This book deals with business analytics (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, management science (mathematical modeling and simulation), along with data mining and fact-based data to measure past business performance to guide an organization in business planning, predicting the future outcomes, 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 amounts of data. Successful companies view their data as an asset and use them to gain a competitive advantage. These 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, 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. It uses a number of models derived from statistics, management science, and operations research areas.
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 above categories uses different tools, and the use of these analytics depend 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.
The different types of analytics and the tools used in these analytics are described below:
- 1.Descriptive analytics involves the use of descriptive statistics, including graphical and numerical methods to describe the data. Successful use and implementation of descriptive analytics requires the understanding of types of data and visual/graphical techniques using computer. 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. Also, it requires the knowledge and understanding of different statistical measures and how statistics are used to summarize and draw conclusions from the data. Some other topics of interest are the understanding of empirical rule and the relationship between two variables—the covariance and correlation coefficient. The tools of descriptive analytics are helpful in understanding the data, identifying the trend or patterns in the data, and making sense from the data contained in the databases of companies. The understanding of databases, data warehouse, web search and query, and Big Data concepts is important in extracting and applying descriptive analytics tools.
Besides the descriptive statistics tools, an understanding of a number of other analytics tools is critical in describing and drawing meaningful conclusion from the data. These include: (a) probability theory and its role in decision making, (b) sampling and inference procedures, (c) estimation and confidence intervals, (d) hypothesis testing/inference procedures for one and two population parameters, and (e) chi-square and nonparametric tests. The understanding of these tools is critical in understanding and applying inferential statistics tools—a critical part of data analysis, decision making, and predictive analytics.
Highlight of This Book: Business Analytics—Volume II
Unlike the first volume, volume II mainly focuses on predictive analytics—a critical part of BA that focuses on predictive models to predict future business trends. A brief explanation of predictive analytics and associated models is discussed below.
- 1.Predictive Analytics: As the name suggests, predictive analytics is the application of predictive models to predict future trends. The most widely used models are regression, forecasting, data mining, and machine learning–based models. Variations of regression models include: (a) simple regression models, (b) multiple regression models, (c) nonlinear regression models including the quadratic or second-order models, and polynomial regression models, (d) regression models with indicator or qualitative independent variables, and (e) regression models with interaction terms or interaction models. Regression models are one of the most widely used models in various types of applications. These models are used to explain the relationship between a response variable and one or more independent variables. The relationship may be linear or curvilinear. The objective of these regression models is to predict the response variable using one or more independent variables or predictors.
The predictive models also involve a class of time series analysis, forecasting, data mining, and machine learning models. The commonly used forecasting models are regression-based models that use regression analysis to forecast future trend. Many regression and time series models are discussed in subsequent chapters. Most predictive models are used to forecast the future trend.
Other Models and Tools Used in Predictive Modeling and Analytics
Data mining, machine learning, and neural network applications are also an integral part of predictive analytics. The following topics are introduced in this text:
Data Mining and Advanced Data Analysis
Introduction to Machine Learning, Neural Networks, Artificial Intelligence
Business Intelligence (BI) and Online Analytical Processing tools
Data Visualization and Applications
Different Regression Models
Introduction to Classification and Clustering Techniques
- 2.Prescriptive Analytics: Prescriptive analytics is concerned with optimal allocation of resources in an organization. A number of operations research and management science tools have been applied for allocating the limited resources in the most effective way. The operations management tools derived from management science and industrial engineering including the simulation tools are also used to study different types of manufacturing and service organizations. These are proven tools and techniques in studying and understanding the operations and processes of organizations. The tools of operations management can be divided mainly into three areas. These are (a) planning, (b) analysis, and (c) control tools. The analysis part is the prescriptive analytics part that uses the operations research, management science, and simulation tools. The control part is used to monitor and control the product and service quality. There are a number of prescriptive analytics tool. These include:
- 1.Linear Optimization Models including maximization and minimization of different resources, computer analysis, and sensitivity analysis
- 2.Integer Linear Optimization Models
- 3.Nonlinear Optimization Models
- 4.Simulation Modeling and Applications
- 5.Monte Carlo Simulation
The analytics tools come under the broad area of Business Intelligence (BI) that incorporates Business Analytics (BA), data analytics, and advanced analytics. All these areas come under the umbrella of BI and use a number of visual and mathematical models.
Modeling is one of the most important parts of BA. Models are of different types. An understanding of different types of models is critical in selecting and applying the right model or models to solve business problems. The widely used models are: (a) graphical models, (b) quantitative models, (c) algebraic models, (d) spreadsheet models, and (e) other analytic tools.
Most of the tools in descriptive, predictive, and prescriptive analytics are described using one or the other type of model which are usually graphical, mathematical, or computer models. Besides these models, simulation and a number of other mathematical models are used in analytics.
BA is a vast area. It is not possible to provide a complete and in-depth treatment of all the BA topics in one concise book; therefore, the book is divided into two parts:
- •Business Analytics: A Data-Driven Decision-Making Approach for Business—Volume I
- •Business Analytics: A Data-Driven Decision-Making Approach for Business—Volume II
The first volume is available through amazon (www.amazon.com).
The first volume provides an overview of BA, BI, and data analytics and the role and importance of these in the modern business decision making. It introduces the different areas of BA: (1) descriptive analytics, (2) predictive analytics, and (3) prescriptive analytics. The tools and topics covered under each area of these analytics along with their applications in decision-making process are discussed in the first volume. The main focus of the first volume is descriptive analytics and its applications.
The focus of this second volume is predictive analytics. The introductory chapters of this volume outline the broad view of BI that constitutes not only BA but also data analytics and advanced analytics. An overview of all these areas is presented in the first two chapters followed by predictive analytics topics which is the focus of this text. The topics and the chapters contained in the second volume are outlined below. The specific topics covered in this second volume are:
Salt Lake City, UTAH, U.S.A.