BBA Unit 1 Notes PDF

Business Analytics BBA Semester III 2024-25 Business Analytics Notes UNIT I Introduction to Business Analytics Business analytics is the process of using data to make better decisions. Data Management, data visualizations, predictive modelin...

Business Analytics BBA Semester III 2024-25 Business Analytics Notes UNIT I Introduction to Business Analytics Business analytics is the process of using data to make better decisions. Data Management, data visualizations, predictive modeling, data mining, forecasting simulation, and optimization are some of the tools used to create insights from data. Business analytics can be applied to various areas of business, including sales, marketing, finance, operations, and customer service. By analyzing data from these areas, organizations can identify trends, patterns, and correlations that can help them make informed decisions and optimize their business processes. Types of Business Analytics : There are four types of Business Analytics : 1) Descriptive Analytics : Descriptive Analytics is used to analysis historical data to determine the response of a unit over a set of given variables. It is the interpretations of historical data to better understand changes that occurred in a business. This type of analytics can be used to gain an overall picture of how a business is performing and is often used alongside predictive and prescriptive analytics. Common insights include year- over-year comparisons, the number of users, and revenue per Investor. It tracks key performance indicators (KPIs) for a better understanding of the present state of a business. A key performance indicator (KPI) is a quantifiable metric that measures performance over time for a specific goal. KPIs are a critical part of business intelligence (BI) and are used to help businesses make better decisions. Examples of Descriptive Analytics : Summarizing past events Exchange of data Social media usage Reporting general trends. BBA Semester 3 Simran Kadbe  Business Analytics 2) Diagnostic Analytics : Diagnostic analytics is a data analytics technique that uses past data to identify the reasons behind events, outcomes, or behaviors. It's also known as root cause analysis. Diagnostic Analytics focuses on the past performance which help to understand why something has happened in the past. Using techniques such as drill- downs, data mining data discovery, and correlations, you can solve the driving factors. Examples of Diagnostic Analytics : Examining market demand Identifying technical issues Explaining customer behavior Improving organization culture 3) Predictive Analytics : Predictive analytics focuses on forecasting future outcomes by answering the question: "What is likely to happen?" It uses historical data, machine learning algorithms, and statistical models to predict future trends and behaviors. This analytics type is essential for risk management, market analysis, and forecasting future sales. Predictive Analytics uses all the past gathered data to tell what likely happen on initial level. The Prediction of the possible outcome is made using statistical models and machine learning techniques. For Example : Forecasting future outcomes such as sales or demand. Predicting credit risk in financial markets. Anticipating customer churn or retention rates Trading BBA Semester 3 Simran Kadbe  Business Analytics 4) Prescriptive Analytics : Prescriptive Analytics generates information to handle similar future situation relying on past performance. It use several tools, statistics, and machine learning algorithm for available internal data and external data. It gives you insight about what may happen, when, why. For example : Tracking Fluctuating product prices Price modeling Suggest the best course of action Terminology of Business Analytics : Business Analytics is the process of collecting, organizing, analyzing, and interpreting data to gain insights that can be used to make informed business decisions. It involves using statistical and quantitative analysis techniques to extract meaningful insights from data and using these insights to improve business performance. Some of the techniques used in business analytics include data mining, predictive analytics, data visualization, and statistical analysis. These techniques can be used to generate reports, dashboards, and visualizations that provide actionable insights for business decision-makers. Business analytics is a subset of business intelligence, which is a data-management discipline that includes collecting, storing, and analyzing business data. Business analytics uses a variety of tools and methodologies, including: Data mining: Sorting through large amounts of data to identify patterns and trends Predictive modeling: Using historical data to estimate future outcomes Data visualization: Creating visual representations of data analysis, such as charts, tables, or graphs Aggregation: Gathering and organizing data before analysis BBA Semester 3 Simran Kadbe  Business Analytics Challenges for Business Data Analytics : The following are some of the challenges that organizations may face with business data analytics: Making decisions on topic, scale, or scope for a data initiative can be tricky. Determining which data to measure and capture to achieve business objectives can prove challenging. Finding data that creates value may be difficult; not all data helps make better decisions. Even when the data source is identified, defining the specific subset of data needed can be complex. Poor or unknown quality of data, especially historical. Data integration and accessibility. Data being placed in disparate systems, and of varying format and quality. Business stakeholders not being comfortable with the rapid changes occurring in the business data analytics space. Difficulty bringing business stakeholders to a shared understanding on value when sharing data assets across business domains. Lack of experience or knowledge for those completing the analysis as