Business Analytics, Volume I

A Data-Driven Decision Making Approach for Business

Amar Sahay

• Publisher: Business Expert Press
• Year: 2018
• ISBN:
  9781631573323
• Topic: Economics
• Subtopic: Statistics for Business & Economics
• Index: Economics

Business Analytics: A Data-Driven Decision Making Approach for Business-Part I, /i> provides an overview of business analytics (BA), business intelligence (BI), and the role and importance of these in the modern business decision-making.

The book discusses all these areas along with three main analytics categories: (1) descriptive, (2) predictive, and (3) prescriptive analytics with their tools and applications in business. This volume focuses on descriptive analytics that involves the use of descriptive and visual or graphical methods, numerical methods, as well as data analysis tools, big data applications, and the use of data dashboards to understand business performance.

The highlights of this volume are: Business analytics at a glance; Business intelligence (BI), data analytics; Data, data types, descriptive analytics; Data visualization tools; Data visualization with big data; Descriptive analytics-numerical methods; Case analysis with computer applications.

PART I

Foundations of Business Analytics (BA)

CHAPTER 1

Business Analytics (BA) at a Glance

Chapter Highlights

Introduction to 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 in BA

Tools of Descriptive Analytics

Predictive Analytics

Most Widely Used Predictive Analytics Models

Data Mining, Regression Models, and Time Series Forecasting

Other Predictive Analytics Models

Recent Applications and Tools of Predictive Modeling

Clustering, Classification

Other Areas Associated with Predictive Modeling

Data Mining, Machine Learning, Neural Network, and Deep Learning

Prescriptive Analytics and Tools of Prescriptive Analytics

Applications and Implementation

Summary and Application of Business Analytics (BA) Tools:

Analytical Models and Decision Making Using Models

Glossary of Terms Related to Analytics

Summary

Introduction to Business Analytics

A recent trend in data analysis is the emerging field of business analytics (BA).

This book deals with BA—an emerging area in modern business decision making.

BA is a data driven decision making approach that uses statistical and quantitative analysis, information technology, and 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 tools are also used to visualize and explore the patterns and trends in the data to predict future business outcomes with the help of 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 companies use BA tools as an organizational commitment to data-driven decision making. BA helps businesses in making informed business decisions. It is also critical in automating and optimizing business processes.

BA makes extensive use of data and descriptive statistics, statistical analysis, mathematical and statistical modeling, and data mining to explore, investigate and understand the business performance. Through data, BA helps to gain insight and drive business planning and decisions. The tools of BA focus on understanding business performance based on the data and 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 [1].

Predictive modeling uses statistical models, such as, different types of regression to predict outcomes [2] 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

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.

BA combines advanced statistical analysis and predictive modeling to give us an idea of what to expect so that you can anticipate developments or make changes now to improve outcomes.

BA is more about anticipated future trends of the key performance indicators. This is about using the past data and models to make predictions. This is different from the 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 business data. Business transaction data along with other customer and product related data are continuously stored in the databases. The data mining software are 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. The data are analyzed to determine sales trends and forecasts, develop marketing strategies, and predict customer-buying habits [http://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 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, advanced analytics later in this chapter.

Types of Business Analytics

The BA area can be 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 previous 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, an organization may only use descriptive analytics tools; whereas another company may use 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.

1. Descriptive analytics: graphical and numerical methods and tools in BA

Descriptive analytics involves the use of descriptive statistics including the graphical and numerical methods to describe the data.

Descriptive analytics tools are used to understand the occurrence of certain business phenomenon or outcomes and explain these outcomes through graphical, quantitative and numerical analysis. Through the visual and simple analysis using the collected data we can visualize and explore what has been happening and the possible reasons for the occurrence of certain phenomenon. Many of the hidden patterns and features not apparent through mere examination of data can be exposed through graphical and numerical analysis. Descriptive analytics uses simple tools to uncover many of the problems quickly and easily. The results enable us question many of the outcomes so that corrective actions can be taken.

Successful use and implementation of descriptive analytics requires the understanding of types of data, graphical/visual representation of data, and graphical techniques using computer. The graphical and visual techniques are explained in detail in Chapter 4. The descriptive analytics tools include the commonly used graphs and charts along with some newly developed graphical tools such as, bullet graphs, tree maps, and data dashboards. Dashboards are now becoming very popular with big data. They are used to display the multiple views of the business data graphically.

The other aspect of descriptive analytics is an understanding of numerical methods including the measures of central tendency, measures of position, measures of variation, and measures of shape, and how different measures and statistics are used to draw conclusions and m...