Fundamentals of Business Analytics (Laguna University, PDF)

FUNDAMENTALS OF BUSINESS ANALYTICS Chrisna L. Fucio v2.07012024 1 v2.07012024  LAGUNA UNIVERSITY Vision Laguna University shall be a socially responsive educational ins...

FUNDAMENTALS OF BUSINESS ANALYTICS Chrisna L. Fucio v2.07012024 1 v2.07012024  LAGUNA UNIVERSITY Vision Laguna University shall be a socially responsive educational institution of choice providing holistically developed individuals in the Asia-Pacific Region. Mission Laguna University is committed to produce academically prepared and technically skilled individuals who are socially and morally upright. 2 Course Code: BA 3101 – Fundamentals of Business Analytics Course Description: The course provides students with an overview of the current trends in business analytics that drives today’s business. The course will provide an understanding of data management techniques that can help an organization to achieve its business and address operational challenges. This will also introduce different tools and methods used in business analytics to provide the students with opportunities to apply these techniques in simulation in a computer laboratory. Course Intended Learning Outcomes (CILO): At the end of this course, the students should be able to: 1. Understand data management concepts and criticality of data availability in order to make reliable business decisions; 2. Demonstrate understanding of business intelligence including the importance of data gathering, data storing, data analyzing and accessing data; 3. Describe where to look for data in an organization and create required reports; and 4. Perform high-quality tasks required by the organization in particular, and the industry in general. Course Requirements: ▪ Class Standing - 60% ▪ Major Exams - 40% _________ Periodic Grade 100% Prelim Grade = 60% (Class standing) + 40% (Prelim exam) Midterm Grade = 30% (Prelim Grade) + 70 % (Midterm Grade): [60% (Midterm Class standing) + 40% (Midterm exam)] Final Grade = 30% (Midterm Grade) + 70 % (Final Grade): [60% (Final Class standing) + 40% (Final exam)] 3  Table of Contents Module 1: FOUNDATION OF BUSINESS ANALYTICS Introduction 1 Learning Outcomes 1 Lesson 1. What can we do with Data? 2 Lesson 2. What is Business Analytics? 4 Lesson 3. Evolution of Business Analytics 9 Lesson 4. Types of Business Analytics Techniques 13 Lesson 5. Business Analytics Applications 15 Lesson 6. How Does Business Analytics Work? 18 Assessment Task 19 Summary 20 References 20 Module 2: WHY BUSINESS ANALYTICS IS SO IMPORTANT FOR SUCCESS? Introduction 22 Learning Outcomes 22 Lesson 1. Importance of Business Analytics 23 Lesson 2. Scope of Business Analytics 24 Lesson 3. Benefits of Business Analytics 25 Lesson 4. Role and Responsibilities in Business Analytics 26 Lesson 5. Career in Business Analytics 27 Assessment Task 29 Summary 30 Reference 30 Module 3: DIFFERENCE BETWEEN BUSINESS INTELLIGENCE AND BUSINESS ANALYTICS Introduction 31 Learning Outcomes 31 Lesson 1. Business Intelligence vs. Business Analytics 32 Lesson 2. Business Analytics vs. Data Analytics 37 Lesson 3. Business Analytics vs. Data Science 39 Lesson 4. Common Challenges of Business Analytics 40 Lesson 5. Business Analytics Examples and Tools 43 Assessment Task 45 Summary 45 Reference 46 Module 4: BUSINESS ANALYTICS PROCESS AND FRAMEWORK Introduction Learning Objectives 47 Lesson 1. Business Analytics Process 47 48 4  Lesson 1.1 Relationship of BA Process and Organization 52 Decision-Making Process Lesson 2. Business Analytics Framework 53 Lesson 2.1 Framework for Business Analytics 56 Lesson 2.2 Types of Analytics 59 Assessment Task 65 Summary 65 Reference 66 List of Tables Table Description Page No. 3.1 Octal Number Representation in Binary Numbers 32 4.1 Types of Data Measurement Classification Scales 50 List of Figures Figure Description Page No. 1.1 Data Pyramid 4 1.2 A Visual Perspective of Business Analytics 11 2.1 Business Analytics Benefits 25 4.1 Business Analytics Process by M.J. Schniederjans 48 4.2 Business Analytics Process 50 4.3 Comparison of business analytics and organization 52 decision-making processes 4.4 Importance of Business Analysis Framework 54 4.5 Framework of Business Analytics 57 4.6 Framework of Business Analytics 60 5  MODULE 1 FOUNDATION OF BUSINESS ANALYTICS Introduction Today, businesses are moving forward in a fast-paced environment. Newer technological solutions are offering more effective solutions for organizations than ever before. Business Analytics is one of the significant factors that has contributed significantly to guiding businesses towards more success. The analytics field has evolved from just displaying the facts and figures into more collaborative business intelligence that predicts outcomes and assists in decision making for the future (simplilearn.com/, 2024). Everyone makes decisions. Individuals face personal decisions such as choosing a college or graduate program, making product purchases, selecting a mortgage instrument, and investing for retirement. Managers in business organizations make numerous decisions every day. Some of these decisions include what products to make and how to price them, where to locate facilities, how many people to hire, where to allocate advertising budgets, whether or not to outsource a business function or make a capital investment, and how to schedule production. Many of these decisions have significant economic consequences; moreover, they are difficult to make because of uncertain data and imperfect information about the future (Evans, 2021). Managers today no longer make decisions based on pure judgment and experience; they rely on factual data and the ability to manipulate and analyze data to supplement their intuition and experience, and to justify their decisions. What makes business decisions complicated today is the overwhelming amount of available data and information. We will try to discuss the foundation of business analytics in this module (Evans, 2021). Learning Outcomes At the end of this lesson, the student should be able to: 1. Define business analytics. 6  2. State some typical examples of business applications in which analytics would be beneficial. 3. Summarize the evolution of business analytics and explain the concepts of business intelligence, operations research and management science, and decision support systems. Lesson 1. WHAT CAN WE DO WITH DATA? Until recently, researchers working with data analysis were struggling to obtain data for their experiments. Recent advances in the technology of data processing, data storage and data transmission, associated with advanced and intelligent computer software, reducing cost and increasing capacity, have changed this scenario. It is the time of the Internet of Things, where the aim is to have everything connected. Data previously produced on paper is now online. Each day, larger quantity of data is generated and consumed. Whenever you place a comment in your social network, upload a photograph, some music or a vide, navigate through the Internet, or add a comment to an e-commerce website, you are contributing data increase. In the realm of data analytics, the potential of data is vast and transformative. By leveraging data, we can analyze historical performance to understand and report past trends, use statistical models to forecast future behaviors and outcomes, and recommend optimal actions to achieve the best possible results. This enables organizations to make informed decisions, optimize operations, and drive innovation. Data analytics transforms raw information into actionable knowledge, providing a competitive edge across various industries. By effectively analyzing data, organizations can unlock valuable insights that drive strategic initiatives, optimize operations, and foster innovation. Data analytics transforms raw information into actionable knowledge, enabling better decision-making and a competitive edge in various industries. ✓ ANALYTICS The analysis of data to extract such knowledge is the subject of vibrant area known as data analytics or simply “analytics”. You can find several definitions of analytics in the literature such as: 2  Analytics is the science that analyzes crude data to extract useful knowledge (patterns) from them. This process can also include data collection, organization, pre- processing, transformation, modeling and interpretation. Analytics is the systematic and computational examination of data to extract meaningful insights. It entails analyzing historical data to uncover patterns and trends, utilizing statistical models and algorithms to predict future outcomes, and providing actionable recommendations to optimize decision-making. In essence, analytics transforms raw data into valuable information, empowering organizations to make data-driven decisions, enhance operational efficiency, and drive strategic initiatives. ✓ WHAT IS DATA? Understanding the concept of data is foundational. Data encompasses the raw facts, observations, or information that can be collected, stored, and analyzed to derive insights and make informed decisions. It serves as the fundamental building block upon which analysis, interpretation, and decision-making processes in various fields are constructed. At its core, data can take on multiple forms, including numerical, textual, visual, or auditory representations. It can originate from a plethora of sources, such as sensors, surveys, transactions, social media interactions, or scientific experiments. Each datum, or data point, contributes to a larger dataset, which can range from small and focused to vast and complex, depending on the context and purpose of analysis. Data is the precursor to information. It’s an unorganized collection of values expressed as numbers, text or symbols. That may seem quite abstract – and raw data is somewhat abstract – but data is important because by processing it, through analysis, we can glean useful facts and knowledge. Data is the plural of datum, which is Latin for “a thing that has been given”. So, we might think of a single datum as an isolated fact that might seem meaningless on its own, but when accumulated into a larger collection of data (also known as a dataset), it can be analyzed to tell us factual information (sciencelearn.org.nz, 2024). Data Pyramid: Data, information, knowledge and wisdom are sometimes shown as steps on a pyramid illustrating the different ways we discover and use facts about the world. 