Project: Transforming and Analyzing Data with SQL

The project aims to analyze sales data to understand revenue trends and customer behavior.

Project/Goals

1. Analyze transaction revenue and total revenue by city and country.
2. Analyze products ordered by city and country, including determining the top-selling products by city and country.
3. Analyze data on product categories by city and country.
4. Determine additional ways of analyzing data and write queries to perform analysis.

Process

Transform and analyze sales data with SQL queries.

Data Exploration: Understanding the structure of the sales data, including the tables, columns, and relationships between them. Identifying potential issues such as missing values, duplicates, or inconsistencies in the data.

Data Cleaning: Handling missing or erroneous data by replacing missing values, removing duplicates, or correcting errors. Standardizing data formats and values to ensure consistency across the dataset. Validating data integrity by checking for constraints and relationships between tables.

Data Transformation: Performing transformations such as aggregations, joins, or filtering to derive new datasets or insights. Creating calculated fields or columns based on existing data, such as computing metrics or categorizing data.

Analysis Query Development: Writing SQL queries to analyze the transformed data, answering specific questions or exploring patterns. Using aggregate functions, groupings, and filters to extract relevant insights from the data. Incorporating window functions, subqueries, or common table expressions (CTEs) for complex analysis tasks.

Perform quality assurance checks on SQL queries to ensure data integrity.

Quality Assurance Checks: Performing quality assurance checks on SQL queries to ensure data integrity, accuracy, and consistency. Validating the results against expected outcomes. Checking for NULL values, zero values, unexpected values, and other data anomalies in the output.

Optimization and Performance Tuning: Optimizing SQL queries for better performance by considering factors such as indexing, query structure, and data distribution. Evaluating query execution plans and identifying opportunities for optimization.

Results

1. Regional trends: Analysis revealed differences in purchasing behavior and revenue generation between regions which can be used to inform targeted marketing campaigns.
2. Seasonal variations: Examination of revenue trends over time highlighted seasonal patterns in purchasing behavior which can be used for better inventory planning and resource allocation.
3. Customer segmentation: Analysis identified distinct customer segments based on purchasing habits and sentiment scores which can facilitate personalized marketing strategies and customer engagement initiatives.
4. Product performance: Evaluation of product sales data provided insights into the popularity and profitability of various products which can be used to guide product development and marketing efforts.

Challenges

1. Handling NULL and zero values in the data.
2. Ensuring data consistency across multiple tables.
3. Optimizing SQL queries for performance.

Future Goals

1. Implement more advanced analytics, such as predictive modeling.
2. Enhance data visualization for better insights presentation.
3. Incorporate real-time data processing for up-to-date analysis.