Client Background
- Client: State Government
- Industry Type: Government
- Products & Services: Data Analysis
- Organization Size: 10000+
The Problem
Data cleaning and preprocessing were required across multiple datasets for 20 municipalities. The project was broken down into three stages:
- Stage 1: Power BI Dashboards using data from 20 municipalities.
- Stage 2: Revenue model with monthly-based predictions for 20 municipalities.
- Stage 3: Revenue model with quarterly and yearly-based predictions for 20 municipalities.
Our Solution
The solution focused on creating normalized and cleaned data, which was consolidated into the following files:
- C1-Sum-actual-cleaned_normalized.csv
- C1-Sum-budget-cleaned_normalized.csv
- C4-actual-cleaned_normalized.csv
- C4-budget-cleaned_normalized.csv
- C6-actual-cleaned_normalized.csv
- C6-budget-cleaned_normalized.csv
- C7-actual-cleaned_normalized.csv
- C7-budget-cleaned_normalized.csv
- C9-actual-cleaned_normalized.csv
- C9-budget-cleaned_normalized.csv
- Dim_municipalities.csv
- Dim-budget-type.csv
- Dim-date.csv
- Dim-period-by-year.csv
- Dim-province.csv
Additionally, use case models for revenue and other relevant business processes were written, and preliminary Power BI dashboards were created.
Solution Architecture
- Data Cleaning & Normalization: Files were cleaned and normalized to ensure consistency across datasets.
- Use Case Modeling: Detailed use case models were written for the revenue process.
- Power BI Dashboards: Built initial dashboards to visualize data trends and projections.
- Revenue Modeling: Focused on creating models for monthly, quarterly, and yearly revenue predictions.
Deliverables
- Normalized Cleaned Data Consolidated Files: Normalized Data Files
- Use Case Models: Use Case Models
Tech Stack
Tools Used:
- Power BI (for dashboarding and visualization)
- Excel (for cleaning and preprocessing)
- Python (for data manipulation and preprocessing)
Languages/Techniques Used:
- SQL (if required for database queries)
- Power Query (for data manipulation within Power BI)
- DAX (for calculations within Power BI)
Models Used:
N/A
Skills Applied:
- Data Cleaning & Preprocessing – Cleaned, normalized, and consolidated data for use in dashboards and models.
- Revenue Modeling – Built models to predict monthly, quarterly, and yearly revenues.
- Use Case Modeling – Created detailed use case models for revenue generation and financial forecasting.
- Dashboard Development – Developed preliminary Power BI dashboards for data visualization.
- Data Integration – Combined multiple datasets into a unified, structured format for easier analysis.
Databases:
N/A
Cloud Server:
N/A
Technical Challenges Faced
- Complex Data Cleaning – Multiple datasets with varying structures.
- Data Normalization – Ensuring consistency across different files.
- Revenue Model Accuracy – Predicting revenue with limited historical data.
- Dashboard Design – Effectively visualizing large and complex datasets.
How the Technical Challenges Were Solved
- Data Cleaning – Applied systematic cleaning procedures to standardize data.
- Normalization – Used automated scripts and manual processes to normalize data fields.
- Revenue Modeling – Applied statistical methods and assumptions to create reliable predictions.
- Dashboard Design – Leveraged Power BI features (DAX, Power Query) to create interactive and informative dashboards.
Business Impact
- Improved Decision-Making: Cleaned and consolidated data enabled more accurate analysis, leading to better financial forecasting and strategic decisions for municipalities.
- Enhanced Data Visualization: Power BI dashboards provided real-time insights, making it easier for stakeholders to track performance and identify trends.
- Optimized Revenue Predictions: Monthly, quarterly, and yearly revenue models offered valuable projections that supported budget planning and resource allocation.
- Informed Policy Planning: The comprehensive data and predictive models assisted in shaping policies based on reliable forecasts and demographic insights.
Project Website URL