3  Figure 1.1 Data Pyramid Source: www.sciencelearn.org.nz/, 2024 Data is sometimes represented as the bottom step of a pyramid showing how facts are used. Raw data is processed to glean usable information, which we can use to increase our knowledge about the world. Knowledge in turn leads to wisdom, which tells us how to act. Lesson 2. WHAT IS BUSINESS ANALYTICS? (Evans, 2021) Business analytics is the process of analyzing huge datasets to unearth trends, patterns, correlations, outliers and anomalies to help organizations derive inferences and make data-driven decisions. Components (selecthub.com, 2024) Business analytics leverages the following components: ✓ Data Aggregation: Aggregate raw data to provide summary statistics, including mean, minimum, maximum, sum and count. ✓ Data Mining: Identify trends and patterns in large data sets to solve problems. Data mining employs various statistical techniques such as classification, regression and clustering for data-driven decisions. 4  ✓ Text Mining: Explore and analyze text data obtained from social media posts, blog comments, video or audio scripts, corporate documents, customer emails and more to uncover patterns, keywords, concepts, topics and other attributes. Offer in- demand products and services, promote experiences and analyze competitor performance through text mining. ✓ Forecasting: Scrutinize historical data to predict future events. For instance, analyze past data to forecast retail sales during holiday seasons or energy consumption in summer. Leverage forecasting to determine budget allocation or anticipate expenses. ✓ Data Visualization: Create intuitive visuals to find patterns and correlations in datasets. Data visualization supports exploratory data analysis, modeling and forecasting. ✓ Optimization: Leverage simulation techniques like what-if analysis to determine the best-case scenario. Business Analytics may be defined as refining past or present business data using modern technologies. They are used to build sophisticated models for driving future growth. A general Business Analytics process may include Data Collection, Data Mining, Sequence Identification, Text Mining, Forecasting, Predictive Analytics, Optimization, and Data Visualization. Every business today produces a considerable amount of data in a specific way. Business Analytics now are leveraging the benefits of statistical methods and technologies to analyze their past data. This is used to uncover new insights to help them make a strategic decision for the future (simplilearn.com/, 2024). Business Intelligence, a subset of the Business Analytics field, plays an essential role in utilizing various tools and techniques such as machine learning and artificial intelligence technologies to predict and implement insights into daily operations. Thus, Business Analytics brings together fields of business management, and computing to get actionable insights. These values and inputs are then used to remodel business procedures to generate more efficiency and build a productive system (simplilearn.com/, 2024). 5  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 (Evans, 2021). Using Business Analytics. Tools and techniques of business analytics are used across many areas in a wide variety of organizations to improve the management of customer relationships, financial and marketing activities, human capital, supply chains, and many other areas. Leading banks use analytics to predict and prevent credit fraud. Investment firms use analytics to select the best client portfolios to manage risk and optimize return. 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 analytics 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. Even sports teams are using business analytics to determine both game strategy and optimal ticket prices. For example, teams use analytics to decide on ticket pricing, who to recruit and trade, what combinations of players work best, and what plays to run under different situations. Among the many organizations that use analytics to make strategic decisions and manage day-to-day operations are Caesars Entertainment, the Cleveland Indians baseball, Phoenix Suns basketball, and New England Patriots football teams, Amazon.com, Procter & Gamble, United Parcel Service (UPS), and Capital One bank. It was reported that nearly all firms with revenues of more than $100 million are using some form of business analytics (Evans, 2021). 6 Some common types of business decisions that can be enhanced by using analytics includes: o Pricing (for example, setting prices for consumer and industrial goods, government contracts, and maintenance contracts), o Customer segmentation (for example, identifying and targeting key customer groups in retail, insurance, and credit card industries), o Merchandising (for example, determining brands to buy, quantities, and allocations), o Location (for example, finding the best location for bank branches and ATMs, or where to service industrial equipment), o Supply chain design (for example, determining the best sourcing and transportation options and finding the best delivery routes) o Staffing (for example, ensuring the appropriate staffing levels and capabilities and hiring the right people—sometimes referred to as “people analytics”) o Health care (for example, scheduling operating rooms to improve utilization, improving patient flow and waiting times, purchasing supplies, predicting health risk factors), and many others in operations management, finance, marketing, and human resources—in fact, in every discipline of business. Various research studies have discovered strong relationships between a company’s performance in terms of profitability, revenue, and shareholder return and its use of analytics. Top-performing organizations (those that outperform their competitors) are three times more likely to be sophisticated in their use of analytics than lower performers and are more likely to state that their use of analytics differentiates them from competitors. However, research has also 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. So, no matter what your job position in an organization is or will be, the study of analytics will be quite important to your future success. You may find many uses in your everyday work for the Excel-based tools that we will study. You may not be skilled in all the 7 technical nuances of analytics and supporting software, but you will, at the very least, be a consumer of analytics and work with analytics professionals to support your analyses and decisions. For example, you might find yourself on project teams with managers who know very little about analytics and analytics experts such as statisticians, programmers, and economists. Your role might be to ensure that analytics is used properly to solve important business problems. Impacts and Challenges The benefits of applying business analytics can be significant. Companies report reduced costs, better risk management, faster decisions, better productivity, and enhanced bottom- line performance such as profitability and customer satisfaction. For example, 1- 800-Flowers.com used analytic software to target print and online promotions with greater accuracy; change prices and offerings on its Web site (sometimes hourly); and optimize its marketing, shipping, distribution, and manufacturing operations, resulting in a $50 million cost savings in one year. Business analytics is changing how managers make decisions.8 To thrive in today’s business world, organizations must continually innovate to differentiate themselves from competitors, seek ways to grow revenue and market share, reduce costs, retain existing customers and acquire new ones, and become faster and leaner. IBM suggests that traditional management approaches are evolving in today’s analytics-driven environment to include more fact-based decisions as opposed to judgment and intuition, more prediction rather than reactive decisions, and the use of analytics by everyone at the point where decisions are made rather than relying on skilled experts in a consulting group. Nevertheless, organizations face many challenges in developing analytics capabilities, including lack of understanding of how to use analytics, competing business priorities, insufficient analytical skills, difficulty in getting good data and sharing information, and not understanding the benefits versus perceived costs of analytics studies. Successful application of analytics requires more than just knowing the tools; it requires a high-level understanding of how analytics supports an organization’s competitive strategy and effective execution that crosses multiple disciplines and managerial levels (Evans, 2021). 8 Lesson 3. EVOLUTION OF BUSINESS ANALYTICS Technologies have been used as a measure to improve business efficiency since the beginning. Automation has played a considerable role in managing and performing multiple tasks for large organizations. The unprecedented rise of the internet and information technology has further boosted the performance of businesses. With advancement today, we have Business Analytics tools that utilize past and present data to give businesses the right direction for their future (simplilearn.com/, 2024). Analytical methods, in one form or another, have been used in business for more than a century. The core of business analytics consists of three disciplines: business intelligence and information systems, statistics, and modeling and optimization. Analytic Foundations 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 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. Business intelligence software can answer basic questions such as “How many units did we sell last month?” “What products did customers buy and how much did they spend?” “How many credit card transactions were completed yesterday?” Using BI, we can create simple rules to flag exceptions automatically; for example, a bank can easily identify transactions greater than $10,000 to report to the Internal Revenue Service. BI has evolved into the modern discipline we now call information systems (IS). Statistics has a long and rich history, yet only rather recently has it been recognized as an important element of business, driven to a large extent by the massive growth of data in today’s world. Google’s chief economist noted that statisticians surely have one of the best jobs. Statistical methods allow us to gain a richer understanding of data that goes beyond business intelligence reporting by not only summarizing data succinctly but also finding unknown and interesting relationships among the data. Statistical methods include 9 the basic tools of description, exploration, estimation, and inference, as well as more advanced techniques like regression, forecasting, and data mining. 