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Course Name: BUSINESS ANALYTICS I Course Code: BQE Course Level: 1 Credit Units: 2 Credit Hours: 30
Course Description: This course is designed to introduce the following business analytics knowledge to students: (1) Quantitative data analysis (2) Business analytics modelling in the Excel software. This course teaches students the process of analysing big data and discovering new information to support management decision-making. Topics include the analysis of production data, analysis and management, and marketing research analysis
Course Objectives: To provide students with the ability to analyse the production data, analysis and management, and marketing research analysis
Learning Outcomes: Students should be able to implement analytical models in the software tools. In addition, students should be able to interpret the results of business analytics and their implications for business administration. According to the data analysis results, students should be able to make data-driven decisions to optimize the business process and address issues in business administration.
Course Content: Topic Sub Topics Hours Introduction (^) Introduction to Business Analytics 4 Visualization/ Data Issues (^) Organization/sources of data Importance of data quality Dealing with missing or incomplete data Data Classification Davenport and Harris article - “The Dark Side of Customer Analytics”
Estimation techniques
Time series and linear regression 6
Data Mining Introduction to data mining Data mining process Data mining tool XLMiner Cluster Analysis Market Basket Analysis
Basic models Spreadsheet Models 4
Total 30
Mode of Delivery: Lectures and coursework
Assessment Pattern Course work (in-class tests and course work assignments) 40% Final examination 60%
Reading List
- Albright C. and Winston W. L. (2019). Business Analytics: Data Analysis & Decision Making. 7 th^ Edition. Cengage Learning.
- Camm J. D., Cochran J. J., Fry M. J., Ohlmann J. W., Anderson D. R., 3rd^ Edition. Business Analytics. Cengage Learning.
- Evans J. R. Business Analytics by, 2nd^ Edition. Pearson. London.
- Ramesh Sharda R., Delen D., Efraim Turban E. (2017). Business Intelligence, Analytics, and Data Science: A Managerial Perspective , 4th Edition. Pearson.
- Siegel E. (2016). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Wiley.
- D. J. Power, C. Heavin, J. McDermott & M. Daly (2018) Defining business analytics: an empirical approach, Journal of Business Analytics, 1:1, 40-53, DOI: 10.1080/2573234X.2018.
- Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2022). Predictive analytics in business analytics: Decision tree. Advances in Decision Sciences, 26 (1), 1-29. Retrieved from https://www.proquest.com/scholarly- journals/predictive-analytics-business-decision-tree/docview/2674049708/se-
- Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value?. Information Systems Journal , 27 (3), 237 - 269. https://onlinelibrary.wiley.com/doi/pdf/10.1111/isj.
- Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics , 2 (1), 1-21. https://doi.org/10.1080/23270012.2015.
ML Machine Learning^ Computers learning from data. NLP Natural Language Processing A field of AI that interprets human language. SQL Structured Query Language A programming language used to manage databases.
INTRODUCTION TO BUSINESS ANALYTICS
Business analytics begins with a data set (a simple collection of data or a data file) or commonly with a database (a collection of data files that contain information on people, locations, and so on). As databases grow, they need to be stored somewhere. Technologies such as computer clouds (hardware and software used for data remote storage, retrieval, and computational functions) and data warehousing (a collection of databases used for reporting and data analysis) store data. Database storage areas have become so large that a new term was devised to describe them. Big data describes the collection of data sets that are so large and complex that software systems are hardly able to process them (Isson and Harriott, 2013, pp. 57–61). Isson and Harriott (2013, p. 61) define little data as anything that is not big data. Little data describes the smaller data segments or files that help individual businesses keep track of customers. As a means of sorting through data to find useful information, the application of analytics has found a new purpose. Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.
EVOLUTION OF BUSINESS ANALYTICS The journey of Business Analytics can be divided into four key stages, aligned with developments in technology, data availability, and business needs.
1. Traditional Analytics (Before 1990s)
🔹 Focus: Manual analysis & basic reporting
Data was collected manually (paper records, spreadsheets). Analysis was descriptive—focused on " What happened?" Tools: Excel, basic databases (e.g., dBase, Lotus 1-2-3)
Example : A company tracks monthly sales in Excel to compare regions.