well as the managers receiving the results. Change in organizational culture required to trust insights gleaned from data over experience and intuition. Business managers finding it challenging to structure data teams. Difficulty finding the right tools. BBA Semester 3 Simran Kadbe  Business Analytics Types of Data Used in Business Analytics The data utilized in business analytics comes in different forms, each offering unique insights into various aspects of business operations: Structured Data: Structured data refers to organized and easily manageable information that fits neatly into predefined categories. This type of data often resides within databases and spreadsheets and includes information like sales figures, transaction records, and customer demographics. Semi-Structured Data: Semi-structured data has a semblance of organization but doesn't adhere strictly to predefined formats. Examples include JSON files (Javascript files) and XML documents (Excel Files). This type of data often contains rich contextual information that can be valuable for analytics. Unstructured Data: Unstructured data is more complex and lacks a predefined structure. It includes text, images, audio, and video content. Social media posts, customer reviews, and multimedia content fall under this category. Extracting meaningful insights from unstructured data requires advanced techniques like natural language processing and image recognition. Different types of Tools used in Business Analytics : 1. Excel – Excel is most important tool used for Business Analytics. 2. Microsoft Power BI – Power BI is a data visualization tool and help user to create interactive reports. 3. Tableau – Tableau is another data visualization tool which connect to data sources and create reports, visualization, maps and dashboards. 4. Board -Another top-rated business analytics tool is Board which is best known because of its analytics model that permits users to create custom intuitive and interactive user reports and dashboards. 5. Domo – It is cloud-based business intelligence and analytics platform, allows companies to access and analyze data from various sources. Besides that, it also facilitates integration with other tools and platforms and offers a wide range of data visualization choices BBA Semester 3 Simran Kadbe  Business Analytics Applications of Business Analytics : 1. Finance Business Analytics assists financial managers in managing their finances optimally and then taking relevant measures. Implementing business analytics in various sectors of finance (such as investment banking and budgeting) can prove to be highly fruitful for the finance industry. It helps in building future strategies for a new product by observing similar products and methodologies. In addition to this, business analytics can also be used to predict future loan defaulters. 2. Production management and Inventory Management is a key element in every organization. It aims to enhance the profits and productivity of an organization all the while trying to reduce overall costs. Business Analytics serves as a great tool for management and manufacturing. It is involved in every phase of product development. It supports analyzing the inventory measures and designing business solutions that are most suitable for products. It can help determine the costs and gauge the expected sales of products. This way the organizations can adapt to the latest styles and opportunities in the industry. Hence, business analytics stands as a boon for the diverse sectors of management, be it inventory management or product management. 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. By identifying inefficiencies and bottlenecks in the supply chain, companies can reduce costs, improve product availability, and enhance overall operational efficiency. BBA Semester 3 Simran Kadbe  Business Analytics 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. Customer Relationship Management (CRM) Customer Relationship Management or CRM is the process of building and managing the organization’s relationships as well as interactions with customers. Business analytics can be used in customer relationship management to understand the customer base better and therefore, implement corresponding strategies. This helps significantly drive sales and amplifies the organization’s profits. Customers’ purchasing patterns, needs, buying behaviors, issues, feedback, and all the other indicators can be obtained and analyzed through business analytics methodologies. These indicators can then be used to foster long-lasting and loyal relationships between clients and the organization. 7. Marketing Marketing, when combined with business analytics can prove to be one of the best strategies an organization can implement. Business analytics helps the organization to know its users, their needs, behaviors, and purchasing styles to design and modify suitable plans and schemes. Sales can be optimized and user experience can be enhanced. Business analytics can help marketers know their target audience and their interest. It can also be used to evaluate and determine how well a product or a marketing strategy is performing in the market. Considering these factors, organizations can modify their strategies and implement better planning. BBA Semester 3 Simran Kadbe  Business Analytics Business Analytics Process : Business analytics is the process of using data to improve business decisions. It involves collecting, cleaning, and analyzing data to identify trends, patterns, and insights that can be used to improve business performance. The business analytics process is critical because it can assist businesses in: Increase revenue and profitability Reduce costs Improve customer satisfaction Make better decisions Gain a competitive advantage There are Seven steps in Business analytics Process : Fig 1.1: Business Analytics Process Step 1: Defining the Business Needs Step 2: Exploring Data Step 3: Analyzing the Data Step 4: Predicting what is Likely