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. A significant amount of research was carried on in public and private think tanks during the late 1940s and through the 1950s. As the focus on business applications expanded, the term management science (MS) became more prevalent. Many people use the terms operations research and management science interchangeably, so 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 various computer languages, and using them to find the best (“optimal”) solutions and decisions. INFORMS, the Institute for Operations Research and the Management Sciences, is the leading professional society devoted to OR/MS and analytics and publishes a bimonthly magazine called Analytics (http://analytics-magazine.org/). Digital subscriptions may be obtained free of charge at the Website. Modern Business Analytics Modern business analytics can be viewed as an integration of BI/IS, statistics, and modeling and optimization, as illustrated in Figure 1.1. While these core topics are traditional and have been used for decades, the uniqueness lies in their intersections. For example, data mining is focused on better understanding characteristics and patterns among variables in large databases using a variety of statistical and analytical tools. Many standard statistical tools as well as more advanced ones are used extensively in data mining. Simulation and risk analysis relies on spreadsheet models and statistical analysis to examine the impacts of uncertainty in estimates and their potential interaction with one another on the output variable of interest. 10  Figure 1.2: A Visual Perspective of Business Analytics Source: Business Analytics, Global Edition (2021) Decision support systems (DSSs) 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. DSSs have been used for many applications, including pension fund management, portfolio management, work-shift scheduling, global manufacturing and facility location, advertising budget allocation, media planning, distribution planning, airline operations planning, inventory control, library management, classroom assignment, nurse scheduling, blood distribution, water pollution control, ski-area design, police-beat design, and energy planning 11  A key feature of a DSS is the ability to perform what-if analysis—how specific combinations of inputs that reflect key assumptions will affect model outputs. What-if analysis is also used to assess the sensitivity of optimization models to changes in data inputs and provide better insight for making good decisions. Perhaps the most useful component of business analytics, which makes it truly unique, is the center of Figure 1.1— visualization. Visualizing data and results of analyses provides a way of easily communicating data at all levels of a business and can reveal surprising patterns and relationships (Evans, 2021). Software Support and Spreadsheet Technology Many companies, such as IBM, SAS, and Tableau Software, have developed a variety of software and hardware solutions to support business analytics. For example, IBM’s Cognos Express, an integrated business intelligence and planning solution designed to meet the needs of midsize companies, provides reporting, analysis, dashboard, scorecard, planning, budgeting, and forecasting capabilities. It is made up of several modules, including Cognos Express Reporter, for self-service reporting and ad hoc query; Cognos Express Advisor, for analysis and visualization; and Cognos Express Xcelerator, for Excel-based planning and business analysis. Information is presented to users in a context that makes it easy to understand; with an easy to-use interface, users can quickly gain the insight they need from their data to make the right decisions and then take action for effective and efficient business optimization and outcome. SAS provides a variety of software that integrate data management, business intelligence, and analytics tools. SAS Analytics covers a wide range of capabilities, including predictive modeling and data mining, visualization, forecasting, optimization and model management, statistical analysis, text analytics, and more (Evans, 2021). Although commercial software often has powerful features and capabilities, they can be expensive, generally require advanced training to understand and apply, and may work only on specific computer platforms. Spreadsheet software, on the other hand, is widely used across all areas of business and used by nearly everyone. Spreadsheets are an effective platform for manipulating data and developing and solving models; they support powerful commercial add-ins and facilitate communication of results. Spreadsheets provide a flexible modeling environment and are particularly useful when the end user is not the designer of the model. Teams can easily use spreadsheets and understand the logic upon 12 which they are built. Information in spreadsheets can easily be copied from spreadsheets into other documents and presentations. Spreadsheet technology has been influential in promoting the use and acceptance of business analytics. Spreadsheets provide a convenient way to manage data, calculations, and visual graphics simultaneously, using intuitive representations instead of abstract mathematical notation. Although the early applications of spreadsheets were primarily in accounting and finance, spreadsheets have developed into powerful general-purpose managerial tools for applying techniques of business analytics. The power of analytics in a personal computing environment was noted decades 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 (Evans, 2021). Lesson 4. TYPES OF BUSINESS ANALYTICS TECHNIQUES Business analytics techniques can be segmented in the following four ways: 1. Descriptive Analytics: This technique describes the past or present situation of the organization's activities. This analytics type lets you analyze historical data for a comprehensive view of business performance. Present KPIs and metrics in reports and dashboards to understand information quickly. Slice and dice data to gauge patterns, trends, outliers, anomalies and other points of interest. Descriptive analytics helps you spot anomalies. See how sales regions stack up and why a specific product loses value over time. These anomalies can prompt diagnostic analytics to understand the root causes of problems (selecthub.com, 2024). 2. Diagnostic Analytics: This technique discovers factors or reasons for past or current performance. 13  It examines the data to discover the reasons behind events. Determine the causes of trends and patterns by employing data discovery, mining and correlation analysis. Find answers to questions like, ‘Why do sales peak during a particular season? Why is the clickthrough rate low during specific times?’ through diagnostic analytics (selecthub.com, 2024). 3. Predictive Analytics: This technique predicts figures and results using a combination of business analytics tools. Leverage AI, machine learning and statistical techniques to create robust predictive models. These models assign a numerical value based on the occurrence or non- occurrence of specific events. For instance, healthcare organizations use predictive analytics to forecast patients likely to develop certain medical conditions and their progression (selecthub.com, 2024). 4. Prescriptive Analytics: This technique recommends specific solutions for businesses to drive their growth forward. Analyze past data and performance to offer recommendations to handle similar scenarios. It gauges potential decisions and their impact on outcomes while suggesting an optimal course of action. For example, a loan approval engine sanctions loans based on credit score, income and profession. A complete business analytics life cycle starts from raw data received from the devices or services, then collecting data in an unstructured type, then processing and analyzing data to draw actionable insights. These are then integrated into business procedures to deliver better outcomes for the future (simplilearn.com/, 2024). Lesson 5. BUSINESS ANALYTICS APPLICATIONS Business Analytics is now systematically integrated across several applications in the field of supply chain management, customer relationship management, financial 14 management, human resources, manufacturing, and even build smart strategies for sports too. Applications of Business Analytics in Various Industries (Terra, 2023) Although business analytics is being leveraged in most commercial sectors and industries, the following applications are the most common (Terra, 2023). 1. Banking - Credit and debit cards are an everyday part of consumer spending, and they are an ideal way of gathering information about a purchaser’s spending habits, financial situation, behavior trends, demographics, and lifestyle preferences. 2. Customer Relationship Management (CRM) - Excellent customer relations is critical for any company that wants to retain customer loyalty to stay in business for the long haul. CRM systems analyze important performance indicators such as demographics, buying patterns, socio-economic information, and lifestyle. 3. Finance - The financial world is a volatile place, and business analytics helps to extract insights that help organizations maneuver their way through tricky terrain. Corporations turn to business analysts to optimize budgeting, banking, financial planning, forecasting, and portfolio management. 4. Human Resources - Although HR is often the punchline of many office jokes, its value in keeping a company successful is not to be underestimated. Great businesses are composed of a great staff, and it’s HR’s job to not only find the ideal candidates but keep them on board. Business analysts help the process by pouring through data that characterizes high performing candidates, such as educational background, attrition rate, the average length of employment, etc. By working with this information, business analysts help HR by forecasting the best fits between the company and candidates. 