Introduction to Business Analytics PDF

Unit 1: Introduction to Business Analytics Department of Information Technology 1 What is Business Analytics? Business analytics is the process of transforming data into insights to improve business decisions. Data management, data visualization,...

Unit 1: Introduction to Business Analytics Department of Information Technology 1 What is Business Analytics? Business analytics is the process of transforming data into insights to improve business decisions. Data management, data visualization, predictive modeling, data mining, forecasting simulation, and optimization are some of the tools used to create insights from data. Department of Information Technology 2 At its core, business analytics involves a combination of the following: identifying new patterns and relationships with data mining; using quantitative and statistical analysis to design business models; conducting A/B and multi-variable testing based on findings; forecasting future business needs, performance, and industry trends with predictive modeling; and communicating your findings in easy-to-digest reports to colleagues, management, and customers. Department of Information Technology 3 Types of Business Analytics Department of Information Technology 4 Types of Business Analytics 1. Descriptive analytics: used mainly in business intelligence applications. The goal is to describe what happened and what actions need to be taken. 2. Predictive analytics: uses advanced statistical techniques, predictive models, and forecasting to answer the question, "what is going to happen in the future?" 3. Prescriptive analytics: extends the previous type of analytics to recommend the best action to take next to achieve the best possible result. 4. Autonomous analytics: uses advanced machine learning and AI to learn from the data and automatically apply the action that yields the best result. Department of Information Technology 5 Business Analytics Tools Spreadsheet software like Excel for fast data insights and quick sharing with other team members. Programming languages like R or Python for data mining, analysis, predictive modeling, and forecasting. Data visualization tools like Power BI or Tableau display historical and current data trends and statistics. Department of Information Technology 6 How Data Analytics Influences Business Decisions Organization-wide optimization may encompass: shaping and evaluating future company decisions based on the performance of past initiatives or market trends; examining individual departments’ performance within an organization and influencing their growth efforts; monitoring employees’ performance and productivity; determining current and future staffing needs and the market skills needed to perform these roles effectively; assessing and predicting how well potential investments will perform; identifying demand for a particular product or service based on market trends and consumer behavior; scheduling release dates for new products and media; Department of Information Technology 7 Business Analytics with Excel You will explore the principles of business analytics using tools in Microsoft Excel. To complete the activities, you will need the Microsoft Excel desktop app. We will also use the Solver add-in for Excel. (Check MOLE on how to load Solver add-in for Excel.) Department of Information Technology 8 Introduction to Spreadsheet Models What is spreadsheet modelling? Spreadsheet modelling is the creation of various models using spreadsheet software like MS Excel. The software is used to organise data and apply formulas to understand the reasons for various events. Models are also used to predict events in the future. Spreadsheets are very versatile as they allow you to enter different data types and apply various formulas to arrive at a desired outcome. Department of Information Technology 9 Features Of Spreadsheet Models Formulas And Functions: allow you to create unique formulas that will help you analyse the available data and help you get the desired outcome. Enabling Visualisation: One of the most important features of spreadsheet modelling is visualisation. You can show the results of your analysis using various visual aids like graphs and charts. Format The Way You Want: You can separate information and create headers. It is also possible to merge cells and make them bigger to accommodate large amounts of data without disturbing the other cells. Department of Information Technology 10 Scenario 1: Sales Data Analysis in Excel Dataset includes: Date: Sales date. Product: The name of the product sold. Sales Quantity: Number of units sold. Sales Revenue: Total revenue generated from sales. Region: The region where the sale was made. Department of Information Technology 11 Scenario 1: Sales Data Analysis in Excel Department of Information Technology 12 Basic Analysis we can apply: 1. Sorting and Filtering: sort the data by different columns, such as sorting by Sales Revenue to see which product or region generated the most revenue. 2. Simple Calculations: a. Total Sales Revenue: calculate the total sales revenue by summing up the Sales Revenue column. b. Average Sales Quantity: calculate the average number of units sold for each product using the AVERAGE function. Department of Information Technology 13 Basic Analysis we can apply: 3. Creating a Pivot Table: basic pivot table to summarize data. Or a Pivot Table that shows total sales revenue by region or by product. 