to Happens (Predictive Modelling) Step 5: Optimizing Step 6: Making Decision and Measuring the Outcome. Step 7: Updating the System with the Results of the Decision. BBA Semester 3 Simran Kadbe  Business Analytics Step 1: Defining the Business Needs The first stage in the business analytics process involves understanding what the business would like to improve on or the problem it want to solved. Sometimes, the goal is broken down into smaller goals. Relevant data needed to solve these business goals are decided upon by the business stakeholders, business users with the domain knowledge and the business analyst. At this stage, key questions such as, “what data is available”, “how can we use it”, “do we have sufficient data” must be answered. Step 2: Exploring Data This stage involves cleaning the data, removing missing data, removing outliers, and transforming combinations of variables to form new variables. Time series graphs are plotted as they are able to indicate any patterns or outliers. The removal of outliers from the dataset is a very important task as outliers often affect the accuracy of the model if they are allowed to remain in the data set. As the saying goes: Garbage in, garbage out (GIGO)! Once the data has been cleaned, the analyst will try to make better sense of the data. The analyst will plot the data using scatter plots (to identify possible correlation or non-linearity). He will visually check all possible slices of data and summarise the data using appropriate visualisation and descriptive statistics (such as mean, standard deviation, range, mode, median) that will help provide a basic understanding of the data. At this stage, the analyst is already looking for general patterns and actionable insights that can be derived to achieve the business goal. Mean : Mean of a data set is calculated by adding all numbers in the data set and then dividing by the number of values in the set. The Formula for mean : Mean = (Sum of data values) / (Number of data values) Median : The median is the middle value when a data set is ordered from least to greatest. The median is the middle number in a data set when the numbers are listed in order. The formula for the median is: When 'n' is odd, median = ((n + 1)/2)th data value n= numbers of data value. When 'n' is even, median = Average of (n/2)th value and its next value BBA Semester 3 Simran Kadbe  Business Analytics Mode : The value that appears most frequently in a set of numbers. The mode is the value that occurs most often in a data set. To find the mode, count how often each number appears and the number that appears the most times is the mode. Standard Deviation : The standard deviation formula is used to find the deviation of the data value from the mean value i.e. it is used to find the dispersion of all the values in a data set to the mean value. There are different standard deviation formulas to calculate the standard deviation of a random variable. Range : The range in statistics for a given data set is the difference between the highest and lowest values. Formula : Range(X) = Max(X) – Min(X) Step 3: Analyzing the Data After cleaning and preparing the data. The next step is Analyzing the data. This involves applying various statistical methods to identify trends, correlations and patterns that can use to understand business problems. Applying Statistical Methods: Statistical analysis tools play a important role in deriving the meaningful insights from data. Identifying Patterns and insights : Patterns and correlation in data can be discovered through careful analysis. These might reveal hidden relationship between customer behavior and purchasing patterns, operational inefficiencies or market trends. Using Business Analytical tools and software : modern business analytics use specialized software tools that simplify the analytics process. These tools including R, Python, SAS, SPSS, Power BI, Qlik, Domo, Board and Tableau. It will provide diverse set of statistical functions, data visualization capabilities, and machine learning algorithms. BBA Semester 3 Simran Kadbe  Business Analytics Step 4: Predicting what is Likely to Happens (Predictive Modelling) We take insights from analysis phase, predictive modeling techniques such as decision tree, neural networks and logistic regression are used to forecast future trends, behaviors and outcomes. Multiple models are evaluated based on accuracy, performance metrics, and alignment with organizational goals to select the most robust and reliable predictive model. Step 5: Optimizing (Solution Optimization) The optimization phase is where the insights gained from analysis and prediction are translated into actionable strategies. This step are using optimization techniques, testing various scenarios, and ultimately deciding on the most effective course of action to achieve the defined business objectives. Implementing Optimization Techniques: Optimization techniques aim to find the best possible solution given a set of constraints. In the context of business analytics, this could mean increasing revenue, lowering costs, improving efficiency, or optimizing resource allocation. These techniques can range from simple linear programming to more complex algorithms like genetic algorithms or simulated annealing. Scenario Analysis and Simulation: Scenario analysis involves exploring different “what-if” scenarios to understand the potential impact of various decisions on the business. This can be accomplished using simulation models that mimic real-world processes and allow for the testing of various strategies under different conditions. By simulating different scenarios, businesses can evaluate the potential risks and rewards of different choices and make more informed decisions. Choosing the Best Course of Action: Based on the outcomes of optimization techniques and scenario analysis, the most promising course of action is chosen. This decision should be based on a combination of quantitative and qualitative data, such as