Fundamentals of Business Analytics BAT401 Lesson 1 PDF

st It’s 1 Sem. Welcome Back, Learners! Log-in Fundamentals of Business Analytics Fundamentals of Business Analytics Log-in Mr. Niño G. Eusebio, LPT  LET’S START! Introduction Rules Course Outline Ice Breaker Back Sta...

st It’s 1 Sem. Welcome Back, Learners! Log-in Fundamentals of Business Analytics Fundamentals of Business Analytics Log-in Mr. Niño G. Eusebio, LPT  LET’S START! Introduction Rules Course Outline Ice Breaker Back Start  LET’S PLAY! 11 1 0 1 1 0 1 1 10 0 11 0 0 00 0 0 0 1 1 0 1 0 1 1 0 0 00 0 1 11 1 1 0 1 0 1 1 10 1 0 1 1 0 1 1 10 0 0 1 01 0 1 1 11 ‘Cross out Me, Binary’ – The students will search patterns on the board and cross out those 3 consecutive numbers. Back Start  LET’S CHECK 1 1 0 1 1 0 1 1 1 0 1 0 0 00 0 0 0 1 1 0 1 0 1 1 0 0 0 0 1 11 1 1 0 1 0 1 0 1 1 0 1 1 0 1 1 10 0 0 1 01 0 1 1 11 ‘Cross out Me, Binary’ – The students will search a pattern on the board and cross out those 3 consecutive zeros (000) and ones (111). Back Start 01 Pre-test  Topic of the Day Lecture I Meet us  CHAPTER I Business Analytics Meet us Meet us  What is Business Analytics? Decisions have significant economic consequences; moreover, they are difficult to make because of uncertain data and imperfect information about the future. What makes business decisions complicated today is the overwhelming amount of available data and information. Business analytics, or simply analytics, is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions.  Business analytics is “a process of transforming data into actions through analysis and insights in the context of organizational decision making and problem solving.” Business analytics is supported by various tools such as Microsoft Excel and various Excel add-ins, commercial statistical software packages such as SAS or Minitab, and more-complex business intelligence suites that integrate data with analytical software. ❖ Leading banks use analytics to predict and prevent credit fraud. ❖ Manufacturers use analytics for production planning, purchasing, and inventory management. ❖ Retailers use analytics to recommend products to customers and optimize marketing promotions. ❖ Pharmaceutical firms use it to get life-saving drugs to market more quickly. The leisure and vacation industries use analytics to analyze historical sales data, understand customer behavior, improve web site design, and optimize schedules and bookings. Airlines and hotels use analytics to dynamically set prices over time to maximize revenue. Sports teams are using business analytics to determine both game strategy and optimal ticket prices.  Some common types of decisions that can be enhanced by using analytics include: ∙ Pricing (for example, setting prices for consumer and industrial goods, government contracts, and maintenance contracts), ∙ Customer Segmentation (for example, identifying and targeting key customer groups in retail, insurance, and credit card industries), ∙ Merchandising (for example, determining brands to buy, quantities, and allocations), ∙ Location (for example, finding the best location for bank branches and ATMs, or where to service industrial equipment)  Research has suggested that organizations are overwhelmed by data and struggle to understand how to use data to achieve business results and that most organizations simply don’t understand how to use analytics to improve their businesses. Thus, understanding the capabilities and techniques of analytics is vital to managing in today’s business environment. One of the emerging applications of analytics is helping businesses learn from social media and exploit social media data for strategic advantage. Evolution of Business Analytics The modern evolution of analytics began with the introduction of computers in the late 1940s and their development through the 1960s and beyond. Early computers provided the ability to store and analyze data in ways that were either very difficult or impossible to do so manually. This facilitated the collection, management, analysis, and reporting of data, which is often called business intelligence (BI), a term that was coined in 1958 by an IBM researcher, Hans Peter Luhn. BI has evolved into the modern discipline we now call information systems (IS). Much of modern business analytics stems from the analysis and solution of complex decision problems using mathematical or computer-based models—a discipline known as operations research, or management science. Operations research (OR) was born from efforts to improve military operations prior to and during World War II. After the war, scientists recognized that the mathematical tools and techniques developed for military applications could be applied successfully to problems in business and industry. The field became known as Operations Research/Management Science (OR/MS). Many OR/MS applications use modeling and optimization—techniques for translating real problems into mathematics, spreadsheets, or other computer languages, and using them to find the best (“optimal”) solutions and decisions.  Decision Support Systems (DSS) began to evolve in the 1960s by combining business intelligence concepts with OR/MS models to create analytical-based computer systems to support decision making. DSSs include three components: 1. Data Management. The data management component includes databases for storing data and allows the user to input, retrieve, update, and manipulate data. 2. Model Management. The model management component consists of various statistical tools and management science models and allows the user to easily build, manipulate, analyze, and solve models. 3. Communication System. The communication system component provides the interface necessary for the user to interact with the data and model management components.  Data mining is focused on better understanding characteristics and patterns among variables in large databases using a variety of statistical and analytical tools. Simulation and risk analysis relies on spreadsheet models and statistical analysis to examine the impacts of uncertainty in the estimates and their potential interaction with one another on the output variable of interest. Spreadsheets and formal models allow one to manipulate data to perform what-if analysis—how specific combinations of inputs that reflect key assumptions will affect model outputs. Perhaps the most useful component of business analytics, which makes it truly unique, is visualization. Visualizing data and results of analyses provide a way of easily communicating data at all levels of a business and can reveal surprising patterns and relationships.  Personal computers and spreadsheets provide a convenient way to manage data, calculations, and visual graphics simultaneously, using intuitive representations instead of abstract mathematical notation. The power of analytics in a personal computing environment was noted some 20 years ago by business consultants Michael Hammer and James Champy, who said, “When accessible data is combined with easy-to-use analysis and modeling tools, frontline workers when properly trained—suddenly have sophisticated decision-making capabilities.” Although many good analytics software packages are available to professionals, we use Microsoft Excel. Impacts & Challenges Companies report reduced costs, better risk management, faster decisions, better productivity, and enhanced bottom-line performance such as profitability and customer satisfaction. Many organizations lack analytical talent, and those that do have analytical talent often don’t know how to apply the results properly. While analytics is used as part of the decision-making process in many organizations, most business decisions are still based on intuition.  Topic of the Day Lecture Meet us II to be continued  Assignment ; References: Module 1, Fundamentals of Business Analytics- BAT401 ‘’Goodbye, class! See you again.’’ Mr. Niño G. Eusebio, LPT Log-out