AI and AI-Powered Analytics

• Published in: AI Meets BI pp. 9 - 17
• Main Authors: Bulusu, Lakshman, Abellera, Rosendo
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2021; Auerbach Publishers, Incorporated
• Edition: 1
• Subjects: Confidence Prediction Interval; DNN; NLG; Deep Learning Models; Vice Versa; Natural Language Capabilities; Machine Learning; Automatic Image Recognition; Cognitive Computing; NLP; Business Processes; Bi Platform; Bi Solution; Classification Machine Learning Algorithm; Predictive Analytics; Bi System; Max Pooling Layer; Machine Learning Platform; Bi; Enterprise Landscape; Prescriptive Analytics; Applying Data Quality; Seasonal ARIMA; User Landscape; Adaptive Intelligence
• ISBN: 0367643812; 9780367332600; 9780367643812; 0367332604
• DOI: 10.1201/9781003122081-2

From smart data discovery to data standardization to Artificial Intelligence (AI)-powered models applied to the data, AI has empowered the user landscape from technical user and data scientist to business analyst, citizen scientist, power users, and end users, enabling them to create analytics from business context in real time and on data as it is created. This chapter looks into these details and how AI and AI-powered analytics have resulted in a BI solution that delivers intelligent insights from any and all kinds of data and, at the same time, is next-gen, scalable, contextually relevant, and one that is customizable and personalized-from on-premise to on-the-go. AI changes the enterprise landscape of business intelligence by way of adaptive intelligence whereby applications and analytics can be integrated together by applying and embedding AI/ML-based techniques. The so-called analytics sphere contains descriptive analytics at its core and progresses outward in enclosing spherical circles that encompass diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive computing.