Power BI dashboard project was developed using a public dataset from Kaggle for analytical practice. The project focuses on transforming raw user and revenue data into clear, interactive visual insights that support strategic decision-making.
Data Preparation
For this individual assignment, I built a Power BI dashboard using a public dataset from Kaggle, which includes User ID, Subscription Plan, Join Date, Last Payment Date, Country, Age, Gender, Device, and Plan Duration.
I first performed data cleaning and preparation in Excel, then imported the structured data into Power BI to create data models, measures, and interactive visualizations. The project focuses on transforming raw data into clear business insights across user demographics, subscription trends, and revenue performance.
Turning Data into Insights for Strategic Decision-Making
The following insights and strategic recommendations are derived from the dashboard analysis of Netflix’s user and revenue data (2013–2024). By examining user demographics, subscription behavior, device usage, geographic distribution, and revenue trends, the dashboard identifies key performance patterns and potential growth opportunities. These findings provide data-driven suggestions to enhance retention, optimize revenue, and strengthen market positioning in priority regions.
Learning Experiences
This dashboard project demonstrates how raw data can be transformed into meaningful business insights to support strategic decision-making. By analyzing user demographics, subscription behavior, device usage, geographic distribution, and revenue trends, I was able to identify key performance patterns and growth opportunities for Netflix.
However, the analysis would be even more valuable if the dataset included movie genre information. Genre-level data would allow deeper insights into audience preferences, especially across different age groups and regions, leading to more targeted content and marketing strategies.
Through this project, I strengthened my skills in data cleaning, data modeling, analytical thinking, and dashboard design. I also learned how to translate numerical findings into practical business recommendations — bridging the gap between data analysis and strategic decision-making.
