course | AI Applications in Business Data Analysis

Business Data Analysis with Excel and Generative AI
 

Applications: Microsoft Excel and Microsoft Copilot/ChatGPT

 

Understanding Business Data
 

• The role of data in organizational performance and decision-making 
• Differences between data, information, insight, and business action 
• Common sources of organizational data 
• Structured, semi-structured, and unstructured data 
• Quantitative and qualitative business information 
• Understanding data quality, relevance, accuracy, and reliability 
   

Business Analytics Fundamentals
 

• Descriptive analytics: What happened? 
• Diagnostic analytics: Why did it happen? 
• Predictive analytics: What may happen next? 
• Prescriptive analytics: What action should be taken? 
• Selecting the appropriate analytical approach for a business question 
• Translating workplace challenges into clear analytical questions 
• Defining objectives, KPIs, stakeholders, and expected outcomes 

Organizing and Analysing Data in Excel
 

• Structuring business data correctly in Excel 
• Using tables, sorting, filtering, and conditional formatting 
• Applying essential formulas for business analysis 
• Using logical, lookup, date, and summary functions 
• Creating PivotTables and PivotCharts 
• Comparing performance across periods, teams, and departments 
• Using Excel’s Analyze Data functionality 
• Recognizing trends, patterns, and performance exceptions 
   

Generative AI for Excel Analysis
 

• Writing effective prompts for analytical tasks 
• Asking AI to generate and explain Excel formulas 
• Using AI to suggest suitable analytical approaches 
• Asking AI to interpret tables and analytical results 
• Refining prompts to improve the quality of responses 
• Validating AI-generated formulas, calculations, and conclusions 
   

Business Examples
 

• Departmental performance analysis 
• Budget versus actual expenditure 
• Employee attendance and productivity 
• Customer requests and service performance 

Data Cleaning and Preparation with Power Query

Applications: Excel Power Query and Microsoft Copilot/ChatGPT
 

Importance of Data Preparation
 

• Why data quality affects analytical results 
• Common data-quality issues in organizational reports 
• Identifying missing, duplicated, inconsistent, and outdated records 
• Recognizing incorrect data types and formatting issues 
• Understanding the risks of analyzing unreliable information 
• Establishing validation checks before beginning an analysis 
   

Importing Business Data
 

• Importing information from Excel workbooks  
• Importing CSV files and structured data 
• Working with multiple worksheets and files  
• Understanding columns, records, headers, and data types 
• Selecting relevant information for analysis 
• Removing unnecessary rows and columns 
   

Cleaning and Transforming Data with Power Query
 

• Correcting data types and formatting problems 
• Removing duplicate and incomplete records 
• Handling missing values 
• Standardizing names, dates, departments, and categories 
• Splitting, merging, and transforming columns 
• Replacing inconsistent values 
• Combining and appending multiple datasets 
• Consolidating monthly or departmental files 
• Creating repeatable data-cleaning processes 
• Refreshing reports when new data becomes available
   

AI-Assisted Data Preparation
 

• Using AI to suggest data-quality rules 
• Asking AI to explain Power Query transformation steps 
• Generating instructions for cleaning common data issues 
• Categorizing and organizing unstructured information 
• Identifying possible anomalies and inconsistencies 
• Checking AI recommendations before applying them 
• Protecting confidential information when using AI tools 
   

Business Examples
 

• Combining monthly performance reports 
• Cleaning employee or customer records 
• Consolidating data from different departments 
• Preparing operational data for management reporting 

Interactive Analysis and Dashboards with Power BI
 

Applications: Power BI Desktop and Power BI AI Visuals

 

Introduction to Power BI
 

• Understanding the Power BI environment 
• Importing prepared business data 
• Reviewing and transforming data 
• Understanding relationships between datasets 
• Creating a simple and reliable data model 
• Differentiating dimensions, measures, and indicators 
   

Business Measures and KPIs
 

• Creating basic calculated measures without advanced coding 
• Defining organizational KPIs 
• Comparing actual results against targets 
• Calculating totals, averages, percentages, and variances 
• Comparing performance over different periods 
• Applying filters and business categories 
• Presenting performance by department, location, or service 
   