5. Manufacturing - Business analysts work with data to help stakeholders understand the things that affect operations and the bottom line. Identifying things like equipment downtime, inventory levels, and maintenance costs help companies streamline inventory management, risks, and supply-chain management to create maximum efficiency. 15 6. Marketing - Which advertising campaigns are the most effective? How much social media penetration should a business attempt? What sort of things do viewers like/dislike in commercials? Business analysts help answer these questions and so many more, by measuring marketing and advertising metrics, identifying consumer behavior and the target audience, and analyzing market trends. As you can see, business analytics plays a valuable role in many different industries. You may also notice that some of the applications merge into each other, but that’s hardly surprising. By leveraging business analytics, multiple departments and teams can coordinate their efforts based on the information gathered and processed. It’s up to the business analyst to identify roadblocks and areas that need improvement, helping different departments to work together to achieve a common goal. Business Analytics Applications (Terra, 2023) 1. Customer Segmentation Customer segmentation is a vital business analytics application that helps companies group their customers based on shared characteristics such as demographics, buying behavior, and preferences. By analyzing customer data, businesses can tailor their marketing strategies, product offerings, and customer service to target specific segments effectively, increasing customer satisfaction and overall profitability. 2. Predictive Analytics Predictive analytics leverages historical and real-time data to forecast future trends and events. This application is used extensively in industries like finance, healthcare, and e- commerce for tasks such as predicting stock prices, patient outcomes, and product demand. It enables proactive decision-making, risk mitigation, and optimization of business operations. 3. Supply Chain Optimization Businesses utilize analytics to optimize their supply chains by analyzing data related to inventory levels, supplier performance, transportation logistics, and demand forecasting. 16 By identifying inefficiencies and bottlenecks in the supply chain, companies can reduce costs, improve product availability, and enhance overall operational efficiency. 4. Fraud Detection Fraud detection analytics employs advanced algorithms and machine learning models to identify and prevent fraudulent activities, such as credit card fraud, insurance fraud, and cyberattacks. By analyzing transactional data patterns and anomalies, organizations can minimize financial losses and maintain the trust of their customers. 5. Market Basket Analysis Market basket analysis involves examining customer purchase history to discover patterns in product co-purchases. Retailers use this application to optimize product placement, cross-selling, and promotional strategies. By understanding which products are frequently bought together, businesses can increase sales and enhance the customer shopping experience. 6. Churn Analysis Churn analysis focuses on identifying and reducing customer churn, which is the rate at which customers stop using a company's products or services. By analyzing customer behavior and feedback, businesses can implement retention strategies to retain valuable customers and reduce revenue loss. 7. A/B Testing A/B testing is a fundamental analytics application for optimizing digital marketing campaigns and website performance. It involves conducting controlled experiments by randomly assigning users to different versions of a webpage or marketing content. By comparing the performance of these versions, companies can make data-driven decisions to improve conversion rates and user engagement. 8. Employee Performance Analytics Employee performance analytics helps organizations evaluate the productivity and engagement of their workforce. By analyzing data on key performance indicators (KPIs), 17 attendance, and employee feedback, companies can make informed decisions about talent management, training, and workforce optimization. 9. Quality Control and Process Improvement In manufacturing and production industries, analytics is employed to monitor product quality, detect defects, and optimize production processes. By analyzing data from sensors and production lines, businesses can reduce defects, improve efficiency, and minimize waste. 10. Sentiment Analysis Sentiment analysis, also known as opinion mining, uses natural language processing and machine learning techniques to assess public sentiment and opinions from sources like social media, customer reviews, and surveys. Companies can gain insights into how their brand is perceived and use this information to shape marketing strategies and product development. Lesson 6. HOW DOES BUSINESS ANALYTICS WORK? Business analytics begins with several foundational processes before any data analysis occurs. A smaller sample data set gets used for initial analysis. Spreadsheets with statistical functions and sophisticated data mining and predictive modeling software are both examples of analytics tools. The raw data shows patterns and relationships. The analytical process then iterates until the business goal is achieved by posing new questions (simplilearn.com/, 2024). 18  Assessment Task Activity No. 1 1. Provide two examples of questions that business intelligence can address. ____________________________________________________________________________ ____________________________________________________________________________ 2. How do statistical methods enhance business intelligence reporting? ____________________________________________________________________________ ____________________________________________________________________________ 3. What is operations research/management science? ___________________________________________________________________________ ___________________________________________________________________________ 4. How does modern business analytics integrate traditional disciplines of BI, statistics, and modeling/optimization? ____________________________________________________________________________ ____________________________________________________________________________ 5. What are the components of a decision support system ___________________________________________________________________________ ___________________________________________________________________________ 6. Give at least two scenarios of using data. ___________________________________________________________________________ ___________________________________________________________________________ 19  Summary We all interact with data every day, though in most cases it’s been processed – whether by human analysts and/or using computers – to provide information in a form we can digest. This might be a chart or graph or a textual summary. Today, business analytics has become a buzzword for companies around the globe. Every business, irrespective of its size, is on a lookout for different ways to make sense of the vast amount of raw data available. This is because business analytics has been transforming the way companies function for over a decade now. From targeting the right customers and increasing sales to helping HR personnel select the right candidates and reducing overhead costs; there is hardly any sector where data analytics has failed to tap in. This module has introduced you the foundation of business analytics. We have discussed: ✓ What is Data? ✓ What is Business Analytics? ✓ Evolution of Business Analytics ✓ Types of Business Analytics Techniques ✓ Business Analytics Applications ✓ How Does Business Analytics Work? References o James R. Evans (2021) Business Analytics: Methods, Models, and Decisions: GLOBAL EDITION o What is Business Analytics: Fundamentals Every Beginner Needs to Know (last updated on July 4, 2024) retrieved from https://www.simplilearn.com/what-is-business-analytics article#:~:text=A%20general%20Business%20Analytics%20process,data%20in%20 a%20specific%20way. o Payal Tikait (May 2, 2024) What Is The Business Analytics Process? A Comprehensive Guide retrieved from 20  https://www.selecthub.com/business-analytics/business-analytics- process/#:~:text=Business%20analytics%20is%20the%20process,and%20make%20 data%2Ddriven%20decisions. o John Terra (October 2023) Business Analytics Applications and Notable Use Cases retrieved from https://www.simplilearn.com/business-analytics-applications-and-use-cases-article o Data and how we use it (2024) retrieved from https://www.sciencelearn.org.nz/resources/3266-data-and-how-we-use-it o What is Business Analytics: Fundamentals Every Beginner Needs to Know? From https://www.simplilearn.com/what-is-business-analytics- article#:~:text=A%20general%20Business%20Analytics%20process,data%20in%20 a%20specific%20way 21  MODULE 2 WHY BUSINESS ANALYTICS IS SO IMPORTANT FOR SUCCESS? Introduction What if you could analyze your past business performances and results, and use that information to prepare for the future? That is essentially what business analytics is all about. Business analytics entails the analysis of data to create predictive models, as well as the application of optimization techniques, and communicating the results to employees and customers. It utilizes a data-driven methodology to the business environment, and as such relying on statistics and data modeling to create insights for the business. In today’s business environment, every organization is looking for a way to make their decision making more efficient and business analytics gives them that advantage. What makes business analytics standout is the fact that it can be applied in several areas. Learning Outcomes At the end of this lesson, the student should be able to: 1. Explain why analytics is important in today’s business environment. 2. Understand the benefits of using business analytics. 