Please read and have a look to client's brief pdf , Internal stakeholder chart, Data model and the relevant data provided before proceeding further for a better understaning of this simulation solution.
Overview:
This project is a 3-month pilot collaboration between Accenture and Social Buzz. The main goal is to enhance Social Buzz’s operations and prepare them for future growth.
- Audit Big Data Practices: Evaluate and improve how Social Buzz handles and utilizes large sets of data.
- Guide Through IPO: Provide strategic guidance to ensure a successful Initial Public Offering (IPO).
- Analyze Data for Insights: Examine data to identify trends and popular content categories, helping Social Buzz better understand their audience.
- Improved Data Management: By auditing big data practices, Social Buzz can optimize their data handling processes, leading to more efficient operations.
- Successful IPO: With Accenture’s expertise, Social Buzz can navigate the complexities of going public, increasing their chances of a successful IPO.
- Better Audience Insights: Analyzing data for insights allows Social Buzz to tailor their content and strategies to meet the preferences of their audience, enhancing user engagement and satisfaction.
This project leverages Accenture’s industry expertise to help Social Buzz scale effectively and achieve their business goals.
Analysis to find Social Buzz's top 5 most popular categories of content.
Following are the skills involved in this whole simulation project. SQL can be used to join the tables and find the insights required. The same can be done with excel as well. I'll recomment to try both. Power Query can be utilized for data modelling and we can create report for the insight using Power BI/ Excel/ SQL
- Extra: MS Excel/Power BI/SQL
- Communication
- Data Analysis
- Data Modeling
- Data Understanding
- Data Visualization
- Presentations
- Project Planning
- Public Speaking
- Storytelling
- Strategy
- Teamwork
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Data Understanding:
The key to success on any data project is to understand the data in detail. So we took the time to understand the data model and the domain of your business. -
Data Cleaning:
After understanding your business, we then cleaned the available datasets and thought about what an ideal dataset should look like for this problem. -
Data Modelling:
After ensuring the data was clean for analysis, we processed and modeled this data into a dataset that can precisely answer the business questions and produce the results needed. -
Data Analysis:
With our new dataset, we used our analytical expertise to uncover insights from this dataset and to produce visualizations to describe the insights. -
Business Decisions:
Finally, we used these insights to unlock business decisions and make recommendations on the next steps.
Animals and science are the two most popular categories of content, showing that people enjoy "real-life" and "factual" content the most.
Food is a common theme with the top 5 categories, with "Healthy Eating" ranking the highest. This may give an indication of the audience within your user base. You could use this insight to create a campaign and work with healthy eating brands to boost user engagement