Building Interactive Reports
 

• Selecting suitable visualizations for different analytical questions 
• Creating charts, tables, cards, and KPI visuals 
• Applying filters, slicers, and drill-down functionality 
• Designing clear and user-friendly report pages 
• Highlighting risks, exceptions, and opportunities 
• Avoiding misleading charts and unnecessary visual elements 
• Structuring dashboards for management and operational users 
   

Using Power BI AI Capabilities
 

• Using the Decomposition Tree for root-cause exploration 
• Using Key Influencers to identify factors affecting outcomes 
• Detecting unusual results and performance exceptions 
• Exploring data through natural-language questions 
• Interpreting AI-supported findings 
• Validating suggested relationships against business knowledge 
   

Business Examples
 

• Organizational performance dashboard 
• Customer satisfaction analysis 
• Workforce and HR dashboard 
• Operational efficiency monitoring 

Forecasting, Insight Generation, and AI-Assisted Reporting
 

Applications: Microsoft Excel, Power BI, and Microsoft Copilot/ChatGPT

 

Understanding Business Forecasting
 

• The purpose of forecasting in business planning 
• Identifying trends, recurring patterns, and seasonality 
• Selecting an appropriate forecasting period 
• Understanding historical data and external factors 
• Differentiating forecasts from guaranteed outcomes 
• Recognizing uncertainty and limitations 
   

Forecasting with Excel and Power BI
 

• Creating forecasts using historical business data 
• Applying forecasting features in Excel 
• Applying forecasting features in Power BI 
• Identifying anomalies and unexpected changes 
• Comparing forecasted and actual results 
• Developing best-case, expected, and worst-case scenarios 
• Updating forecasts when new information becomes available 
• Recognizing when a forecast should not be used 
   

AI-Assisted Insight Generation
 

• Using AI to summarize analytical findings 
• Asking AI to identify possible trends and relationships 
• Converting technical results into clear business language 
• Exploring alternative explanations for performance results 
• Distinguishing evidence from assumptions 
• Checking AI-generated insights against source data 
• Identifying unsupported claims or recommendations 
   

Data Visualization and Storytelling
 

• Selecting the right chart for the intended message 
• Presenting trends, comparisons, composition, and relationships 
• Structuring an analytical story for decision-makers 
• Explaining the business context and key findings 
• Communicating risks, opportunities, and implications 
• Supporting recommendations with evidence 

AI-Assisted Management Reporting

• Drafting executive summaries using generative AI 
• Converting dashboard findings into structured reports 
• Adapting messages for management and operational audiences 
• Developing evidence-based recommendations 
• Reviewing AI-generated reports for errors and unsupported claims 
• Maintaining human accountability for the final report 
   

Business Examples
 

• Workload and demand forecasting 
• Budget and expenditure forecasting 
• Customer service volume forecasting 
• Workforce and resource planning

Responsible AI and Workplace Application
 

Applications: Microsoft Copilot/ChatGPT, Excel, and Power BI

 

Data Privacy and Responsible AI
 

• Protecting confidential, personal, and sensitive information 
• Understanding which information should not be entered into public AI tools 
• Applying organizational data-security requirements 
• Identifying hallucinations, bias, and inaccurate outputs 
• Recognizing unsupported AI-generated conclusions 
• Maintaining transparency when AI supports analysis 
   

Validating AI-Generated Outputs
 

• Checking AI-generated formulas and calculations 
• Comparing summaries with the original data  
• Reviewing assumptions and limitations 
• Applying professional judgment before accepting recommendations 
• Maintaining human accountability for AI-supported decisions 
• Documenting appropriate review and approval steps 
   

Applying AI in the Workplace
 

• Selecting appropriate AI applications for different business tasks 
• Recognizing tasks that require human expertise and judgment 
• Identifying a relevant use case for each participant’s department 
• Defining the business need and expected benefit 
• Identifying the data required for the selected use case 
• Considering potential privacy, accuracy, and governance risks 
• Defining practical next steps for responsible application