3. Identify the potential careers, roles and responsibilities in business analytics. Lesson 1. IMPORTANCE OF BUSINESS ANALYTICS Business Analytics plays a vital role in transforming raw data into valuable insights that can inform decision-making. By using Business Analytics tools, organizations can gain a deeper understanding of the primary and secondary data emerging from their activities, enabling them to refine their processes and improve productivity. To maintain a competitive 22 edge, businesses need to stay ahead of their peers and leverage the latest tools to improve efficiency and generate more profits (https://exeedcollege.com/). ✓ Organizations employ Business analytics so they can make data-driven decisions. Business analytics gives business an excellent overview and insight on how companies can become more efficient, and these insights will enable such business optimize and automate their processes. ✓ Business analytics also offers adequate support and coverage for businesses who are looking to make the right proactive decisions. Business analytics also allows organizations to automate their entire decision-making process, so as to deliver real- time responses when needed. ✓ One of the apparent importance of business analytics is the fact that it helps to gain essential business insights. It does this by presenting the right data to work it. This goes a long way in making decision making more efficient, but also easy. ✓ Efficiency is one area of business analytics helps any organization to achieve immediately. Since its inception, business analytics have played a key role in helping business improve their efficiency. Business analytics collates a considerable volume of data in a timely manner, and also in a way that it can easily be analyzed. This allows businesses to make the right decisions faster. ✓ Business analytics help organizations to reduce risks. By helping them make the right decisions based on available data such as customer preferences, trends, and so on, it can help businesses to curtail short and long-term risk. There is no denying it that business analytics have come to change the dynamics of businesses and how they operate. Its importance cannot be overestimated, and with more and more companies relying on it for their decision-making process, it is something your business should consider incorporating if it hasn’t done so already. To stay competitive, companies need to be ahead of their peers and have all the latest toolsets to assist their decision making in improving efficiency as well as generating more profits. Now that we have added more value into our learning on What is Business Analytics by learning the importance, let us next understand its scope (Simplilearn, 2022). 23 Lesson 2. SCOPE OF BUSINESS ANALYTICS Business Analytics has been applied to a wide variety of applications. Descriptive analytics is thoroughly used by businesses to understand the market position in the current scenarios. Meanwhile, predictive and prescriptive analytics are used to find more reliable measures for businesses to propel their growth in a competitive environment. In the last decade, business analytics is among the leading career choices for professionals with high earning potential and assisting businesses to drive growth with actionable inputs (simplilearn.com/, 2024). The Future Scope of Business Analytics The use of business analytics is becoming increasingly important as organizations strive to gain a competitive edge and maximize profits. The future scope of business analytics is vast and will continue to expand as technology advances and the need for data- driven decisions increases. 1. Automation - Business analytics is becoming increasingly automated as organizations look to simplify processes and reduce costs. Automated solutions can collect, organize and analyze data quickly and accurately, giving businesses near real-time insights into their operations. This can help organizations make faster decisions with greater accuracy. 2. Big Data Analytics - As the amount of available data grows exponentially, businesses are turning to big data solutions for improved performance. By leveraging the power of big data tools such as Hadoop or Spark, companies can gain deeper insight into customer behavior, market trends, and more to optimize their strategies for maximum success. 3. Artificial Intelligence (AI) - AI technologies such as machine learning algorithms are being used more frequently in business analytics to detect patterns, make predictions and optimize decisions. AI-powered solutions are becoming increasingly sophisticated, making them invaluable for organizations seeking a competitive edge in the market. 24 4. Cloud Computing - Transforms how businesses handle data and analytics. By moving their analytical processes to the cloud, companies can reduce costs and improve scalability while accessing powerful tools for analysis and leveraging near real-time insights into their operations. 5. Internet of Things (IoT) - The IoT revolution is creating vast amounts of corresponding data that can be analyzed for improved performance. Companies can use IoT data to gain insights into customer behavior, optimize operations or develop new products and services based on customer needs and requirements. Business analytics is an ever-evolving field that businesses increasingly turn to for improved performance. By leveraging automation, big data analytics, AI, cloud computing, and IoT, organizations can gain deeper insight into their operations and make better decisions for long-term success. Lesson 3. BENEFITS OF BUSINESS ANALYTICS Primary Benefits ( Payal Tikait, 2024) Business analytics offers the following benefits: Figure 2.1 Business Analytics Benefits Source: Payal Tika (2024) 25 ✓ Make Data-driven Decisions: Business analytics allows you to make smarter decisions by analyzing the root causes of underlying issues. View manufacturing needs, marketing and sales outreach campaigns, supply chain processes and HR budgets objectively. ✓ Weave Compelling Stories: Business analytics solutions consume large amounts of data to turn it into beautiful visuals while telling effective stories. Derive accessible insights in a few clicks and uncover innovative ideas by viewing data from different angles. ✓ Generate What-if Scenarios: Leverage predictive analytics to create models that affect future business outcomes. Create what-if scenarios by replacing variables with different values to gauge results. ✓ Leverage Augmented Analytics: Augmented analytics expedites data processing and analysis through advanced machine learning and artificial intelligence. Find and explore information, ask relevant questions and obtain insights. According to Simplilearn.com (2024), Business Analytics brings actionable insights for businesses. However, here are the main benefits of Business Analytics: 1. Improve operational efficiency through their daily activities. 2. Assist businesses to understand their customers more precisely. 3. Business uses data visualization to offer projections for future outcomes. 4. These insights help in decision making and planning for the future. 5. Business analytics measures performance and drives growth. 6. Discover hidden trends, generate leads, and scale business in the right direction. Lesson 4. ROLES AND RESPONSIBILITIES IN BUSINESS ANALYTICS The primary duty of business analytics professionals is to gather and analyze data to affect the strategic choices that an organization makes. The following are some projects for which they could perform the analysis: 26  ✓ Identifying strategic opportunities from data patterns ✓ Identifying potential issues, the company might face and possible solutions ✓ Making a budget and business forecast ✓ Tracking business initiative progress ✓ Updating stakeholders on business objective progress ✓ Comprehending KPIs ✓ Comprehending regulatory and reporting requirements Employers typically look for the following skills when hiring for these positions: ✓ An understanding of stakeholder analysis ✓ Familiarity with process modeling ✓ Analytical problem-solving ✓ Oral and written communication skills ✓ Fundamental knowledge of IT systems, including databases ✓ Attention to detail ✓ Familiarity with business analytics tools and software ✓ The capacity to visualize data Lesson 5. CAREER IN BUSINESS ANALYTICS The role of Business Analytics professionals may change accordingly to meet organizational goals and objectives. Several individual profiles are closely associated with business analytics when dealing with data. In this competitive age, business analytics has revolutionized the procedures to discover intelligent insights and gain more profits using their existing methods only. Business Analytics Techniques also help organizations personalize customers with more optimized services and even include their feedback to create more profitable products. Large organizations today are now competing to stay top in the markets by utilizing practical business analytics tools. 27  Several business analytics tools are available in the market that offers specific solutions to match requirements. Professionals might need business analytics skills, like understanding and expertise of statistics or SQL to manage them. (simplilearn.com/, 2024). Business Analytics Careers according to Syracuse University, Business analytics students gain skills that are applicable and valuable across a wide range of industries. Employers in the commerce, government, nonprofit, service, and manufacturing sectors all seek workers with data literacy skills. As a result, it’s possible for business analytics professionals to find opportunities in a number of areas. While job titles and descriptions can vary by organization, common career opportunities for business analytics professionals include: ✓ Big data analytics specialists. ✓ Market research analysts. ✓ Logisticians. ✓ Financial analysts. ✓ Project management specialists and business operations specialists. ✓ Operations research analysts. ✓ Management analysts. ✓ Statisticians. ✓ Computer systems analysts ✓ Information security analysts ✓ Advertising, promotions, and marketing managers Career and Salary Trends in Business Analytics Since the beginning of the 21st century, a variety of new career options and employment paths emerged, which did not exist before. However, no career choice can match Business Analytics in salaries, learning, and training opportunities in everyday work. With businesses increasingly leveraging business analytics tools, multiple avenues are opening up for qualified business analysts in diverse sectors, including automotive, healthcare, retail, banking, hospitality, and aviation 28  For someone with a background in business analytics, there are many options. According to PayScale, some standard job titles and yearly salaries are as follows: Business systems analyst: $70,155 Business analyst: $69,785 Business analyst: $69,639 Senior business analyst: $86,050 Senior business analyst: $51,009 Assessment Task Activity No. 1 1. Excluding the given example in this Module. Give at least 10 jobs you can do with a Business Analytics with their roles and responsibilities. 