2018-04-13 This book analyses the role of Enterprise Resource Planning (ERP) and Business Intelligence (BI) systems in improving information quality through an empirical analysis carried out in Italy. The study begins with a detailed examination of ERP features that highlights the advantages and disadvantages of ERP adoption. Critical success factors for ERP implementation and post-implementation are then discussed, along with the capabilities of ERP in driving the alignment between management accounting and financial accounting information.The study goes on to illustrate the features of BI systems and to summarize companies’ needs for BI. Critical success factors for BI implementation are then presented, along with the BI maturity model and lifecycle. The focus of the research entails a detailed empirical analysis in the Italian setting designed to investigate the role played by ERP and BI systems in reducing information overload/underload and improving information quality by influencing the features of information flow. The practical and theoretical implications of the study are discussed and future avenues of research are suggested. This book will be of value for all those who have an interest in the capacities of ERP and BI systems to enhance business information quality. |
| business intelligence systems examples: Predictive Business Analytics Lawrence Maisel, Gary Cokins, 2013-09-26 Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions. |
| business intelligence systems examples: Implementing Information Technology Governance: Models, Practices and Cases Van Grembergen, Wim, De Haes, Steven, 2007-09-30 In many organizations, information technology (IT) has become crucial in the support, sustainability, and growth of the business. This pervasive use of technology has created a critical dependency on IT that calls for a specific focus on IT governance. Implementing Information Technology Governance: Models, Practices and Cases presents insight gained through literature reviews and case studies to provide practical guidance for organizations who want to start implementing IT governance or improving existing governance models, and provides a detailed set of IT governance structures, processes, and relational mechanisms that can be leveraged to implement IT governance in practice. |
| business intelligence systems examples: Business Intelligence For Dummies Swain Scheps, 2011-02-04 You're intelligent, right? So you've already figured out that Business Intelligence can be pretty valuable in making the right decisions about your business. But you’ve heard at least a dozen definitions of what it is, and heard of at least that many BI tools. Where do you start? Business Intelligence For Dummies makes BI understandable! It takes you step by step through the technologies and the alphabet soup, so you can choose the right technology and implement a successful BI environment. You'll see how the applications and technologies work together to access, analyze, and present data that you can use to make better decisions about your products, customers, competitors, and more. You’ll find out how to: Understand the principles and practical elements of BI Determine what your business needs Compare different approaches to BI Build a solid BI architecture and roadmap Design, develop, and deploy your BI plan Relate BI to data warehousing, ERP, CRM, and e-commerce Analyze emerging trends and developing BI tools to see what else may be useful Whether you’re the business owner or the person charged with developing and implementing a BI strategy, checking out Business Intelligence For Dummies is a good business decision. |
| business intelligence systems examples: Business Analytics Jay Liebowitz, 2013-12-19 Together, Big Data, high-performance computing, and complex environments create unprecedented opportunities for organizations to generate game-changing insights that are based on hard data. Business Analytics: An Introduction explains how to use business analytics to sort through an ever-increasing amount of data and improve the decision-making cap |
| business intelligence systems examples: Decision Support Systems and Intelligent Systems Efraim Turban, Jay E. Aronson, 1998 B> This book is widely known for its comprehensive treatment of decision support theory and how it is applied. Through four editions, this book has defined the course and set the standard for up-to-date coverage of the latest decision support theories and practices by managers and organizations. This fifth edition has been streamlined and updated throughout to reflect new computing technologies. Chapter 9 has been completely rewritten to focus on the Internet and Intranet. The reader will find expanded coverage of data warehousing, data mining, on-line analytical processes, and an entirely new chapter on intelligent agents (Ch. 19). Internet related topics and links to Internet exercises and cases appear throughout the new edition. |
