Businesses can derive valuable insights from diverse data sources such as Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) systems, social media platforms, surveys, web analytics tools, government databases, market research reports, internal financial statements, and competitor analyses. However, when utilizing Microsoft Excel for data analysis and modeling, common errors and challenges arise. These include formula errors due to incorrect references or syntax, data entry mistakes leading to inaccuracies, version control issues in collaborative environments, limitations in handling large datasets, potential lack of data validation, difficulty in auditing complex models, security concerns, dependency on manual updates, limited automation features, and the risk of overemphasizing aesthetics over data accuracy and functionality. Recognizing and addressing these challenges is crucial for maintaining the reliability and effectiveness of data-driven decision-making processes within an organization