29  Summary The Importance of Data Analytics: Information has become the currency of progress in our increasingly interconnected and data-driven world. The sheer volume of data generated daily is staggering, with sources ranging from social media interactions and online transactions to sensor data from many devices. However, the true value of data lies not in quantity but in quality and the ability to extract actionable insights from it. This is where data analytics emerges as a fundamental pillar in information management and decision-making. It is the process by which we transform raw, unstructured data into actionable knowledge, unveiling patterns, trends, and invaluable insights that guide our choices and strategies. Its significance reverberates across many sectors and disciplines, reshaping how organizations and individuals interact with data. This module discussed the parts of computer which includes the: ✓ Scope of Business Analytics ✓ Benefits of Business Analytics ✓ Roles and Responsibilities in Business Analytics ✓ Career in Business Analytics References o Why Business Analytics is so important for Success? from https://exeedcollege.com/blog/why-business-analytics-is-so-important-for-success/ o Scope of Business Analytics from https://emeritus.org/in/learn/scope-of-business- analytics/ o What is Business Analytics: Fundamentals Every Beginner Needs to Know? From https://www.simplilearn.com/what-is-business-analytics- article#:~:text=A%20general%20Business%20Analytics%20process,data%20in%20a%2 0specific%20way 30  MODULE 3 DIFFERENCE BETWEEN BUSINESS INTELLIGENCE AND BUSINESS ANALYTICS Introduction In the age of information, data is gold and businesses that can unearth, and make the best use of it find themselves far ahead of their competitors. Business Intelligence (BI) technology is a tool that allows modern businesses to leverage data to make sound, fact- based, and actionable decisions to improve their operations and profit margins. Business intelligence and business analytics are two terms that are often used interchangeably by professionals. But business experts frequently debate whether business intelligence is a subset of business analytics, or vice versa, and there is often an overlap between how the two fields are defined. Understanding the differences between business intelligence and business analytics can help leaders choose the appropriate tools and talent to help grow their businesses. Current and aspiring business students can also use this knowledge to assess what educational programs can prepare them best for a successful career in their chosen field. Learning Outcomes At the end of this lesson, the student should be able to: 1. Become familiar with the business analytics vs. data analytics, and data science. 2. Differentiate between Business Intelligence and Business Analytics 3. Identify the common challenges of Business Analytics and 4. List the examples and tools of Business Analytics. 31 Lesson 1. BUSINESS INTELLIGENCE vs. BUSINES ANALYTICS Making smart business decisions, identifying problems, and being profitable demands methods and tools that turn data into actionable insights. Business Intelligence (BI) and Business Analytics (BA) present data management solutions. While BI is often used as a foundation to answer complex questions, BA is more advanced. The two terms sound similar but serve different purposes. Understanding Business Intelligence vs Business Analytics is crucial to get the best from your data. Business Intelligence (BI) uses the past and present to identify trends and patterns in the organizational procedures, while Business Analytics determines the reasons and factors that led to present situations. Business Intelligence focuses mainly on descriptive analysis, while Business Analytics deals with predictive analysis. BI tools are part of Business Analytics that helps understand the Business Analytics process better (simplilearn.com/, 2024). Table 3.1 Business Intelligence vs Business Analytics CHARACTERISTICS BUSINESS INTELLIGENCE BUSINESS ANALYTICS Terms / definition Analyses history and present Analyses historical data to drive to drive current business current business. requirements. Business analytics is about Business intelligence is about predicting future outcomes of the understanding a company's actions taken by the company. past and present. Focus Business Intelligence tools Business analytics tools focus on focus on data management data analysis. Application Suitable for all large-scale Suitable for all companies where companies productivity and future growth are the goals Usage For present business For future business operations. operations. Business analytics typically focuses 32  Business intelligence typically on detailed analysis of specific focuses on enterprise-wide areas within an organization (such reporting across multiple as marketing or sales). departments and teams. Tools Used TIBCO Word processing PowerBI MS Visio SAP Business Objects MS Office Tools QlikSense Google docs Approach Business intelligence focuses Business analytics focuses on on descriptive statistics. predictive analytics and prescriptive analytics (the latter two of which can be used in conjunction with descriptive stats). Example BI is typically focused on BA is typically focused on presenting information in a presenting information in a way that way that makes it easy to makes it easy for people outside of understand for people within an organization (e.g., investors) to a particular organization (e.g., understand what's happening inside executives of it (and how they can benefit from those insights). Roles BI is mainly used by IT BA is mainly used by business departments and their departments and their consultants. vendors. What Is Business Intelligence? Business Intelligence is an infrastructure comprising strategies and technologies used in enterprise industries for collecting and analyzing the existing business data that gives insights into the historical, present, and predictive events of business operations. BI gives comprehensive business metrics to support better decision-making. BI frameworks present current, authentic, and visionary views of commercial operations. They mostly utilize the information gathered in an information stockroom/shop and sometimes operational information (simplilearn.com/, 2023). 33  What Is Business Analytics? Business Analytics is the use of strategies and technologies to explore and extract insights and performance from historical business information for successfully driving future business plans, meeting customer needs, and increasing productivity. It works on unused bits of knowledge and provides an understanding of business performance on the basis of information and measurable strategies (simplilearn.com/, 2023). Key Differences Between Business Intelligence and Business Analytics Now that you know the definitions of the two terms let's explore a complete comparison of business intelligence vs business analytics (simplilearn.com/, 2023). ✓ Current Circumstances vs Future Events The focus on the occurrence of events is the primary distinction between business intelligence vs business analytics. While BI concentrates on current and past events recorded in the data, BA focuses mostly on what is more likely to happen in the future. The two practices have different timelines for applying the results while using the same data. When your analysis concerns what is happening now and why Business Intelligence is your choice for data management, contrastingly, Business Analytics answers the question: What is likely to happen next? Use BI to form strategies for current situations and BA for strategies that would impact future operations. ✓ Structured Data vs Semi-structured Data BI applications are more suitable for structured data from enterprise applications like ERP and Financial Software Systems. It helps get insights from past financial transactions. Business Analytics application suits both unstructured and semi-structured data, transforms them into meaningful data before analysis, and then gets insights from that data with thorough predictive analytics. 34  ✓ Descriptive vs Predictive Approach Business Intelligence answers the two main questions: What has happened in the past? What is happening now and why? It is descriptive and gives you detailed information. On the other hand, Business Analytics is mostly about predictive analytics. It identifies patterns in business data, suggests why things are happening, and forecasts the likelihood of future events. Its goal is to predict future events based on what's already occurred. It helps formulate better decisions and be prepared accordingly. What is Big Data Analytics? (www.selecthub.com, 2024) Big data analytics is the blanket term for processing large quantities of data. For what purpose is irrelevant: it can be used to discover market, customer, social media, traditional media, geospatial and other trends and subsequent outliers. It can focus on internal data or environmental (Richard Allen, 2024). It allows for mass aggregation of data and fusing your internal metrics with whatever relevant environmental data you can get your hands on. This helps you reduce costs, make decisions quicker and predict trends (Richard Allen, 2024). Big data has four major components, known as the four V’s: ✓ Volume: the amount of data being processed. ✓ Variety: the different kinds of data being used. ✓ Velocity: the speed at which the data is processed and analyzed. ✓ Veracity: the accuracy of the data. These are the four major considerations for businesses looking to implement a big data analytics system. You need to be able to process a lot of data from different sources at 35 high speeds, and then have confidence in the reliability of the end result. From there, we can describe the three different data structure classifications when analyzing big data. Here’s a general overview: ✓ Structured: Highly organized quantitative data. The easiest to digest and use. ✓ Unstructured: Includes photos, videos, audio files, text, etc. Difficult to scrape information from, but more enriching than structured. ✓ Semi-structured: A blend of the two. For example, a cell phone photo with attached metadata. Understanding the limitations and benefits of the structure of the data you’re working with and what characteristics of the data need to be considered are essential to extracting the most useful information possible. Sorting out the structure and characteristics of big data opens a whole new realm of analytics and consequential intelligence that isn’t possible without such a volume of information. Some unique benefits of working with big data are listed in this chart: A point that used to fit into that chart but doesn’t as much these days is “develop a competitive advantage.” While using big data analytics software puts your business ahead of the pack that doesn’t, that group at the rear is dwindling in size, almost daily depending on the industry. For some sectors, such as financial services, the use of big data solutions is a prerequisite, not an advantage over your peers (Richard Allen, 2024). 