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2022 | Book

Business Analytics for Managers Read chapter Read first chapter

Business Analytics for Professionals

Editors: Prof. Dr. Alp Ustundag, Emre Cevikcan, Omer Faruk Beyca

Publisher: Springer International Publishing

Book Series : Springer Series in Advanced Manufacturing

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book explains concepts and techniques for business analytics and demonstrate them on real life applications for managers and practitioners. It illustrates how machine learning and optimization techniques can be used to implement intelligent business automation systems. The book examines business problems concerning supply chain, marketing & CRM, financial, manufacturing and human resources functions and supplies solutions in Python.

Table of Contents

Frontmatter

Methods and Technologies for Business Analytics

Frontmatter
Business Analytics for Managers
Abstract
Humans are decision-makers. Career and lifestyle decisions such as starting a business, purchasing a product, and investing in the future are all personalized decisions. Managers also in businesses make many decisions every day. If there is one truth that may be agreed upon about all managers, it is that they are overwhelmed by the burden of decisions. In today's business environment companies use data and analytics to gain competitive advantage by taking the most accurate strategic and operational decisions. Companies can vastly enhance their business operations through the use of business analytics, such as sophisticated data analysis, optimization and machine learning. The goal of business analytics is to help organizations improve their decision making with faster, better analysis.
Yakup Turgut, Yildiz Kose, Alp Ustundag, Emre Cevikcan
Descriptive Analytics
Abstract
In this chapter, descriptive analytics and statistical analyses are introduced. Specifically, data exploration and visualization, probability distributions, statistical inference, and Bayesian statistics are explained. Along with theory, practical applications on a sample data set are provided. Applications are performed using the following Python libraries: Pandas, Seaborn, and Statmodels.
Nizamettin Bayyurt, Sefer Baday
Prediction Modeling
Abstract
Predictive modeling can be defined as modeling the historical data using statistical and machine learning techniques to predict future observations. Prediction modeling tasks can be grouped into three categories: supervised learning, unsupervised learning and reinforcement learning.
Nursah Alkan, Yakup Turgut, Emre Ari, Seval Ata, Mehmet Yasin Ulukus, Mehmet Ali Ergun, Omer Faruk Beyca
Time Series Analysis
Abstract
The emergence of digital technologies has been changing how things are done in the workplace, in society, and even at home. Recent technological advancements enable the instantaneous recording, processing, and dissemination of information and therefore decision-making processes become more efficient and effective.
Erkan Isikli, Leyla Temizer, Abdullah Emin Kazdaloglu, Emre Ari
Neural Networks and Deep Learning
Abstract
Artificial neural network is a well-known machine learning technique inspired by biological neural network structures. It mimics the human brain’s working mechanism by artificially forming a neural network. In this book, artificial neural networks are referred to as neural networks. The principal idea of a neural network is to show transformation between input and output as connections between neurons in a sequence (arrangement) of layers (White L, Togneri R, Liu W, Bennamoun M (2019) Neural Representations of Natural Language, vol. 783. Singapore: Springer Singapore). Neural networks are mostly used for prediction, decision making, pattern recognition, and novelty detection (Si in Data Mining Techniques for the Life Sciences, Humana Press, Totowa, NJ, 2010). The first is that without domain expertise, neural networks may assist in estimating function structures and parameters (Si in Data Mining Techniques for the Life Sciences, Humana Press, Totowa, NJ, 2010).
Zeynep Burcu Kizilkan, Mahmut Sami Sivri, Ibrahim Yazici, Omer Faruk Beyca
Feature Engineering
Abstract
As the amount of data generated and collected grows, analyzing and modeling so many input variables get more difficult. So, it is important to reduce model complexity and establish simple, accurate and robust models. Feature engineering is the process of using domain knowledge to extract input variables from raw data, prioritize them and select the best ones so that machine learning algorithms work well and model performance is improved.
Alp Ustundag, Mahmut Sami Sivri, Kenan Menguc
Text Analytics
Abstract
In the age of big data, organizations and businesses have had to manage and make sense of data generated by a wide variety of systems, processes and transactions. The data contained in traditional relational databases is rather small compared to various sensor or social media data.
Mahmut Sami Sivri, Buse Sibel Korkmaz
Image Analysis
Abstract
One of the biggest impacts on the advancement of information technologies is the easy acquisition and processing of information.
Nurullah Calik, Behcet Ugur Toreyin
Prescriptive Analytics: Optimization and Modeling
Abstract
Prescriptive analytics, a type of complex business analytics, aims to suggest the best among various decision options to benefit from the predicted future using large amounts of data. In this process, prescriptive analytics combines the output of predictive analytics and uses artificial intelligence, optimization algorithms, and expert systems to provide adaptive, automated, constrained, time-bound, and optimal decisions, thus having the potential to bring the greatest intelligence and value to businesses.
Nursah Alkan, Kenan Menguc, Özgür Kabak
Big Data Management and Technologies
Abstract
We create and collect more data today than we have in the past. All this data comes from and is reviewed from different sources, including social media platforms, our phones and computers, healthcare gadgets and wearable technology, scientific instruments, financial institutions, the manufacturing industry, news channels and more. When these small and wide data are analyzed, it offers businesses the opportunity to take quick action in business-development processes (B2B, B2C), gain a different perspective, and better understand applications, creating new opportunities. While changing their sales and marketing strategies, businesses are now able to manage the data they collect in real time to transform themselves, to record them in a healthy way, to analyze and evaluate data-based processes, and to determine their digital transformation roadmaps, their interactions with their customers, sectoral diffraction, application and advanced analyses. They want to accelerate the transformation processes within the technology triangle. As a result, big data technologies and applications are at the center of everything and becomes an important application for digital transformation.
Altan Cakir

