Business analytics.pdf - PART I Foundations of Business Analytics(BA CHAPTER 1 Business Analytics(BA at a Glance Chapter Highlights Introduction to

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Unformatted text preview: 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 factbased 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 [ ]. 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 make decision 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 are important in extracting and applying descriptive analytics tools. Figure 1.1 Tools of descriptive analytics Tools of Descriptive Analytics: Figure 1.1 outlines the tools and methods used in descriptive analytics. These tools are explained in subsequent chapters. 2.Predictive analytics Predictive Analytics: As the name suggests predictive analytics is the application of predictive models to predict future business outcomes and trends. Most Widely Used Predictive Analytics Models The most widely used predictive analytics models are regression, forecasting, and data mining techniques. These are briefly explained in the following. Data mining techniques are used to extract useful information from huge amounts of data using predictive analytics, computer algorithms, software, mathematical, and statistical tools. Regression models are used for predicting the future outcomes. Variations of regression models include: (a) Simple regression models, (b) Multiple regression models, (c) Non-linear 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. Forecasting techniques are widely used predictive models that involve a class of Time Series Analysis and Forecasting models. The commonly used forecasting models are regression based models that uses regression analysis to forecast future trend. Other time series forecasting models are simple moving average, moving average with trend, exponential smoothing, exponential smoothing with trend, and forecasting seasonal data. All these predictive models are used to forecast the future trend. Figure 1.2 shows the widely used tools of predictive analytics. Other Predictive Analytics Tools Besides the tools described in Figure 1.2, an understanding of a number of other analytics tools is critical in describing and drawing meaningful conclusions 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 non-parametric tests. The understanding of these tools is critical in understanding and applying inferential statistics tools—a critical part of data analysis and decision making. These tools are outlined in Figure 1.3. Figure 1.2 Tools of predictive analytics Figure 1.3 Prerequisite to predictive analytics Additional Tools and Applications of Predictive Analytics Predictive analytics methods are also used in detecting anomalies (or outlier) detection, patterns, association learning, and the concepts of classification and clustering to predict the probabilities and future business outcomes. We briefly describe here anomaly, association learning, classification, and clustering. Figure 1.4 shows the broad categories and applications of predictive analytics. Figure 1.4 Categories of predictive analytics Association learning is used to identify the items that may co-occur and the possible reasons for their co-occurrence. Classification and clustering techniques are used for association learning. Anomaly detection is also known as outlier detection and is used to identify specific events, or items, which do not conform to usual or expected pattern in the data. Typical example would be the detection of bank fraud. Classification and clustering algorithms are used to divide the data into categories or classes. The purpose is to predict the probabilities of future outcomes based on the classification. Clustering and classification both divide the data into classes and therefore, seem to be similar but they are two different techniques. They are learning techniques used widely to obtain reliable information from a collection of raw data. Classification and clustering are widely used in data mining. Classification Classification is a process of assigning items to pre specified classes or categories. For example, a financial institution may study the potential borrowers to predict whether a group of new borrowers may be classified as having a high degree of risk. Spam filtering is another example of classification, where the inputs are e-mail messages that are classified into classes as “spam” and “no spam.” Classification uses the algorithms to categorize the new data according to the observations of the training set. Classification is a supervised learning technique where a training set is used to find similarities in classes. This means that the input data are divided into two or more classes or categories and the learner creates a model that assigns inputs to one or more of these classes. This is typically done in a supervised way. The objects are classified on the basis of the training set of data. The algorithm that implements classification is known as the classifier. Some of the most commonly used classification algorithms are K-Nearest Neighbor algorithm and decision tree algorithms. These are widely used in data mining. An example of classification would be credit card processing. A credit card company may want to segment customer database based on similar buying patterns. Clustering Clustering technique is used to find natural groupings or clusters in a set of data without pre specifying a set of categories. It is unlike classification where the objects are classified based on pre specified classes or categories. Thus, clustering is an unsupervised learning technique where a training set is not used. It uses statistical tools and concepts to create clusters with similar features within the data. Some examples of clustering are: •Cluster of houses in a town into neighborhoods based on similar features like houses with overall value of over million dollars. •Marketing analyst may define distinct groups in their customer bases to develop targeted marketing programs. •City-planning may be interested in identifying groups of houses according to their house value, type, and location. •In cellular manufacturing, the clustering algorithms are used to form the clusters of similar machines and processes to form machine-component cells. •Scientists and Geologists may study the Earthquake epicenters to identify clusters of fault lines with high probability of possible earthquake occurrences. Main Article: Cluster Analysis Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some pre specified criterion or criteria, while observations drawn from different clusters are dissimilar. Clustering techniques differ in application and make different assumptions on the structure of the data. In clustering, the clusters are commonly defined by some similarity metric or similarity coefficient and may be evaluated by internal compactness (similarity between members of the same cluster) and separation between different clusters. Other clustering methods are based on estimated density and graph connectivity. It is important to note that clustering is unsupervised learning, and commonly used method in statistical data analysis. The Difference Between Clustering and Classification Clustering is an unsupervised learning technique used to find groups or clusters of similar instances on the basis of features. The purpose of clustering is a process of grouping similar objects to determine whether there is any relationship between them. Classification is a supervised learning technique used to find similarities in classification based on a training set. It uses algorithms to categorize the new data according to the observations in the training set. Other Areas Associated with Predictive Analytics Figure 1.5 outlines recent applications and tools of predictive analytics. The tools outlined in the Figure 1.5 are briefly explained in the following. Extensive applications have emerged in recent years using these methods, which are hot topics of research. A number of applications in business, engineering, manufacturing, medicine, signal processing, and computer engineering using machine learning, neural networks, and deep learning are being reported. Figure 1.5 Recent applications and tools of predictive modeling Machine Learning, Data Mining, and Neural Networks In the broad area of data and predictive analytics, machine learning is a method used to develop complex models and algorithms that are used to make predictions. The analytical models in machine learning allow the analysts to make predictions by learning from the trends, patterns, and relationships in the historical data. Machine learning automates model building. The algorithms in machine learning are designed to learn iteratively from data without being programmed. According to Arthur Samuel, machine learning gives “computers the ability to learn without being explicitly programmed.”[3][4] Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term “machine learning” in 1959 while at IBM. Machine learning algorithms are extensively used for data-driven predictions and in decision making. Some applications where machine learning has been used are e-mail filtering, detection of network intruders or detecting a data breach, optical character recognition (OCR), learning to rank, and computer vision. Machine learning is employed in a range of computing tasks. Often designing and programming explicit algorithms that are reproducible and have repeatability with good performance is difficult or infeasible. Machine Learning and Data Mining Machine learning and data mining are similar in some ways and often overlap in applications. Machine learning is used for prediction, based on known properties learned from the training data; whereas data mining algorithms are used for discovery of (previously) unknown patterns. Data mining is concerned with knowledge discovery in databases (KDD). Data mining uses many machine learn...
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