While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitalization, the integration of digital measurement and control systems, and automatic exchange between devices (Internet of Things). In the future, the use of business intelligence (BI) and a look into the past will no longer be sufficient for most companies.Instead, business analytics, i.e., predictive and predictive analyses and automated decisions, will be needed to stay competitive in the future. The use of growing amounts of data is a significant challenge and one of the most important areas of data analysis is represented by artificial intelligence methods.This book provides a concise introduction to the essential aspects of using artificial intelligence methods for business analytics, presents machine learning and the most important algorithms in a comprehensible form using the business analytics technology framework, and shows application scenarios from various industries. In addition, it provides the Business Analytics Model for Artificial Intelligence, a reference procedure model for structuring BA and AI projects in the company.
This book is a translation of the original German 1st edition Künstliche Intelligenz für Business Analytics by Felix Weber, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.
Author(s): Felix Weber
Publisher: Springer Vieweg
Year: 2023
Language: English
Pages: 145
City: Wiesbaden
Preface
About the Aim of the Book
Contents
1: Business Analytics and Intelligence
Need for Increasing Analytical Decision Support
Distinction Between Business Intelligence and Business Analytics
Categorisation of Analytical Methods and Models
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Business Analytics Technology Framework (BA.TF)
Data Sources
Data Preparation
Data Storage
Analysis
Access and Use
(Big)-Data Management and Governance
Procedure Model: Business Analytics Model for Artificial Intelligence (BAM.AI)
Development Cycle
Business Understanding
Data Discovery
Data Wrangling
Analysis
Validation
New Data Acquisition
Deployment Cycle
Publish
Analytic Deployment
Application Integration
Test
Production/Operations
Continuous Improvement
References
2: Artificial Intelligence
Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Overview of the Types of Machine Learning
Neural Networks
Types of Problems in Artificial Intelligence and Their Algorithms
Classification
Dependencies and Associations
Clustering
Regression, Prediction, or Forecasting
Optimization
Detection of Anomalies (Outliner)
Recommendation or Recommender Systems
When to Use Which Algorithm?
References
3: AI and BA Platforms
Basic Concepts and Software Frameworks
Data Management
Data Warehouse
Data Lake
Data Streaming and Message Queuing
Database Management System
Apache Hadoop
Data Analysis and Programming Languages
Python
R
SQL
Scala
Julia
AI Frameworks
Tensorflow
Theano
Torch
Scikit-Learn
Jupyter Notebook
Business Analytics and Machine Learning as a Service (Cloud Platforms)
Amazon AWS
Amazon AWS Data Services
Amazon S3 (Data Service)
Amazon RDS (Data Service)
Amazon AWS ML Services
Amazon SageMaker (ML Service)
Amazon Forecast (MLaaS)
Amazon Personalize (Analytics Service)
Google Cloud Platform
Data Services from Google
Firebase/Google Firebase Realtime Database
Google BigQuery
ML Services from Google
Google Prediction API and Cloud AutoML
Google Cloud Machine Learning Engine (Cloud Machine Learning Engine)
IBM Watson
Microsoft Azure
Data Services from Microsoft Azure
Azure Cosmos DB
ML Services from Microsoft Azure
Microsoft Azure Machine Learning Studio
Microsoft Azure Machine Learning Services
Overview of Other Microsoft Azure Services
SAP Business Technology Platform (SAP BTP)
Data Services from SAP
SAP Data Hub and SAP Data Intelligence
ML Services from SAP
SAP Leonardo Machine Learning Foundation
AP Predictive Service
SAP HANA Database Platform
Build or Buy?
References
4: Case Studies on the Use of AI-Based Business Analytics
Case Study: Analyzing Customer Sentiment in Real Time with Streaming Analytics
Customer Satisfaction in the Retail Sector
Technology Acceptance and Omnichannel for More Data
Customer Satisfaction Streaming Index (CSSI)
Implementation in a Retail Architecture
Results
Case Study: Market Segmentation and Automation in Retailing with Neural Networks
The Location Decision in Stationary Trade
Marketing Segmentation and Catchment Area
Classical Clustering Approaches and Growing Neural Gas
Project Structure
The Data and Sources
Implementation
Results
References