Business Applications

Frontmatter
Supply Chain Analytics
Abstract
Supply chain management (SCM) is the process of managing the people, resources, activities, and technology involved in manufacturing and delivering a product or service in order to reduce costs and avoid shortages. Supply chain analytics is the term that refers to the analytical decision-making processes using huge amount of data generated through the supply chain. Analytics in the supply chain is a critical component of SCM. Descriptive, predictive, and prescriptive analytics should be combined to optimize supply chain planning processes.
Yakup Turgut, Kenan Menguc, Nursah Alkan, Yildiz Kose, Seval Ata, Sule Itir Satoglu, Ozgur Kabak
CRM and Marketing Analytics
Abstract
Customer relationship management (CRM) and marketing analytics is a combination of techniques, technologies, and strategies that serve to create and deliver value to profitable customers. It involves internal business processes and functions (such as marketing, sales) as well as external influences (such as competitors). CRM systems collect, analyze and model information about customers using data science methods at all stages of their life cycle to establish and maintain long-term profitable relationships and create loyal customers.
Sultan Ceren Oner, Yusuf Isik, Abdullah Emin Kazdaloglu, Mirac Murat, Tolga Ahmet Kalayci, Kubra Cetin Yildiz, Aycan Pekpazar, Mahmut Sami Sivri, Nevcihan Toraman, Basar Oztaysi, Umut Asan, Cigdem Altin Gumussoy
Financial Analytics
Abstract
Financial analytics is the application of data science methods and techniques in finance domain. Data analytics has a significant and rising role in helping companies reduce risk and make more efficient financial decisions. Machine learning and advanced analytics are used in several financial applications such as fraud detection and prevention systems, credit risk modelling, financial statement analysis, algorithmic trading, robo-advisory systems etc.
Mahmut Sami Sivri, Abdullah Emin Kazdaloglu, Emre Ari, Hidayet Beyhan, Alp Ustundag
Human Resources Analytics
Abstract
Human resource analytics is a special part of analytics where the main focus is the human resource. In HR analytics, the analytical process is applied to the organization’s human resources.
Ozgur Akarsu, Cigdem Kadaifci, Sezi Cevik Onar
Manufacturing Analytics
Abstract
With the advent of the Industry 4.0 revolution, the manufacturing industry uses analytics enhanced with real-time production data to enable and maintain enterprise-wide automation as well as making better and faster decisions.
Nursah Alkan, Dogan Oruc, Arif Gulbiter, Mehmet Ali Ergun
Metadata
Title
Business Analytics for Professionals
Editors
Prof. Dr. Alp Ustundag
Emre Cevikcan
Omer Faruk Beyca
Copyright Year
2022
Electronic ISBN
978-3-030-93823-9
Print ISBN
978-3-030-93822-2
DOI
https://doi.org/10.1007/978-3-030-93823-9