2. Business Intelligence (1990s–2000s)
🔹 Focus: Descriptive and diagnostic analytics
BI systems emerged to automate reporting and dashboards. Focus shifted to understanding "Why did it happen?" Tools: SAP BI, Oracle BI, SQL, early versions of Tableau
Example : A manager uses a dashboard to view sales trends and spot underperforming products.
3. Advanced Analytics (2010s)
🔹 Focus: Predictive and prescriptive analytics
Explosion of data from web, mobile, and IoT devices Use of machine learning, data mining, and forecasting Shift toward real-time analytics and data-driven strategies Tools: Python, R, SAS, RapidMiner, Apache Spark
Example : An airline uses predictive models to set ticket prices dynamically based on demand forecasts.
Operations Optimization
Streamlines supply chain, logistics, and resource use
Reducing delivery time and costs Product Development Uses feedback and data to guide product features and improvements
Enhancing a mobile app based on usage data Human Resources Analytics
Manages talent, performance, and workforce planning
Predicting employee turnover
BENEFITS OF USING BUSINESS ANALYTICS
Faster and better decisions Increased revenue Increased efficiency and productivity Cost reduction and resource optimization Better customer experiences Competitive edge in the market
- Improved Decision-Making. Business Analytics provides factual, data-driven insights that reduce guesswork in decision-making.
📌 Example: Amazon uses customer purchase history and browsing behavior to recommend products. These recommendations are driven by analytics models, leading to higher sales conversions.
- Increased Operational Efficiency. By analyzing workflows and performance data, businesses can identify inefficiencies and streamline operations. 📌 Example: FedEx uses predictive analytics to optimize delivery routes and reduce fuel usage, saving costs and improving delivery speed.
- Enhanced Customer Experience. Analytics helps companies understand customer preferences, behavior, and pain points, allowing them to personalize services. 📌 Example: Netflix analyzes viewing habits to recommend shows tailored to individual users, increasing viewer engagement and retention.
- Competitive Advantage. Organizations that use analytics effectively can respond faster to market changes and outperform competitors. 📌 Example: Zara , the fashion retailer, uses real-time sales data from stores to adjust inventory and product offerings, keeping pace with trends faster than traditional retailers.
- Risk Reduction and Management. Business Analytics can identify patterns that indicate risk, allowing companies to take preventative action.
📌 Example: Banks use credit scoring models based on BA to assess the risk of lending to a customer. This reduces default rates and financial losses.
- Cost Reduction. Analytics can reveal areas where resources are wasted and suggest cost-saving strategies.
📌 Example: Walmart uses analytics to manage its vast supply chain. It forecasts demand more accurately,
reducing overstock and understock issues and lowering storage costs.
- Better Forecasting and Planning. Predictive models help businesses anticipate future trends, sales, or resource needs. 📌 Example: Airlines like Delta use predictive analytics to set ticket prices dynamically based on demand forecasts, time of booking, and competitor pricing.
- Improved Product and Service Development. Analytics helps identify what customers want, guiding new product features and improvements.
📌 Example: Spotify uses analytics to see which features users engage with most. This informs future updates and music curation features.
- Real-Time Monitoring and Alerts. Real-time analytics can trigger alerts when something goes wrong, enabling faster responses.
📌 Example: E-commerce platforms use real-time analytics to detect unusual traffic or transactions that might indicate fraud, enabling instant action.
- Data-Driven Culture and Accountability. Organizations that rely on data for decisions promote transparency and accountability among teams.
📌 Example: Google encourages a data-driven approach across all departments, ensuring every strategy is backed by measurable insights.
BUSINESS ANALYTICS (BA) VERSUS BUSINESS INTELLIGENCE (BI) 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 select the appropriate tools and talent to drive business growth. Current and aspiring business students can also use this knowledge to assess what educational programs can best prepare them for a successful career in their chosen field.
Business intelligence (BI) is a set of technological processes for collecting, managing and analyzing organizational data to yield insights that inform business strategies and operations.
