Practical Applications of Data Analytics Chapter 2 PDF

Practical Applications of Data Analytics Chapter 2  Understand - Understand business intelligence (BI) Explain - Explain how BI supports decision- making Learning Outline Objectives...

Practical Applications of Data Analytics Chapter 2  Understand - Understand business intelligence (BI) Explain - Explain how BI supports decision- making Learning Outline Objectives - Outline key BI tools Identify - Identify essential BI skills Describe - Describe BI applications across various industries Key Components of Data Analytics  Business Intelligence (BI) - An umbrella term encompassing various IT applications designed for analyzing and presenting business data. - Functions: - Collecting and analyzing data - Communicating results to decision-makers - Importance: - Data is the backbone of enterprises in a data-centric economy. - Data-driven decisions are more effective than intuition-based decisions.  Business Intelligence (BI) - Data is viewed as a valuable resource for generating insights and gaining a competitive edge Example: A retail company uses BI tools to analyze sales data, identifying trends and patterns to optimize inventory management and enhance customer satisfaction.  BI for Better Decisions - Data-Driven Culture: - Reduces uncertainty and risk in decision-making. - Enables managers to make more informed decisions with lower risk levels. - Enhances decision speed in a hypercompetitive environment. Example: A company monitors social media comments to quickly address negative feedback, preventing potential damage to its reputation, while also leveraging positive comments for marketing opportunities.  Operational vs. Strategic Decisions Operational Decisions - Characteristics: - Routine, tactical, focused on efficiency. - Often involve repetitive tasks. - Example: Upgrading software to improve system performance or updating the company website for better user experience. - Benefit: Past data analysis aids in making better operational decisions. Automating decisions using models, such as financial loan approvals based on decision trees.  Operational vs. Strategic Decisions - Impact the long-term Strategic Decisions - Characteristics: direction of the company. - Example: Expanding into a - Benefit: Generate new new market or targeting a ideas and strategies from - Often lack clear-cut goals new customer base. data mining patterns. Evolve and immediate impact. Conducting 'what if' over time with new data analyses for various insights. scenarios.  BI Tools - Variety: Ranges from simple tools to sophisticated systems. - Spreadsheets: Microsoft Excel for data organization and basic analysis. - Dashboards: IBM Cognos, Tableau, Power BI for real-time data visualization and interactive reporting. - Data Mining: IBM SPSS Modeler, Weka (open source) for complex data analysis and pattern recognition. Example: A financial services firm uses Tableau to create interactive dashboards that provide real-time insights into market trends and customer behaviors.  BI Skills - Business Domain Knowledge: Understanding the specific industry and business processes. - Statistics Knowledge: Ability to analyze and interpret data accurately. - Coding Skills: Proficiency in programming languages (e.g., Python, R) for data manipulation and analysis. - Critical Thinking: Open-mindedness to think outside the box and consider multiple perspectives. - Problem Selection: Choosing valuable and impactful data mining problems, considering resource intensiveness. Example: A data analyst in healthcare leverages their medical knowledge and statistical skills to predict patient outcomes and optimize treatment plans.  Customer Relationship Management - Maximize ROI: BI and Data Targeted marketing campaigns based on customer data. Mining - Improve Retention: Churn analysis to Applications identify and address factors leading to customer loss. Across - Maximize Value: Cross-selling and up- Industries selling opportunities identified through data analysis. - Example: An e- commerce platform uses BI to analyze purchase history and recommend personalized products to customers, increasing sales and customer satisfaction.  Healthcare and Wellness - Diagnose Disease: Predictive analytics BI and Data for early detection of diseases. Mining - Predict Risk: Risk assessment models Applications to identify high-risk patients. Across - Treatment Effectiveness: Industries Analyzing patient data to evaluate treatment outcomes. - Example: A hospital uses BI to track patient recovery rates and adjust treatment protocols for better outcomes.  Education - Student Enrollment: Recruitment and BI and Data retention strategies based on data analysis. Mining - Course Offerings: Optimizing course Applications schedules to meet student demand. Across - Fundraising: Targeted campaigns Industries to alumni and donors. - Example: A university uses BI to analyze enrollment trends and improve student retention by identifying at-risk students and providing support.  Retail - Inventory Optimization: Managing stock levels to meet demand without BI and Data overstocking. - Store Layout: Mining Analyzing customer flow to optimize Applications product placement. Across - Logistics: Planning for seasonal Industries demand fluctuations. - Example: A supermarket chain uses BI to forecast demand for seasonal products and optimize inventory levels, reducing waste and increasing sales.  Banking - Loan Applications: Automating approvals based on BI and Data credit scores and other criteria. Mining - Fraud Detection: Identifying Applications suspicious transactions using Across data analysis. - Customer Value: Industries Cross-selling and up- selling financial products. - Example: A bank uses BI to analyze transaction data and detect fraudulent activities, preventing financial losses.  Financial Services - Predict Market Changes: Forecasting bond and stock price movements. - Event Impact Analysis: Assessing how events affect market behavior. - Fraud Prevention: Identifying and preventing fraudulent trading activities. - Example: An investment firm uses BI to predict stock price fluctuations and make informed trading decisions.  BI and Data Mining Insurance - Forecast Claim Costs: Predicting future claims Applications for better business planning. Across - Rate Plans: Determining optimal insurance rates based on risk assessment. Industries - Marketing Optimization: Targeting specific customer segments with tailored offers. - Example: An insurance company uses BI to analyze claims data and optimize pricing strategies to balance risk and profitability.  BI and Data Mining Manufacturing Applications - Product Quality: Identifying patterns to improve manufacturing processes. Across - Machinery Maintenance: Predicting and preventing equipment failures. Industries - Example: A manufacturing plant uses BI to monitor machinery performance and schedule maintenance, reducing downtime and costs.  BI and Data Telecom Mining - Churn Management: Identifying factors leading to customer churn and addressing them. Applications - Marketing and Product Creation: Developing Across new products based on customer data. - Network Management: Managing network Industries failures and optimizing performance. - Example: A telecom company uses BI to analyze customer usage patterns and develop targeted retention strategies, reducing churn rates.  Public Sector - Law Enforcement: Predictive policing to prevent crime. - Scientific Research: Analyzing data for research and development.  BI and Data Mining Applications Across - Example: A city government uses BI to analyze crime data and deploy police resources more Industries effectively, improving public safety.  - Importance of Business Intelligence - Different types of decisions (Operational vs. Strategic) Summary - Overview of BI tools - Essential BI skills - BI applications across various industries