Hardoon / Shmueli Getting Started with Business Analytics

Insightful Decision-Making
1. Auflage 2013
ISBN: 978-1-4398-9654-9
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 190 Seiten

Assuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Making explores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications.

The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and the various requirements for integrating business with analytics.

The second part presents the technologies underlying business analytics: data mining and data analytics. The book helps you understand the key concepts and ideas behind data mining and shows how data mining has expanded into data analytics when considering new types of data such as network and text data.

The third part explores business analytics in depth, covering customer, social, and operational analytics. Each chapter in this part incorporates hands-on projects based on publicly available data.

Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework for data mining in business analytics. It takes you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.

Zielgruppe

Business analytics users; students in MBA courses; researchers in machine learning and statistical learning.

Autoren/Hrsg.

Hardoon, David Roi

Shmueli, Galit

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management

Inhaltsverzeichnis

Introduction to Business Analytics

The Paradigm Shift

From Data to Insight

From Business Intelligence to Business Analytics

Levels of "Intelligence"

The Business Analytics Cycle

Objective

Data

Analytic Tools and Methods

Implementation
Guiding Questions

Requirements for Integrating Business Analytics

Common Questions

Data Mining and Data Analytics

Data Mining in a Nutshell

What Is Data Mining?
Predictive Analytics

Forecasting

Optimization

Simulation

From Data Mining to Data Analytics

Network Analytics

Text Analytics

Business Analytics

Customer Analytics

"Know Thy Customer"

Targeting Customers

Project Suggestions

Social Analytics

Customer Satisfaction

Mining Online Buzz

Project Suggestions

Operational Analytics

Inventory Management

Marketing Optimization

Predictive Maintenance

Human Resources and Workforce Management

Project Suggestions

Bibliography

Über Autor(innen)

David R. Hardoon is head of analytics at SAS Singapore, where he is responsible for the positioning of business analytics capabilities and solutions to customers across different business sectors. Dr. Hardoon is also an adjunct faculty member in the School of Information Systems at Singapore Management University and an honorary senior research associate in the Centre for Computational Statistics and Machine Learning at University College London. His research interests include developing and applying computational analytical models for business knowledge discovery and analysis in areas such as taxonomy, neuroscience, aerospace, and finance. He earned a PhD in computer science in the field of machine learning from the University of Southampton.

Galit Shmueli is a SRITNE chaired professor of data analytics and associate professor of statistics and information systems at the Indian School of Business. She is the author of 70 journal articles, references, textbooks, and book chapters in statistics, management, information systems, and marketing. Her research and teaching focus on statistical and data mining methods for contemporary data and applications in information systems and healthcare. She earned a PhD in statistics from the Israel Institute of Technology.