| business intelligence systems examples: Applied Business Analytics Nathaniel Lin, 2015 Now that you've collected the data and crunched the numbers, what do you do with all this information? How do you take the fruit of your analytics labor and apply it to business decision making? How do you actually apply the information gleaned from quants and tech teams? Applied Business Analytics will help you find optimal answers to these questions, and bridge the gap between analytics and execution in your organization. Nathaniel Lin explains why analytics value chains often break due to organizational and cultural issues, and offers in the trenches guidance for overcoming these obstacles. You'll learn why a special breed of analytics deciders is indispensable for any organization that seeks to compete on analytics; how to become one of those deciders; and how to identify, foster, support, empower, and reward others who join you. Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at every level: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes: How analytical and conventional decision making differ -- and the challenging implications How to determine who your analytics deciders are, and ought to be Proven best practices for actually applying analytics to decision-making How to optimize your use of analytics as an analyst, manager, executive, or C-level officer |
| business intelligence systems examples: Business Intelligence and Analytics: Systems for Decision Support PDF eBook, Global Edition Efraim Turban, Ramesh Sharda, Dursun Delen, 2014-09-10 Appropriate for all courses in Decision Support Systems (DSS), computerised decision making tools, and management support systems. Decision Support and Business Intelligence Systems provides the only comprehensive, up-to-date guide to today's revolutionary management support system technologies, and showcases how they can be used for better decision-making. The 10th edition focuses on Business Intelligence (BI) and analytics for enterprise decision support in a more streamlined book. In addition to traditional decision support applications, this edition expands the reader’s understanding of the various types of analytics by providing examples, products, services, and exercises by discussing Web-related issues throughout the text. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. |
| business intelligence systems examples: Practical Business Analytics Using SAS Shailendra Kadre, Venkat Reddy Konasani, 2015-02-07 Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios. The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations. The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life. Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes: More than 200 examples and exercises, including code and datasets for practice. Relevant examples for all industries. Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course. Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before. |
| business intelligence systems examples: Business Intelligence Roadmap Larissa Terpeluk Moss, S. Atre, 2003 This software will enable the user to learn about business intelligence roadmap. |
| business intelligence systems examples: Integration Challenges for Analytics, Business Intelligence, and Data Mining Azevedo, Ana, Santos, Manuel Filipe, 2020-12-11 As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students. |
BUSINESS INTELLIGENCE - Xavier University
Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the …
BUSINESS INTELLIGENCE: CONCEPTS, COMPONENTS TOOLS, …
Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text …
The Definitive Guide do Business Intelligence The Definitive …
Here are a few examples of some of the ways business intelligence is defined: A variety of software applications used to analyze an organiza- tion’s raw data.
Ultimate Guide to Business Intelligence in The Enterprise
Enterprise BI systems bring a variety of uses, business benefits and challenges to organizations, as explained in this in-depth business intelligence guide.
Unit 14: Business Intelligence - City of London College
Systems that are used to manage, analyse and display business intelligence to support the decision-making process; the importance of reliable data; impacts of reliable data in businesses.
Approach to Building and Implementing Business Intelligence …
Abstract: The article aims at describing processes of building Business Intelligence (BI) systems. Taking the BI systems specifics into consideration, the authors present a suggested …
TYIT SEM VI BUSINESS INTELLIGENCE NOTES …
A business intelligence system provides decision makers with information and knowledge extracted from data, through the application of mathematical models and algorithms. In some …
Business Intelligence Definition and Solutions - Ohio
Business Intelligence topics covering definition, objectives, systems and solutions. What is business intelligence? Business intelligence, or BI, is an umbrella term that refers to a variety …
Developing and Leveraging Business Intelligence Systems for …
There are five styles of BI: data mining; OLAP analysis; data visualization; dashboards and enterprise reporting. BI styles enable users to quickly adapt to new business requirements and …
APPLICATION OF BUSINESS INTELLIGENCE SYSTEM IN …
The main tool in business analysis, which is the foundation of making business decisions during restructuring process, is the business intelligence system. Business intelligence systems …
Managerial Aspects of Business Intelligence Implementation
Current research addresses the managerial aspects of the BI and outlines the importance of the efficient cross-departmental information flow between all parties involved in the project, …
THE CURRENT STATUS OF BUSINES INTELLIGENCE: A …
We investigated existing reviews on business intelligence and critical success factors. We discussed those topics in order to extract the relevant terms and their relationships.