BI tells you what is happening in your business right now and what has already happened. BA goes further to tell you why it happened, what might happen next , and what you should do about it. BI is like a rearview mirror – it helps you see where you’ve been. BA is like a GPS – it helps you choose the best route moving forward. For example, BI : Shows last quarter's sales performance by region and product. BA : Analyzes trends and predicts which products will sell more next quarter and suggests optimal pricing strategies.
Summary of Business Analytics vs. Business Intelligence Aspect Business Intelligence (BI) Business Analytics (BA) Purpose Understand what happened in the past and monitor current performance
Understand why it happened and predict what will happen
Customer Service
Analyze average response times and satisfaction scores
Predict churn and recommend retention strategies
Challenges in Business Analytics Despite the growing adoption of Business Analytics, businesses face several technical, organizational, and ethical challenges that can limit the effectiveness of analytics initiatives.
- Data Quality Issues Inaccurate, incomplete, or inconsistent data leads to unreliable insights. Garbage in, garbage out: even the best tools can’t fix bad data.
📌 Example: A retail company has duplicate customer entries, leading to incorrect segmentation and marketing targeting.
- Data Silos Data stored in isolated systems across departments can’t be easily accessed or integrated. Limits holistic analysis and slows decision-making.
📌 Example: Sales and customer support teams have separate databases, making it hard to understand the full customer journey.
- Lack of Skilled Talent Skilled data analysts, data scientists, and business users are in high demand but short supply. Many employees lack the data literacy needed to interpret analytics results.
📌 Example: A company invests in a powerful analytics tool, but employees don’t know how to use or interpret its output.
- High Implementation Costs Setting up an analytics infrastructure (software, hardware, training) can be expensive and time-consuming, especially for small businesses.
📌 Example: A startup struggles to afford enterprise-level BI platforms or hire full-time analysts.
- Privacy and Security Concerns Collecting and analyzing personal or sensitive data raises issues around data protection and compliance (e.g., GDPR, HIPAA).
📌 Example: A healthcare organization must ensure patient data is anonymized and secure during analysis.
- Resistance to Change Some employees and managers resist adopting analytics, preferring intuition or traditional methods over data-driven decisions.
📌 Example: A sales manager ignores predictive sales forecasts and continues to set targets based on past experience.
- Complexity of Tools and Models Many analytics tools are technically complex, making them hard to understand or implement correctly. Advanced models like machine learning may be seen as a "black box."
📌 Example: A model predicts customer churn, but decision-makers don’t trust it because they don’t understand how it works.
- Poor Integration with Business Strategy Analytics efforts fail if they’re not aligned with business goals or KPIs. Insights may be interesting but not actionable.
📌 Example: A report shows website traffic growth, but the business goal is customer retention — making the insight irrelevant.
- Overreliance on Data Not all decisions should be purely data-driven. Data may lack context, leading to wrong conclusions if not combined with human judgment.
📌 Example: A model suggests cutting a product line due to low sales — but fails to account for its strategic brand value.
TYPES OF BUSINESS ANALYTICS
- Descriptive Analytics: As the name implies, this type of analytics describes the data it contains. An example would be a pie chart that breaks down the demographics of a company’s customers.
Descriptive Analytics answers the question: “What happened?” It involves the collection, organization, and presentation of historical data to describe business performance.
Purpose: To summarize past events. To provide reports, dashboards, and visualizations. To identify trends, patterns, and anomalies.
Techniques & Tools: Data aggregation e.g., summarizing data (e.g., total monthly sales) and data mining. Reporting techniques (Statistical Summaries : Mean, median, mode, standard deviation) and tools (Excel, SQL queries). Data Visualization techniques (Charts, dashboards, heatmaps) and tools (Tableau, Power BI, QlikView).
Example Use Cases: A retail company reviews sales data from the last quarter.
tool will break and intervening before it occurs, or knowing when changing demographics or psychographics will positively or negatively impact their product lines.
Predictive Analytics answers the question: “What is likely to happen?” It uses historical data and statistical algorithms to forecast future events.
Purpose: To anticipate trends and outcomes. To support proactive decision-making. To identify risks and opportunities in advance.
Techniques & Tools: Statistical modeling: regression (Predicting numerical outcomes e.g., sales), time series forecasting (Predicting future values based on historical data). Machine learning algorithms: classification (Grouping data into categories (e.g., churn vs. loyal customers), clustering. Tools: Python (scikit-learn, TensorFlow), R, SAS, IBM SPSS, Azure ML.
