With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI.

With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI.

Open Source Data Warehousing and Business Intelligence is an all-in-one reference for developing open source based data warehousing (DW) and business intelligence (BI) solutions that are business-centric, cross-customer viable, cross-functional, cross-technology based, and enterprise-wide. Considering the entire lifecycle of an open source DW & BI implementation, its comprehensive coverage spans from basic concepts all the way through to customization. Highlighting the key differences between open source and vendor DW and BI technologies, the book identifies end-to-end solutions that are scalable, high performance, and stable. It illustrates the practical aspects of implementing and using open source DW and BI technologies to supply you with valuable on-the-project experience that can help you improve implementation and productivity. Emphasizing analysis, design, and programming, the text explains best-fit solutions as well as how to maximize ROI. Coverage includes data warehouse design, real-time processing, data integration, presentation services, and real-time reporting. With a focus on real-world applications, the author devotes an entire section to powerful implementation best practices that can help you build customer confidence while saving valuable time, effort, and resources.

This book highlights the practical aspects of using Oracle Essbase and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. It explains the key steps involved in Oracle Essbase and OBIEE implementations. Using case studies, the book covers Oracle Essbase for analytical BI and data integration, using OBIEE for operational BI including presentation services and BI Publisher for real-time reporting services, Self-service BI- in terms of VLDB, scalability, high performance, stability, long-lasting and ease of use that saves time, effort, and costs, while maximizing ROI.

Open Source Data Warehousing and Business Intelligence is an all-in-one reference for developing open source based data warehousing (DW) and business intelligence (BI) solutions that are business-centric, cross-customer viable, cross-functional, cross-technology based, and enterprise-wide. Considering the entire lifecycle of an open source DW & BI implementation, its comprehensive coverage spans from basic concepts all the way through to customization. Highlighting the key differences between open source and vendor DW and BI technologies, the book identifies end-to-end solutions that are scalable, high performance, and stable. It illustrates the practical aspects of implementing and using open source DW and BI technologies to supply you with valuable on-the-project experience that can help you improve implementation and productivity. Emphasizing analysis, design, and programming, the text explains best-fit solutions as well as how to maximize ROI. Coverage includes data warehouse design, real-time processing, data integration, presentation services, and real-time reporting. With a focus on real-world applications, the author devotes an entire section to powerful implementation best practices that can help you build customer confidence while saving valuable time, effort, and resources.

Focusing on tried and true best practice techniques in cross-technology based Oracle embedded programming, this book provides authoritative guidance for improving your code compilation and execution. Geared towards IT professionals developing Oracle-based Web-enabled applications in PL/SQL, Java, C, C++, .NET, Perl, and PHP, it covers application development from concepts to customization, following a pragmatic approach to design, coding, testing, deployment, and customization-explaining how to maximize embedded programming practices. Oracle Embedded Programming and Application Development explains application development frameworks using 3GL and 4GL high-level language code as embedded code segments across .NET, Java, and Open Source technologies, in conjunction with SQL and/or PL/SQL and the Oracle RDBMS through version 11gR2. It also: Features pluggable code using parameterized constructs to promote code reuse Explains when to use a particular embedded language as a best fit for specific applications Highlights design considerations that reduce the probability of errors, enable quick resolution, and boost performance in terms of enabling a Fast-Actionable-Synchronized-Tested (FAST) solution implementation Provides best practice techniques that can enhance any application development code-design methodology for a better, easier, faster, cheaper, and pervasive solution that in turn helps achieve a Better Business Benefit (B-B-B) This practical guide details techniques for constructing architecture and code design methodologies for live application development projects that can be generalized and standardized as application development and code design frameworks. Cover to cover, the text provides an understanding of how the designed, developed, and deployed solutions conform to emerging and next-generation trends. It also discusses the conformance and usage of Web 2.0-based RIA functionality and regulatory compliance practices involving auditing and security. Praise for: "Taking an Oracle-centric approach, Lakshman skillfully guides you through the maze of various popular programming languages and environments including .NET, C/C++, Perl, PHP, Java, and even SQL and PL/SQL - not only showing you how they interact with Oracle but also which language is the best fit for a given situation."-John Kanagaraj, Executive Editor, IOUG SELECT Journal

Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics. The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. What You Will Learn See machine learning in OBIEE Master the fundamentals of machine learning and how it pertains to BI and advanced analytics Gain an introduction to Oracle R Enterprise Discover the practical considerations of implementing machine learning with OBIEE Who This Book Is For Analytics managers, BI architects and developers, and data scientists.

This book highlights the practical aspects of using Oracle Essbase and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. It explains the key steps involved in Oracle Essbase and OBIEE implementations. Using case studies, the book covers Oracle Essbase for analytical BI and data integration, using OBIEE for operational BI including presentation services and BI Publisher for real-time reporting services, Self-service BI- in terms of VLDB, scalability, high performance, stability, long-lasting and ease of use that saves time, effort, and costs, while maximizing ROI.

Focusing on tried and true best practice techniques in cross-technology based Oracle embedded programming, this book provides authoritative guidance for improving your code compilation and execution. Geared towards IT professionals developing Oracle-based Web-enabled applications in PL/SQL, Java, C, C++, .NET, Perl, and PHP, it covers application development from concepts to customization, following a pragmatic approach to design, coding, testing, deployment, and customization explaining how to maximize embedded programming practices.

Oracle Embedded Programming and Application Development explains application development frameworks using 3GL and 4GL high-level language code as embedded code segments across .NET, Java, and Open Source technologies, in conjunction with SQL and/or PL/SQL and the Oracle RDBMS through version 11gR2. It also:

• Features pluggable code using parameterized constructs to promote code reuse
• Explains when to use a particular embedded language as a best fit for specific applications
• Highlights design considerations that reduce the probability of errors, enable quick resolution, and boost performance in terms of enabling a Fast-Actionable-Synchronized-Tested (FAST) solution implementation
• Provides best practice techniques that can enhance any application development code-design methodology for a better, easier, faster, cheaper, and pervasive solution that in turn helps achieve a Better Business Benefit (B-B-B)

This practical guide details techniques for constructing architecture and code design methodologies for live application development projects that can be generalized and standardized as application development and code design frameworks. Cover to cover, the text provides an understanding of how the designed, developed, and deployed solutions conform to emerging and next-generation trends. It also discusses the conformance and usage of Web 2.0-based RIA functionality and regulatory compliance practices involving auditing and security.

Praise for:

"Taking an Oracle-centric approach, Lakshman skillfully guides you through the maze of various popular programming languages and environments including .NET, C/C++, Perl, PHP, Java, and even SQL and PL/SQL not only showing you how they interact with Oracle but also which language is the best fit for a given situation."
John Kanagaraj, Executive Editor, IOUG SELECT Journal