IFC Report on Business intelligence systems and central bank …
Central banks increasingly need to use business intelligence (BI) systems to collect, manage and analyse data in order to inform policy decisions. This report presents the results of a survey …
Business Intelligence Solution for an SME: A Case Study
In this paper, we examine the challenges such as lack of technical expertise and limited budget when implementing a BI solution within an SME in the UK. In light of our experiences in …
THE APPLICATION OF BUSINESS INTELLIGENCE SYSTEMS IN …
Abstract: The goal of the paper is to present the application of Business Intelligence systems belonging to the area of business analytics in the domain of logistics and particularly indicate …
12 Business Intelligence Systems - dbjournal.ro
technology to business modeling. Business intelligence represents a wide area of applications and technologies for collecting, storing, analyzing and providing access to information for …
The main purpose of business intelligence systems is to …
A business intelligence system provides decision makers with information and knowledge extracted from data, through the application of mathematical models and algorithms. In some …
Topics in Business Intelligence - SMAA.fi
What is Business Intelligence (BI)? BI refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or …
BUSINESS INTELLIGENCE: CONCEPTS, COMPONENTS, …
Companies have understood the importance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts. It describes the insights on …
Chapter 4: Business Intelligence Dimensions - Springer
Examples of business dimensions have been provided in several sources (Dresner, 2017; Skyrius & Nemitko, 2018; Vuori, 2007) that have indicated a set of important features serving as …
BUSINESS INTELLIGENCE - Xavier University
Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the …
BUSINESS INTELLIGENCE: CONCEPTS, COMPONENTS …
Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text …
The Definitive Guide do Business Intelligence The Definitive …
Here are a few examples of some of the ways business intelligence is defined: A variety of software applications used to analyze an organiza- tion’s raw data.
Ultimate Guide to Business Intelligence in The Enterprise
Enterprise BI systems bring a variety of uses, business benefits and challenges to organizations, as explained in this in-depth business intelligence guide.
Unit 14: Business Intelligence - City of London College
Systems that are used to manage, analyse and display business intelligence to support the decision-making process; the importance of reliable data; impacts of reliable data in businesses.
Approach to Building and Implementing Business Intelligence …
Abstract: The article aims at describing processes of building Business Intelligence (BI) systems. Taking the BI systems specifics into consideration, the authors present a suggested …
TYIT SEM VI BUSINESS INTELLIGENCE NOTES …
A business intelligence system provides decision makers with information and knowledge extracted from data, through the application of mathematical models and algorithms. In some …
Business Intelligence Definition and Solutions - Ohio
Business Intelligence topics covering definition, objectives, systems and solutions. What is business intelligence? Business intelligence, or BI, is an umbrella term that refers to a variety …
Developing and Leveraging Business Intelligence Systems …
There are five styles of BI: data mining; OLAP analysis; data visualization; dashboards and enterprise reporting. BI styles enable users to quickly adapt to new business requirements and …
APPLICATION OF BUSINESS INTELLIGENCE SYSTEM IN …
The main tool in business analysis, which is the foundation of making business decisions during restructuring process, is the business intelligence system. Business intelligence systems …
Managerial Aspects of Business Intelligence Implementation
Current research addresses the managerial aspects of the BI and outlines the importance of the efficient cross-departmental information flow between all parties involved in the project, …
THE CURRENT STATUS OF BUSINES INTELLIGENCE: A …
We investigated existing reviews on business intelligence and critical success factors. We discussed those topics in order to extract the relevant terms and their relationships.
IFC Report on Business intelligence systems and central …
Central banks increasingly need to use business intelligence (BI) systems to collect, manage and analyse data in order to inform policy decisions. This report presents the results of a survey …
Business Intelligence Solution for an SME: A Case Study
In this paper, we examine the challenges such as lack of technical expertise and limited budget when implementing a BI solution within an SME in the UK. In light of our experiences in …
12 Business Intelligence Systems - dbjournal.ro
technology to business modeling. Business intelligence represents a wide area of applications and technologies for collecting, storing, analyzing and providing access to information for …
THE APPLICATION OF BUSINESS INTELLIGENCE …
Abstract: The goal of the paper is to present the application of Business Intelligence systems belonging to the area of business analytics in the domain of logistics and particularly indicate …
The main purpose of business intelligence systems is to …
A business intelligence system provides decision makers with information and knowledge extracted from data, through the application of mathematical models and algorithms. In some …
Topics in Business Intelligence - SMAA.fi
What is Business Intelligence (BI)? BI refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or …
BUSINESS INTELLIGENCE: CONCEPTS, COMPONENTS, …
Companies have understood the importance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts. It describes the insights on …
Chapter 4: Business Intelligence Dimensions - Springer
Examples of business dimensions have been provided in several sources (Dresner, 2017; Skyrius & Nemitko, 2018; Vuori, 2007) that have indicated a set of important features serving as …