Example Use Cases: Predicting customer behavior like churn or purchase likelihood. Forecasting product demand for inventory management. Credit scoring for loan approvals.
Benefits: Enables forward-looking strategies. Helps mitigate risks by anticipating issues. Improves resource allocation and planning.
- Prescriptive Analytics: These analytics help organizations make decisions about the future based on existing information and resources. Every business can use prescriptive analytics by reviewing their existing data to make a guess about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.
Prescriptive Analytics answers the question: “What should we do?” It provides recommendations and decision options based on predictions.
Purpose: To suggest optimal actions to achieve desired outcomes. To simulate different scenarios and assess their impact. To automate decision-making where appropriate.
Techniques & Tools:
Optimization algorithms (linear programming, integer programming). Simulation and scenario analysis. Artificial intelligence and reinforcement learning. Tools: IBM CPLEX, Gurobi, AnyLogic, AI frameworks.
Example Use Cases: Determining the best pricing strategy to maximize profits. Optimizing delivery routes to reduce costs and time. Recommending personalized marketing offers.
Benefits: Drives actionable insights. Supports complex decision-making. Improves operational efficiency and profitability.
Summary Table Type Key Question
Techniques Tools Example Use Case
Main Benefit
Descriptive What happened?
Reporting, dashboards
Excel, Power BI, Tableau
Monthly sales report
Understanding past performance Diagnostic Why did it happen?
Drill-down, correlation, regression
SAS, Python, BI tools
Analyzing sales drop
Root cause analysis
Predictive What will happen?
ML, forecasting Python, R, SAS Predicting customer churn
Forecasting and risk reduction Prescriptive What should we do?
Optimization, simulation
IBM CPLEX,
AI frameworks
Optimizing pricing strategy
Actionable recommendations
BUSINESS ANALYTICS TOOLS AND TECHNIQUES
PART 1: Business Analytics Tools Business analytics tools fall into several categories based on their primary function:
- Data Visualization & Dashboard Tools Tool Description Use Case Example Tableau Interactive dashboards and visual analytics Sales dashboards by region or product Power BI Microsoft’s business intelligence platform KPIs monitoring and report automation QlikView Data visualization and self-service BI Trend analysis and drill-down visual reports Google Data Studio Free cloud-based dashboard tool Website traffic and marketing performance
- Data Analysis & Statistical Tools Tool Description Use Case Example Excel Spreadsheet for basic analytics, graphs, and formulas Budgeting, forecasting, and trend analysis
📌 Use Case: Visualizing customer orders over time to identify trends.
- Diagnostic Analytics Techniques Root Cause Analysis: Identifying the cause of a performance issue Correlation Analysis: Understanding relationships between variables Drill-Down Analysis: Exploring data at deeper levels
📌 Use Case: Analyzing why sales dropped in a specific region.
- Predictive Analytics Techniques Regression Analysis: Predicting numerical outcomes (e.g., sales) Classification Models: Grouping data into categories (e.g., churn vs. loyal customers) Time Series Forecasting: Predicting future values based on historical data Clustering: Grouping similar customers or behaviors
📌 Use Case: Forecasting demand for clay tiles in the rainy season (for Uganda Clays Limited).
- Prescriptive Analytics Techniques Optimization Models: Finding the best decision (e.g., resource allocation) Simulation: Testing different scenarios (e.g., inventory levels) Decision Trees: Evaluating decision paths based on conditions What-if Analysis: Exploring the impact of potential decisions
📌 Use Case: Determining the most cost-effective delivery routes.
- Advanced Techniques (Emerging) Machine Learning Algorithms: Neural networks, decision forests, ensemble models Natural Language Processing (NLP): Analyzing text or speech data Sentiment Analysis: Interpreting emotions from customer feedback AI-Powered Insights: Automated discovery of patterns or anomalies
📌 Use Case: Using NLP to analyze customer reviews for product improvement.