36  Differences Between Them Big data analytics and business analytics carry a lot of similarities: they both bite off some data, chew it up and spit it out as some new form of cohesive, useful information. But they are distinct concepts with some key differences: Business analytics focuses primarily on operational statistics and internal analytics. Big data analytics contextualizes operational data in the much larger scope of industry and market data (Richard Allen, 2024). Because of the intricacy that comes with the volume and variety of big data it also has a much higher barrier to entry than business analytics. The simplest form can be accomplished with Microsoft Excel and some basic calculus knowledge. The most bare- bones big data analytics, however, requires comparatively sophisticated data science that will almost definitely require a specialist. Utilizing big data analytics requires knowledge of data manipulation, source compatibility (via APIs and other integrations), data translation and interpretation and other complex concepts, just to even get started. We’ll go more into the skillsets for each later (Richard Allen, 2024). In the same vein, business analytics is very human-focused, while big data analytics requires too much processing and attention to be conducted without automation processes. The latter requires help from machines at essentially every step of the process: from extract, transform, load to analysis to visualization to modeling predictive analytics. Business analytics, for the majority of its history and modern use, has been constituted and continues to be constituted by human inferences drawn from data. This, however, is changing, which we’ll also get to (Richard Allen, 2024). Lesson 2. BUSINESS ANALYTICS VS. DATA ANALYTICS Analytics has become a driving force for business development and transformation, providing organizations with the capabilities needed to create and implement new, creative strategies that improve customer experiences, enhance growth opportunities, and provide new revenue streams. But the term analytics is so broadly used that it can be difficult to 37 make distinctions in its purpose and applications. Data analytics and business analytics are great examples of this. The terms are often used interchangeably, yet the two are quite distinct from one another, as evidenced by the following examples. When a business is planning their sales strategies for an upcoming season or holiday, they might use business analytics to predict product demand so they can optimize stock and ensure they’re able to meet a specific business goal (Ronald Van Loon, 2023). However, with data analytics, that same hypothetical business might use data to discover that women between the ages of 18 and 24 are the most likely to buy those products—and, then personalize their marketing campaign accordingly (Ronald Van Loon, 2023). Both data analytics and business analytics involve the use of data to inform decision- making and ultimately prepare a business for the future. Let us now begin our learning about Business analytics vs Data analytics by understanding the terms well (Ronald Van Loon, 2023). Data Analytics: Uncovers Trends and Insights Data analytics is the process of analyzing and categorizing data—sorting, storing, cleansing, identifying patterns, and interpreting insights by using various statistical techniques, big data processing, and technology. One of today’s most popular and recognizable forms of data analytics is machine learning, which processes massive volumes of data and uncovers patterns within that data to make intelligent predictions and produce unique insights that answer a particular business question or solve a specific business problem. Data analysis is more technical than business analytics and requires the use of sophisticated analytics tools like Python and Tableau. Data findings must also be translated into meaningful information to present to different teams or to business leaders who need to be able to understand and interpret the insights easily. 38  Data analytics is a crucial practice for improving organizational or operational efficiencies and developing strategies to seize new business opportunities. (Ronald Van Loon, 2023). Business Analytics: Makes it Practical Business Analytics, a sub-division of business intelligence, focuses on the big picture of how data can be used to improve weak areas in an existing procedure or to add value or cost optimization in a specific business process. This may involve the use of reporting or financial analysis tools, data visualization tools, and data mining to improve specific business functions such as sales and marketing, for example. Business analytics focuses on creating solutions and solving existing challenges that are unique to the business and usually stays at the forefront of the data pipeline as opposed to data analytics, which is more focused on the backend. Successful business analytics applies data-derived insights to support decision-making processes and drive practical changes throughout the organization (Ronald Van Loon, 2023). Lesson 3. BUSINESS ANALYTICS VS. DATA SCIENCE Data science and business analytics have been applied in many industries, but perhaps the most prominent example is finance. In the 1990s, banks began using large customer information databases to predict which customers were likely to default on their loans. This made it possible for banks to offer credit cards with lower interest rates while still making money off them. Since then, data science and business analytics have spread across all areas of finance and business management. Companies like Google and Facebook have hired hundreds of data scientists to help them make their businesses more efficient by analyzing user behavior patterns. So what’s the difference between business analytics and data science? For starters, business analytics tend to be focused on making decisions about things that have already happened. Whereas, data science tends to be focused on making predictions about what will happen in the future! 39  What is business analytics? Business analytics collects, analyses, and interprets data to make better business decisions. The relevance of business analytics can be seen in virtually every industry. The ability to collect and analyze data allows companies to make decisions in real-time. For example, a retailer may need information about its customers’ purchasing patterns to determine what products need to be stocked on the shelves for the best sales results (onlinemanipal.com, 2024). What Is Data Science? Data science is the domain of study that deals with vast volumes of data using modern tools and techniques, including essential data science skills, to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models. The data used for analysis can come from many different sources and presented in various formats. Now that you know what data science is, let’s see the data science lifestyle (simplilearn.com/, 2024). Data science is the study of data, a field that has grown rapidly in the last few years. As we’ve gone from an analog world to a digital one, there’s been an increasing need for people who can collect and analyze information from large databases to make decisions and take action (onlinemanipal.com, 2024). Lesson 4. COMMON CHALLENGES OF BUSINESS ANALYTICS When attempting to implement a business analytics strategy, organizations may run into issues with both business analytics and business intelligence: ✓ Too many data sources: Business data is getting produced by a broad range of internet-connected devices. They frequently create various data types, which must get incorporated into an analytics strategy. ✓ Lack of skills: Some companies, mainly small and medium-sized businesses (SMBs), may find it tough to find candidates with the necessary business analytics knowledge and abilities. 40  ✓ Data storage limitations: Before determining how to process data, a company must decide where to store it (simplilearn.com/, 2024). According to Bill Baumann (2024), in his article: Challenges of Implementing Business Analytics: Is Your Company Prepared? Business analytics is evolving. Generative AI in ERP is becoming commonplace, and decision intelligence is morphing from nice-to- have to necessity. While these advancements are helping many organizations remain competitive, you must address foundational challenges before jumping on the train yourself. Today, we’re exploring common challenges of implementing business analytics and sharing strategies to overcome these problems. Learn how to overcome challenges in business analytics with these solutions. 1. Encouraging User Adoption Implementing business analytics software or ERP software requires employees to abandon their familiar workflows to adapt to the new system. Not everyone will be ready or willing to embrace that change. A comprehensive organizational change management (OCM) plan that prioritizes user needs and incorporates ongoing communication is key to successful adoption. This involves understanding user pain points, collecting feedback, and continuously refining solutions to ensure they deliver value. To make sure resistance doesn’t hinder your efforts, involve key stakeholders from the beginning of the ERP implementation. Then, once you’re in the design phase, show them how you’ve implemented their ideas. This will encourage them to support the project. 