Business Analytics, Volume II

A Data Driven Decision Making Approach for Business

Amar Sahay

• Publisher: Business Expert Press
• Year: 2019
• ISBN:
  9781631574801
• Topic: Économie
• Subtopic: Statistiques pour les entreprises et l'économie

This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes.

It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together.

The first three chapters provide an introduction to BA, importance of analytics, types of BA-descriptive, predictive, and prescriptive-along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics-machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics.

CHAPTER 1

Business Analytics at a Glance

Chapter Highlights

• •Introduction to Business Analytics—What Is It?
• •Analytics and Business Analytics
• •Business Analytics and Its Importance in Modern Business Decisions
• •Types of Business Analytics

• ○Tools of Business Analytics

• •Descriptive Analytics: Graphical and Numerical Methods and Tools of BA

• ○Tools of Descriptive Analytics

• •Predictive Analytics

• ○Most Widely Used Predictive Analytics Models

• ▪Data Mining, Regression Models, and Time Series Forecasting

• •Background and Prerequisites to Predictive Analytics Tools
• •Other Areas Associated with Predictive Analytics
• •Recent Applications and Tools of Predictive Modeling

• ○Machine Learning, Data Mining, Artificial Neural Network, and Deep Learning

• •Prescriptive Analytics and Tools of Prescriptive Analytics
• •Analytical Models and Decision Making Using Models
• •Types of Models
• •Applications and Implementation of Analytics
• •Summary and Application of Business Analytics (BA) Tools
• •Summary
• •Glossary of Terms Related to Analytics

Introduction to Business Analytics—What Is It?

This book deals with business analytics (BA)—an emerging area in modern business decision making. This chapter provides an overview of analytics and BA used as decision-making tools in businesses today. These terms are used interchangeably, but there are slight differences in the terms of tools and the methods they use. BA uses a number of tools and algorithms ranging from statistics and data analysis, management science, information systems to computer science that are used in data-driven decision making in companies. This chapter also discusses the broad meaning of the terms—analytics, BA, different types of analytics, the tools of analytics—and how they are used in business decision making. Today, companies collect and analyze massive amounts of data. Because of the huge volume, these data are referred to as big data. Data mining is a way to extract information from big data. We discuss data mining and the techniques data mining use to extract useful information from massive amounts of data in subsequent sections and chapters.. Currently, the emerging field of analytics uses machine learning, artificial intelligence, neural networks, and deep learning techniques. These areas are becoming an essential part of analytics and are extensively used in developing algorithms and models to draw conclusions from big data.

Analytics and Business Analytics

Analytics [18] is the science of analysis—the processes by which we analyze data, draw conclusions, and make decisions. Business analytics (BA) goes well beyond simply presenting data and creating visuals, crunching numbers, and computing statistics. The essence of analytics lies in the application—making sense from the data using prescribed methods of statistical analysis, mathematical and statistical models, and logic to draw meaningful conclusion from the data. It uses methods, logic, intelligence, algorithms, and models that enable us to reason, plan, organize, analyze, solve problems, understand, innovate, and make data-driven decisions, including the decisions from dynamic real-time data.

BA covers a vast area. It is a complex field that encompasses visualization, statistics and modeling, optimization, simulation-based modeling, and statistical analysis. It uses descriptive, predictive, and prescriptive analytics, including text and speech analytics, web analytics, and other application-based analytics and much more. The following explanation of business analytics shows the vast area it covers.

Business analytics is a data-driven decision-making approach that uses statistical and quantitative analysis, information technology, management science (mathematical modeling, simulation), along with data mining and fact-based data to measure past business performance to guide an organization in business planning and effective decision making.

BA has three broad categories—descriptive, predictive, and prescriptive analytics. Each type of analytics uses a number of tools that may overlap depending on the applications and problems being solved. The descriptive analytics tools are used to visualize and explore the patterns and trends in the data. Predictive analytics uses the information from descriptive analytics to model and predict future business outcomes with the help of regression, forecasting, and predictive modeling.