Choosing the Right Tool or Technique Business Objective Recommended Tools Techniques Monitor performance Power BI, Tableau Descriptive dashboards, KPIs Forecast demand R, Python, SAS Regression, time series Segment customers KNIME, RapidMiner, Python Clustering Identify customer churn SPSS, Azure ML, Excel Classification models, decision trees Optimize logistics Gurobi, Excel Solver, Alteryx Prescriptive modeling, optimization Analyze feedback or reviews Python (NLP), MonkeyLearn Sentiment analysis, text mining
KEY ROLES IN BUSINESS ANALYTICS
Business Analytics is a multidisciplinary field that combines business understanding, data analysis, and technology. Here’s a breakdown of the main roles:
- Business Analyst (BA) Main Role: Acts as a bridge between business stakeholders and the technical team.
Responsibilities: Understand business needs and define analytics requirements. Translate business problems into data questions. Design reports, KPIs, and dashboards. Facilitate communication between departments.
Skills Required: Strong domain knowledge Communication and critical thinking Tools: Excel, Power BI, Tableau
Example in Uganda Clays Limited (UCL): A business analyst defines metrics to track sales performance by region and works with IT to implement dashboards.
- Data Analyst Main Role: Processes and analyzes data to generate insights.
Responsibilities: Clean, transform, and analyze data. Perform descriptive and diagnostic analytics. Build visualizations and reports.
Skills Required: SQL, Excel, Python or R Data visualization (Tableau, Power BI) Statistical analysis
Example in UCL: A data analyst identifies seasonal trends in brick and tile sales using historical sales data.
3. Data Scientist
Main Role: Builds advanced models to make predictions and recommendations.
Responsibilities: Perform predictive and prescriptive analytics. Develop and train machine learning models. Work on big data, deep learning, and AI projects.
Responsibilities: Set vision and priorities for analytics projects. Manage cross-functional teams. Measure ROI of analytics initiatives.
Skills Required: Leadership, strategic thinking Business acumen and technical understanding Communication and stakeholder management
Example in UCL:
The analytics manager decides to invest in a new BI platform and leads the implementation to improve reporting across departments.
- Domain Expert / Subject Matter Expert (SME) Main Role: Provides deep industry or company-specific knowledge.
Responsibilities: Guide analytics teams in interpreting data within the business context. Validate assumptions and hypotheses. Help define relevant KPIs and metrics.
Skills Required: In-depth industry knowledge (e.g., manufacturing, sales) Analytical mindset Collaboration and mentoring
Example in UCL: A production manager helps the analytics team understand the impact of kiln capacity on delivery timelines.
Summary Table Role Primary Focus Tools/Skills Business Analyst Bridging business and analytics Excel, Power BI, Requirements Data Analyst Data wrangling and visualization SQL, Tableau, Excel Data Scientist Predictive/prescriptive modelling Python, R, ML, AI tools Data Engineer Data infrastructure and pipelines SQL, ETL, Big Data tools Governance Officer Data privacy and compliance Legal frameworks, data ethics Analytics Manager Strategy and team leadership Management, BI tools Domain Expert (SME) Business context and validation Industry knowledge
BUSINESS ANALYTICS PROCESS
Business Analytics is a systematic process that transforms raw data into meaningful insights to drive business decisions. The process ensures that analytics initiatives are focused, reliable, and aligned with strategic goals.
- Problem Definition What It Is Precisely articulating the business question, issue, or opportunity. Setting clear objectives, scope, and success criteria.
Why It Matters Defines the direction and focus of the entire analytics project. Avoids wasted effort on irrelevant data or analysis.
Activities Engage stakeholders to understand business context. Translate business questions into analytical objectives. Prioritize problems based on impact and feasibility.
Example A telecom company wants to reduce customer churn. Objective: Identify key factors leading to churn and develop a retention strategy.
Best Practices Use SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound). Document assumptions and constraints early on.
- Data Collection What It Is Gathering data relevant to the problem from internal and external sources.
Sources of Data Internal: ERP, CRM, sales, finance, customer support systems. External: Social media, market research, third-party data, IoT devices.
Challenges Data may be fragmented or stored in silos. Ensuring data privacy and compliance (GDPR, HIPAA).
Example Collect customer transaction data, call logs, complaint records, and demographic info.
Best Practices Define required data attributes and granularity. Verify data availability and access permissions.
