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.

This book examines the managerial dimensions of business intelligence (BI) systems. It develops a set of guidelines for value creation by implementing business intelligence systems and technologies. In particular the book looks at BI as a process – driven by a mix of human and technological capabilities – to serve complex information needs in building insights and providing aid in decision making. After an introduction to the key concepts of BI and neighboring areas of information processing, the book looks at the complexity and multidimensionality of BI. It tackles both data integration and information integration issues. Bodies of knowledge and other widely accepted collections of experience are presented and turned into lessons learned. Following a straightforward introduction to the processes and technologies of BI the book embarks on BI maturity and agility, the components, drivers and inhibitors of BI culture and soft BI factors like attention, sense and trust. Eventually the book attempts to provide a holistic view on business intelligence, possible structures and tradeoffs and embarks to provide an outlook on possible developments in BI and analytics.

Market_Desc: Primary MarketEngineering (BE/BTech)/ME/MTech students who are interested to develop conceptual level subject knowledge with examples of industrial strength applications.Secondary MarketMCA/MBA/Business users/business analysts Special Features: · Foreword by Prof R Natarajan, Former Chairman, AICTE, Former Director, IIT Madras.· Excellent authorship.· Single source of introductory knowledge on business intelligence (BI).· Provides a good start for first-time learners typically from the engineering and management discipline.· Covers the complete life cycle of BI/Analytics Application development project.· Helps develop deeper understanding of the subject with an enterprise context, and discusses its application in businesses.· Explains concepts with the help of illustrations, application to real-life scenarios and provides opportunities to test understanding.· States the pre-requisites for each chapter and different reference sources available.· In addition the book also has the following pedagogical features:· Industrial application case studies.· Crossword puzzles/do it yourself exercises/assignments to help with self-assessment. The solutions to these have also been provided. · Glossary of terms.· References/web links/bibliography - generally at the end of every concept.CD Companion:To ensure that concepts can be practiced for deeper understanding at low cost, the book is accompanied with a CD containing:· Step-by-step Hands-On manual on:ü An open source tool, Pentaho Data Integrator (PDI) to explain the process of extraction of data from multiple varied sources.ü MS Excel to explain the concept of analysis.ü MS Access to generate reports on the analyzed data.· An integrated project that encompasses the complete life cycle of a BI project. About The Book: The book promises to be a single source of introductory knowledge on business intelligence which can be taught in one semester. It will provide a good start for first time learners typically from the engineering and management discipline. Business Intelligence subject cannot be studied in isolation. The book provides a holistic coverage beginning with an enterprise context, developing deeper understanding through the use of tools, touching a few domains where BI is embraced and discussing the problems that BI can help solve. It covers the complete life cycle of BI/Analytics project: Covering operational/transactional data sources, data transformation, data mart/warehouse design-build, analytical reporting, and dashboards. To ensure that concepts can be practiced for deeper understanding at low cost, the book is accompanied with step-by-step hands-on manual in the CD.

Going beyond the theoretical foundation, this step-by-step book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. --

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

This comprehensive and authoritative guide will teach you the DAX language for business intelligence, data modeling, and analytics. Leading Microsoft BI consultants Marco Russo and Alberto Ferrari help you master everything from table functions through advanced code and model optimization. You’ll learn exactly what happens under the hood when you run a DAX expression, how DAX behaves differently from other languages, and how to use this knowledge to write fast, robust code. If you want to leverage all of DAX’s remarkable power and flexibility, this no-compromise “deep dive” is exactly what you need. Perform powerful data analysis with DAX for Microsoft SQL Server Analysis Services, Excel, and Power BI Master core DAX concepts, including calculated columns, measures, and error handling Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions Perform time-based calculations: YTD, MTD, previous year, working days, and more Work with expanded tables, complex functions, and elaborate DAX expressions Perform calculations over hierarchies, including parent/child hierarchies Use DAX to express diverse and unusual relationships Measure DAX query performance with SQL Server Profiler and DAX Studio

There are a limited number of intelligence analysis books available on the market. Intelligence Analysis Fundamentals is an introductory, accessible text for college level undergraduate and graduate level courses. While the principles outlined in the book largely follow military intelligence terminology and practice, concepts are presented to correlate with intelligence gathering and analysis performed in law enforcement, homeland security, and corporate and business security roles. Most of the existing texts on intelligence gathering and analysis focus on specific types of intelligence such as ‘target centric’ intelligence, and many of these, detail information from a position of prior knowledge. In other words, they are most valuable to the consumer who has a working-level knowledge of the subject. The book is general enough in nature that a lay student—interested in pursuing a career in intelligence, Homeland Security, or other related areas of law enforcement—will benefit from it. No prior knowledge of intelligence analysis, functions, or operations is assumed. Chapters illustrate methods and techniques that, over the years, have consistently demonstrate results, superior to those achieved with other means. Chapters describe such analytical methods that are most widely used in the intelligence community and serve as recognized standards and benchmarks in the practice of intelligence analysis. All techniques have been selected for inclusion for their specific application to homeland security, criminal investigations, and intelligence operations. Uses numerous hands-on activities—that can easily be modified by instructors to be more or less challenging depending on the course level—to reinforce concepts As current and active members of the intelligence community, the authors draw on their decades of experience in intelligence to offer real-world examples to illustrate concepts All methodologies reflect the latest trends in the intelligence communities assessment, analysis, and reporting processes with all presented being open source, non-classified information As such, the non-sensitive information presented is appropriate—and methods applicable—for use for education and training overseas and internationally Military-style collection and analysis methods are the primary ones presented, but all are directly correlated intelligence to current concepts, functions and practices within Homeland Security and the law communities Covers the counterterrorism environment where joint operations and investigative efforts combine military, private sector, and law enforcement action and information sharing The book will be a welcome addition to the body of literature available and a widely used reference for professionals and students alike.

Business Intelligence: The Savvy Managers Guide, Second Edition, discusses the objectives and practices for designing and deploying a business intelligence (BI) program. It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI. It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program. It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics. Finally, emerging technologies such as text analytics and sentiment analysis are considered. This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts. - Guides managers through developing, administering, or simply understanding business intelligence technology - Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization's data into actionable knowledge - Contains a handy, quick-reference to technologies and terminology

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.

Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes