AI Meets BI

Artificial Intelligence and Business Intelligence
 
 
Auerbach (Verlag)
  • erscheint ca. am 4. November 2020
  • |
  • 240 Seiten
 
E-Book | PDF ohne DRM | Systemvoraussetzungen
978-1-000-28193-4 (ISBN)
 

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.

  • Englisch
  • Milton
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
  • 11,43 MB
978-1-000-28193-4 (9781000281934)
weitere Ausgaben werden ermittelt

Lakshman Bulusu is a veteran IT professional and data scientist, with 28 years of experience in the IT industry. He has worked at major industry verticals in the retail, banking, pharma/health care, insurance, media, telecom, and education fields. He currently consults for a major banking client in the New York/New Jersey metropolitan area. He has expertise in RDBMS technologies, including Oracle®, MS SQL Server, and Vertica and their related technologies. He is also well versed in the latest-and-greatest technologies such as artificial intelligence, data science, and business intelligence. Lakshman also serves as Vice President of Research at Qteria.com. When not at his job, he lectures at various technical schools, user group conferences and summits, and data science meetings. He also devotes his free time to writing poetry in English and in his native language, Telugu.

With a long and proven track record of success in the Business Intelligence (BI) industry throughout several decades, Rosendo Abellera is a subject matter expert and expert practitioner in business intelligence and analytics. As a career consultant, he has serviced numerous leading global commercial clients and major US government organizations. A strategist as well as a hands-on developer, he architected and implemented complete, holistic, and data-centric decision-making systems and solutions from the ground up- from complex data warehouses for Revenue Recognition to advanced dashboards for Financial Analytics.

Chapter 1 Introduction
Chapter 2 AI and AI-Powered Analytics
Chapter 3 Industry Uses Cases of Enterprise BI-A Business Perspective
Chapter 4 Industry Use Cases of Enterprise BI-The AI-Way of Implementation
Chapter 5 What's Next in AI Meets BI?

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