2. Selecting the Right Business Analytics Solution When you ask your employees what they want in a business analytics solution, expect to receive a slew of different answers. Everyone has their own pain points and their own ideas on how analytics can solve them. 41  We recommend isolating each user group and asking them to identify their key business analytics issues. When you approach this step intentionally, you can make sure the final solution is one that all teams can use. 3. Addressing Data Quality Issues Finding a solution that can manage large data volumes is essential, but it’s not enough. Robust data governance is equally critical. According to one survey, poor data quality can cost companies up to 31% of their revenue. Data cleansing, standardization, and governance are crucial not only for traditional analytics but also for the effective training of generative AI models. If quality issues start to affect your big data system, it can lead to inaccurate insights and forecasts. The problems can become even more serious as your teams start to integrate more and different types of data. To avoid this roadblock, make it a point to monitor and improve your data quality on a regular basis. Duplicate entries and typos are common, especially when you’re working with data from multiple sources. To keep your data as clean as possible, create a system that can match duplicates with data variances and report on typos. While it might take a while to develop, this tool can save your team time and help you catch problems before they snowball. 4. Ensuring Executive Buy-In When business intelligence (BI) first debuted, companies deployed systems that were based on large IT infrastructures. While these were advanced for their time, they ultimately delivered information in an inefficient manner. As you make the case for more advanced BI, you may encounter some pushback from your C-suite. They may wonder why they need to change the current setup or add to existing BI investments. Aligning your data strategy with business goals is essential for securing executive buy-in. Demonstrating the potential impact of data on specific business objectives can help garner support and resources for analytics initiatives. The key is to quantify the expected ROI and explain how it outweighs any associated risks or expenses. Discuss how the new solutions will deliver real-time workflow improvements and generate time savings. These data and visualizations 42  should speak for themselves, especially if your previous BI implementation has failed to meet expectations. 5. Contending With Ethical Issues and Bias As the algorithms behind BI tools become increasingly sophisticated, ethical concerns around data privacy, fairness, and potential bias arise. Companies must ensure: ✓ Transparency and explainability: Being transparent about how data is collected, used, and analyzed. ✓ Algorithmic fairness: Identifying and mitigating potential biases in data and algorithms to avoid discriminatory outcomes. ✓ Data privacy: Adhering to data privacy regulations like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) to protect user information. Lesson 5. BUSINESS ANALYTICS EXAMPLES AND TOOLS Many business analytics and business intelligence tools can automate advanced data analytics tasks. Here are a few examples of commercial business analytics software: ✓ Knime Analytics Platform includes machine learning and high-performance data pipelining ✓ Dundas Business Intelligence has automated trend forecasting and an intuitive interface ✓ Qlik's QlikView has data visualization and automated data association features ✓ Sisense is renowned for its data warehousing and dynamic text-analysis capabilities ✓ Splunk comes with a user-friendly interface and data visualization capabilities ✓ Tableau offers sophisticated capabilities for natural language processing and unstructured text analysis. ✓ Tibco Spotfire is an automated statistical and unstructured text analysis tool with powerful abilities. 43  Top 18 Business Analytics Tools for 2024 you can visit https://www.simplilearn.com/business-analytics-tools-article and watch Business Analytics Tools from https://www.youtube.com/watch?v=butoDQ0Yhv0&t=5s Organizations should consider the following factors when choosing a business analytics tool: ✓ The sources from which their data gets derived ✓ The kind of data that requires the analysis ✓ The tool's usability 44  Assessment Task Activity No. 1: 1. Excluding to the given topic and discuss in this Module kindly research the following: a. Business Intelligence vs. Business Analytics b. Business Analytics vs. Data Analytics c. Business Analytics vs. Data Science d. Common Challenges of Business Analytics (also give tips or solution to avoid the given challenges and problems) e. Business Analytics Examples and Tools Summary This module discussed the parts of computer which includes the: ✓ Business Intelligence vs Business Analytics is all about one being more advanced than the other. So, a firm command of the simpler– Business Intelligence is a prerequisite for diving into the more advanced– business analytics. In other words, BI tends to serve as a basis for BA. ✓ Data analytics is the process of analyzing data sets to make decisions about the information contained within them. The endeavor of business goals or insights is not a prerequisite for using data analytics. Business analytics is a part of this broader practice (simplilearn.com/, 2024). ✓ Analytics gets used by data science to guide decision-making. Data scientists investigate data using cutting-edge statistical techniques. They let the data's features direct their analysis. Data science isn't always required, even when sophisticated statistical algorithms get used on data sets. That's because genuine data science investigates solutions to open-ended questions. But business analytics aims to address a particular query or issue (simplilearn.com/, 2024). 45  ✓ Business analytics and data science are terms often used interchangeably, but the truth is far from it. Business analytics is mostly focused on helping companies make business decisions. It involves gathering data, analyzing it, and finding patterns that can be used to predict future outcomes. Data science is also a broader field than business analytics because it involves more than just predicting future events. Data scientists can use their skills to help a company understand its customers better or even make decisions about how to improve its products and services (onlinemanipal.com, 2024). References o Business Intelligence and Business Analytics from https://analytics.hbs.edu/blog/business-intelligence-vs-business-analytics/ o Ronald Van Loon (January 27, 2023) What’s the Difference Between Data Analytics and Business Analytics from https://www.simplilearn.com/business-analytics-vs-data- analytics-article o What is Business Analytics: Fundamentals Every Beginner Needs to Know? From https://www.simplilearn.com/what-is-business-analytics- article#:~:text=A%20general%20Business%20Analytics%20process,data%20in%20 a%20specific%20way o Difference between business analytics and data science (2024) from https://www.onlinemanipal.com/blogs/business-analytics-vs-data-analytics o Business Intelligence vs Business Analytics from https://www.simplilearn.com/business-intelligence-vs-business-analytics-article o Bill Baumann (Feb 1, 2024) Challenges of Implementing Business Analytics: Is Your Company Prepared? From https://www.panorama-consulting.com/challenges-of- implementing-business-analytics/ 46  MODULE 4 BUSINESS ANALYTICS PROCESS AND FRAMEWORK Introduction Process of Business Analytics: One way to conceptualize business analysis is as a research field that assists in determining business needs and problem-solving strategies. The creation of software or system components, process enhancements, organizational modifications, or the creation of strategic plans and policies are a few examples of these solutions. Business analysis’s goal is to find solutions that address the requirement for development. The business analysis process provides ideas and perceptions on how every project’s first framework is developed. It has the secret to directing project participants who carry out business modeling in a systematic way. Learning is a continuous process and there is always more to explore in the field of business analytics. Information, in its raw form, is not much useful for business decision making. It has to be collected, analyzed, and present din a way that is useful for decision making. In this module, we shall discuss the framework that allows us to turn this information into valuable data. We shall discuss how data becomes business value and explain the basic concepts on data analysis framework, data extraction, data warehousing and data analytics. Learning Outcomes At the end of this lesson, the student should be able to: 1. Have a better understanding of Business Analytics Process and its Frameworks. 2. Describe the three steps of the business analytics process. 3. Explain basic concepts on data analysis framework, data extraction, data warehousing and data analytics. 47 Lesson 1. BUSINESS ANALYTICS PROCESS The process of business analytics is an essential tool for interpreting and applying the vast amount of data your company collects and organizes. From customer behavior and conversion rates to revenue and business processes, the information generated by your company’s operations has to tell a helpful story to benefit you. Business analytics is the process that helps turn those data points into actionable insights. According to Marc J. Schniederjans, et.al. (n.d) Business Analytics Process: The complete business analytic process involves the three major component steps applied sequentially to a source of data (see Figure 4.1). The outcome of the business analytic process must relate to business and seek to improve business performance in some way. Figure 4.1 Business Analytics Process Source: Marc J. Schniederjans (n.d.)