the organization’s risk tolerance and strategic objectives. The chosen course of action should be aligned with the overall business goals and have the highest potential for achieving the desired outcomes. BBA Semester 3 Simran Kadbe  Business Analytics Step 6: Making Decision and Measuring the Outcome. The analyst will then make decisions and take action based on the derived insights from the model and the organizational goals. An appropriate period of time after this action has been taken, the outcome of the action is then measured. Decision-making based on analysis and prediction: With a thorough understanding of the data, predictive models, and optimised scenarios, stakeholders can now make informed decisions. The decision-making process should be data-driven, considering the potential risks and rewards of each option. It’s important to involve relevant stakeholders from different departments to ensure that the decision aligns with the overall business strategy. Implementing the chosen solution: Once a decision is made, the next step is to implement the chosen solution. This could involve changes to operational processes, marketing strategies, product offerings, or resource allocation. Effective implementation requires careful planning, clear communication, and coordination across different teams to ensure a smooth transition. Monitoring and measuring results against targets: After implementation, it’s crucial to continuously monitor the results and measure them against the predefined targets. Tracking relevant metrics and KPIs is necessary to assess the solution’s effectiveness. If the results deviate from the expected outcomes, adjustments may be necessary. Regular monitoring and evaluation ensure that the solution remains effective and helps identify areas for further improvement. BBA Semester 3 Simran Kadbe  Business Analytics Step 7: Updating the System with the Results of the Decision. Finally the results of the decision and action and the new insights derived from the model are recorded and updated into the database. Information such as, ‘was the decision and action effective?’, ‘how did the treatment group compare with the control group?’ and ‘what was the return on investment?’ are uploaded into the database. The result is an evolving database that is continuously updated as soon as new insights and knowledge are derived. Feedback Loop for Continuous Improvement: A feedback loop is essential for adapting to changing business landscapes and improving the accuracy of future analyses. By reviewing past decision results on a regular basis, analysts can identify what worked well and what could be improved. This feedback is then used to refine models, processes, and data collection methods, leading to more accurate insights and better decision-making. Updating Models and Processes Based on Outcomes: As new data becomes available and business environments change, the models and processes used in business analytics must be updated. This ensures that the insights generated remain relevant and accurate. By incorporating the feedback loop, organizations can continuously improve their analytical capabilities and stay ahead of the competition. Ensuring Data and Insights are Current and Relevant: Data is the lifeblood of business analytics. Keeping data up to date and of high quality is critical for ensuring the accuracy of insights. Outdated or irrelevant data can lead to flawed conclusions and misguided decisions. Regular data audits and updates are essential to ensure that the analysis is based on the most current and reliable information. BBA Semester 3 Simran Kadbe  Business Analytics Relationship of Business Analytics and Organization : The BA process can solve problems and identify opportunities to improve business performance. In the process, organizations may also determine strategies to guide operations and help achieve competitive advantages. Typically, solving problems and identifying strategic opportunities to follow are organization decision-making tasks. The latter, identifying opportunities, can be viewed as a problem of strategy choice requiring a solution. It should come as no surprise that the BA process described in Section 1.2 closely parallels classic organization decision-making processes. As depicted in Figure 1.2, the business analytic process has an inherent relationship to the steps in typical organization decision-making processes. Fig 1.2: Comparison of business analytics and organization decision-making processes BBA Semester 3 Simran Kadbe  Business Analytics The organization decision-making process (ODMP) developed by Elbing (1970) and presented in Figure 1.2 is focused on decision making to solve problems but could also be applied to finding opportunities in data and deciding what is the best course of action to take advantage of them. The five-step ODMP begins with the perception of disequilibrium, or the awareness that a problem exists that needs a decision. Similarly, in the BA process, the first step is to recognize that databases may contain information that could both solve problems and find opportunities to improve business performance. Then in Step 2 of the ODMP, an exploration of the problem to determine its size, impact, and other factors is undertaken to diagnose what the problem is. Likewise, the BA descriptive analytic analysis explores factors that might prove useful in solving problems and offering opportunities. The ODMP problem statement step is similarly structured to the BA predictive analysis to find strategies, paths, or trends that clearly define a problem or opportunity for an organization to solve problems. Finally, the ODMP’s last steps of strategy selection and implementation involve the same kinds of tasks that the BA process requires in the final prescriptive step (make an optimal selection of resource allocations that can be implemented for the betterment of the organization). The decision-making foundation that has served ODMP for many decades parallels the BA process. The same logic serves both processes and supports organization decision-making skills and capacities. BBA Semester 3 Simran Kadbe  Business Analytics Business Analytics and Decision Making Process ; Business analytics helps organizations make informed decisions by analyzing data to uncover patterns, trends, and correlations. This data-driven approach can help businesses: Improve efficiency: Identify areas to reduce costs or increase revenue Gain a competitive edge: Understand competitors' operations and identify areas for improvement Make strategic choices: Growth and maintain a competitive edge Make evidence-based decisions: Avoid relying on intuition alone Following are the steps used in making relationship between business analyatics organizations decision making process : (Figure 1.2) 1) Perception of disequilibrium : Observer and become aware of potential problem situation. 