Mae Mulligan, a Managing Director at Accenture, is overseeing a project for a client named Social Buzz, which has recently grown to a massive scale. The primary challenge for Social Buzz is managing their expanding data needs internally. Accenture has been tasked to audit Social Buzz's big data practices, provide recommendations for an Initial Public Offering (IPO), and analyze the popularity of different content categories
The main goal for the data analytics team is to analyze sample datasets to understand the popularity of various content categories, specifically identifying the top five categories based on popularity.
Identified essential datasets necessary for the analysis, including Reaction Score, Content ID, Reaction Types, Content Type, and Category.
Executed data cleaning by removing rows with missing values, adjusting data types, and omitting irrelevant columns.
Merged three key datasets (Reaction Types, Reactions, Content) to create a comprehensive dataset that facilitates in-depth analysis.
A refined dataset ready for analysis, focusing on the business question of identifying the top content categories by popularity.
Prepared visual representations using pie charts and bar charts to illustrate the findings effectively.
Developed a PowerPoint presentation detailing the analysis process, findings, and recommendations.
The project successfully created a clear, analyzed dataset that highlights the top five popular content categories for Social Buzz. These insights are crucial for strategic decision-making, particularly in supporting the company’s readiness for an IPO and refining its content strategy.
Present the findings to Social Buzz during a scheduled meeting to discuss the insights and strategic recommendations based on the data analysis.