4. Visualizing Data: create a simple chart (e.g., a bar chart or pie chart) to visualize the total sales by product or region. We can interpret the chart and identify which product is performing best or which region generates the most revenue. Department of Information Technology 14 Interpretation and Recommendation Now, we can identify simple business recommendation based on the analysis. Which product should the company focus on promoting based on sales revenue? Which region shows the most potential for increasing sales? Department of Information Technology 15 Scenario 2: Customer Demographics Analysis Department of Information Technology 16 Basic Analysis we can apply: 1. Sorting and Filtering: a. sort the data by Total Purchase to see which customers are the highest spenders. b. Create filters to view customers by gender or region, helping us understand the distribution of their customer base. 2. Simple Calculations: a. Average Age: Calculate the average age of the customers using the AVERAGE function. b. Total Sales by Region: Use the SUMIF function to calculate the total purchase amount by region. Department of Information Technology 17 Basic Analysis we can apply: 3. Creating a Pivot Table: to analyze customer data by gender, region, or age group. shows the total purchase amount by gender or by region. 4. Visualizing Data: create a simple chart (e.g., a bar chart or pie chart) to customer demographics, such as the distribution of customers by age group or the total purchase amount by region. Department of Information Technology 18 Interpretation and Recommendation Now, we can identify key customer segments based on the analysis we did. Which age group spends the most? Are there any noticeable differences in spending between male and female customers? Which region has the highest sales? Department of Information Technology 19  Introduction to Descriptive Analysis Department of Information Technology 20 What is Descriptive Statistics? Department of Information Technology 21 What is Descriptive Statistics? Definition: Descriptive statistics summarize or describe the essential features of a dataset. Measures of Central Tendency: Mean, Median, Mode Measures of Dispersion: Range, Variance, Standard Deviation Key Uses: To organize and summarize data. To provide insights about data patterns and distributions. Forms the foundation for further statistical analysis. Department of Information Technology 22 Central Tendency – What is it? Definition: Central tendency refers to the value around which data points tend to cluster. Why it matters: It gives us a "typical" value in the dataset. Department of Information Technology 23 Mean (Average) Definition: The sum of all data points divided by the number of data points. Formula: Example: Dataset: [2, 4, 6, 8, 10] Key Use: Gives the "average" value in business analytics, such as average sales per month. Department of Information Technology 24 Median Definition: The middle value when data points are arranged in ascending or descending order. Example: Dataset: [1, 3, 5, 7, 9] Median = 5 If the dataset has an even number of values, take the average of the two middle numbers. Key Use: Useful when the dataset has outliers or skewed data (e.g., income distribution). Department of Information Technology 25 Mode Definition: The most frequently occurring value(s) in the dataset. Example: Dataset: [1, 2, 2, 3, 4] Mode = 2 There can be more than one mode (bimodal, multimodal). Key Use: Indicates the most common category or product in business. Department of Information Technology 26 Measures of Dispersion – What is it? Definition: Dispersion refers to how spread out the data points are in a dataset. Why it matters: It helps in understanding the variability or consistency in the data. Variability: Variability tells you how far apart points lie from each other and from the center of a distribution or a data set. Variability is also referred to as spread, scatter or dispersion Department of Information Technology 27 Range Definition: The difference between the highest and lowest value in a dataset. Formula: Range = Maximum value - Minimum value Example: Dataset: [5, 8, 12, 20] Range = 20 - 5 = 15 Key Use: Helps in assessing the spread of data, such as the range of sales revenue over different months. Department of Information Technology 28 Variance Definition: A measure of how far each data point in the dataset is from the mean. Formula: μ is the mean of the dataset, X is each individual data point. Example: Calculate variance for the dataset [1, 2, 3, 4, 5]. Key Use: Used in finance to measure risk (e.g., stock price variability). Department of Information Technology 29 Standard Deviation Definition: The square root of the variance, indicating the average distance of data points from the mean. Formula: Example: If the variance is 16, the standard deviation = 4. Key Use: Helps understand the consistency of data in business. A low standard deviation means data points are close to the mean, indicating consistency (e.g., in product quality). Department of Information Technology 30 Data Distribution Definition: The way data points are spread across different values. Types of Distributions: Normal Distribution: Symmetrical, bell-shaped curve (e.g., height of people). Skewed Distribution: Data leans to the left or right (e.g., income distribution). Bimodal Distribution: Two peaks (e.g., sales in different seasons). Key Use: Understanding the shape of distribution helps in forecasting and decision-making. Department of Information Technology 31 Skewness Definition: A measure of asymmetry in data distribution. Right-skewed: Mean > Median (e.g., income distribution). Left-skewed: Mean < Median. Example: Sales data where few large sales cause the mean to be higher than the median. Department of Information Technology 32 Applications in Business Analytics Sales Performance: Mean and standard deviation of sales help set realistic targets. Customer Segmentation: Median income can define customer categories. Product Performance: Mode shows the most popular products. Risk Management: Standard deviation measures variability in financial returns. Department of Information Technology 33 Summary Central Tendency: Mean, Median, Mode provide typical values. Dispersion: Range, Variance, and Standard Deviation indicate variability. Data Distribution: Understand how data points are spread, whether symmetrically or skewed. Applications: These descriptive statistics form the foundation for understanding data trends and making business decisions. Department of Information Technology 34