2) Diagnostic process : Diagnostic analytics uses Diagnostic process. It is a data analytics technique that uses past data to identify the reasons behind events, outcomes, or behaviors. It's also known as root cause analysis. It attempts to understand what is happening in a particular situation. 3) Problem Statement : Identify and state problems and solution strategies in relation to organization goals and objectives. 4) Solution Strategy selection : Select optimal courses of action for the organization from the strategies determine previously. 5) Implementation: Implement the strategy. BBA Semester 3 Simran Kadbe  Business Analytics Here’s how business analytics supports decision making in businesses:- Provides a Better Customer Experience Analytics plays a important role in decision making as it helps discover patterns from both employees as well as customers, allowing leaders to understand and interpret their interactions. This allows the leaders to work with the relevant departments to enhance the overall customer experience. Improves Overall Performance By analyzing data, businesses can develop deeper into business operations and their efficiency. This not only helps maximize time and resource allocation but also ensures better performance across the entire company. Makes the Most Out of Consumer Patterns In an increasingly customer-centric era, organizations are being forced to collect and analyze consumer information and data with their behaviour, interests, etc. To remain competitive, businesses must take consumer insights to redefine their marketing and sales strategy. Conducts Better Risk Assessment and Management Another key area that business analytics lends a hand is risk assessment and management. Whether it’s related to structured data or unstructured, analytics help forecast potential issues and threats. So by using this data, you can improve decision making in a crisis, eradicating the reactive style of management completely. Simplify accounting processes Business analytics can help accounting professionals make informed decisions about budgeting and investments. It's crucial that an organization supports all decisions about financial assets with logic and statistical data that guarantees the organization doesn't suffer any significant losses. Insights from business analytics reveal more in-depth information about an organization's financial performance and help professionals decide how to manage assets. For example, data might show that an organization uses too much of its profit on various expenses, such as electricity bills and overtime. These revelations allow professionals to make decisions about minimizing unnecessary costs and saving the organization money in the future. BBA Semester 3 Simran Kadbe  Business Analytics Business analytics Roles in Decision Making : Business analysts are the primary users of business analytics. These professionals develop documentation and system plans that help improve organizations' efficiency. They identify and analyze various business processes to make informed recommendations about business practices. Business analysts examine ways organizations can reduce costs and save money. They also simplify complex information so professionals in the organization can understand specific data. Some business analytics programs offer training to professionals about using systems effectively. Some specific job roles that business analysts have include: Business analyst Business process analyst Business system analyst Data analyst Functional architect IT business analyst System analyst Usability or UX analyst Here are few of the most significant ways in which business analytics can influence a company’s decision-making: Forecasting and Prediction: Business analytics may assist organizations with forecasting future outcomes and making predictions based on historical data. This may involve estimating future sales, finding growth opportunities, and anticipating future market trends. Customer Analytics: By studying customer data, businesses may better comprehend the demands, behaviors, and preferences of their customers. This data can be utilized to enhance consumer engagement and loyalty, optimize marketing campaigns, and find new revenue streams. Operational Analytics: Business analytics can be utilized to examine operational data and find process optimization and efficiency enhancement opportunities. This can assist firms in reducing expenses, enhancing quality, and increasing output. BBA Semester 3 Simran Kadbe  Business Analytics Financial Analytics: Business analytics can assist organizations in analyzing financial data in order to discover potential risk and opportunity areas. This may involve examining financial performance measures, projecting income and expenses, and identifying cost-cutting opportunities. Competitive Intelligence: Business analytics may assist organizations to gain insights into the strategies, strengths, and weaknesses of their competitors. This data can be utilised to guide corporate decisions and generate competitive and effective strategies. BBA Semester 3 Simran Kadbe