In this age of technology, companies collect massive amount of data. Successful companies use their data as an asset and use them for competitive advantage. Most businesses collect and analyze massive amounts of data referred to as big data using specially designed big data software and data analytics. Big data analysis is now becoming an integral part of BA. The organizations use BA as an organizational commitment to data-driven decision making. BA helps businesses in making informed business decisions and in automating and optimizing business processes.

To understand the business performance, BA makes extensive use of data and descriptive statistics, statistical analysis, mathematical and statistical modeling, and data mining to explore, investigate, draw conclusions, and predict and optimize business outcomes. Through data, BA helps to gain insight and drive business planning and decisions. The tools of BA focus on understanding business performance using data. It uses a number of models derived from statistics, management science, and operations research areas. BA also uses statistical, mathematical, optimization, and quantitative tools for explanatory and predictive modeling [15].

Predictive modeling uses different types of regression models to predict outcomes [1] and is synonymous with the field of data mining and machine learning. It is also referred to as predictive analytics. We will provide more details and tools of predictive analytics in subsequent sections.

Business Analytics and Its Importance in Modern Business Decision

Business analytics (BA) helps to address, explore, and answer a number of questions that are critical in driving business decisions. It tries to answer the following questions:

What is happening and why did something happen?

Will it happen again?

What will happen if we make changes to some of the inputs?

What the data is telling us that we were not able to see before?

BA uses statistical analysis and predictive modeling to establish trends, figuring out why things are happening, and making a prediction about how things will turn out in the future. It combines advanced statistical analysis and predictive modeling to give us an idea of what to expect so that one can anticipate developments or make changes now to improve outcomes.

BA is more about anticipated future trends of the key performance indicators, and is about using the past data and models to learn from the existing data (descriptive analytics) and make predictions. It is different from reporting in business intelligence (BI). Analytics models use the data with a view to drawing out new, useful insights to improve business planning and boost future performance. BA helps the company adapt to the changes and take advantage of future developments.

One of the major tools of analytics is data mining, which is a part of predictive analytics. In business, data mining is used to analyze huge amount of business data. Business transaction data, along with other customer and product-related data, are continuously stored in the databases. The data mining software is used to analyze the vast amount of customer data to reveal hidden patterns, trends, and other customer behavior. Businesses use data mining to perform market analysis to identify and develop new products, analyze their supply chain, find the root cause of manufacturing problems, study the customer behavior for product promotion, improve sales by understanding the needs and requirements of their customer, prevent customer attrition, and acquire new customers. For example, Wal-Mart collects and processes over 20 million point-of-sale transactions every day. These data are stored in a centralized database and are analyzed using data mining software to understand and determine customer behavior, needs, and requirements. These data are analyzed to determine sales trends and forecasts, develop marketing strategies, and predict customer-buying habits (http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/).

A large amount of data and information about products, companies, and individuals are available through Google, Facebook, Amazon, and several other sources. Data mining and analytics tools are used to extract meaningful information and pattern to learn customer behavior. Financial institutions analyze data of millions of customers to assess risk and customer behavior. Data mining techniques are also used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education, and electrical power engineering.

BA, data analytics, and advanced analytics are growing areas. They all come under the broad umbrella of business intelligence (BI). There is going to be an increasing demand of professionals trained in these areas. Many of the tools of data analysis and statistics discussed here are prerequisite to understanding data mining and BA. We will describe the analytics tools, including data analytics, and advanced analytics later in this chapter.

Types of Business Analytics

The BA area is divided into different categories depending upon the types of analytics and tools being used. The major categories of BA are:

• •Descriptive analytics
• •Predictive analytics
• •Prescriptive analytics

Each of the above mentioned categories uses different tools, and the use of these analytics depends on the type of business and the operations a company is involved in. For example, one organization may use only descriptive analytics tools or a combination of descriptive and predictive modeling and analytics to predict future business performance to drive business decisions. Other companies may use prescriptive analytics to optimize business processes.

Tools of Business Analytics

The different types of analytics and the tools used in each type of analytics are detailed below.

Descriptive Analytics: Graphical, Numerical Methods, and Tools of Business Analytics

Descr...