Data Analytics for Brand and Marketing Communications with Excel and PowerBI - Session 6

End-to-End Project (Data Generation, Cleaning, & Transformation in Excel) In this data analytics session, the focus is on initiating a comprehensive, end-to-end data analysis project for learning purposes using a fictional financial technology company called Nova Pay. The session covers the initial steps of using AI to outline business objectives and synthetically generate a marketing dataset, followed by executing critical data cleaning and transformation workflows within Excel’s Power Query Editor. Key Topics Covered: Project Initiation & Setup: Defining a problem statement, business objectives, and a target dataset surrounding brand and marketing communications for an FMCG or Fintech company. Synthetic Data Generation: Using an AI tool to formulate a prompt and create a messy, real-world-simulating CSV dataset consisting of nearly 5,000 records across 13 columns. Understanding the Business Brief: Reviewing the company overview, business requirements, and the column-specific data dictionary before jumping straight into data analysis. Power Query Data Cleaning Workflows: Loading source CSV data into a blank Excel workbook and launching the Power Query Editor. Standardizing column labels by promoting the first row to data headers. Detecting, validating, and removing duplicate records based on the unique Campaign ID primary key. Splitting complex combined columns (such as Campaign Name/Region and Launch Date/Time) using specific custom delimiters and advanced alignment properties. Performing structural column merging and cleanup operations. Text transformation and categorical data validation, including trimming white spaces, resolving text inconsistencies, and normalizing values (e.g., standardizing structural short-codes into fully formatted regional text like "Central" and "South"). Project Checkpoint & Assignment: Saving progress inside Power Query and assigning a task to finish standardizing campaign types and managing blank or missing row values using the provided project brief guide. This video serves as a structured, step-by-step tutorial on why data cleaning and transformation take up roughly 70% to 80% of an analyst's project lifecycle, ensuring data integrity before progressing to reporting, dashboard creation, or visual presentations.