SQL Data Quality Management: Handling Duplicates in E-Commerce Data

Overview

This repository showcases my expertise in using SQL for data quality management, specifically focusing on identifying and handling duplicate values in an e-commerce database. These techniques are crucial for maintaining data integrity, which is essential for accurate reporting and analysis in real-world business scenarios.

Key Features

• Identification of duplicate records across various tables
• Join operations to enrich data and identify inconsistencies
• Data update and deletion strategies
• Advanced querying techniques for data quality assessment

Techniques Demonstrated

1. Identifying Duplicate Values

I use aggregation and grouping to quickly identify duplicate entries:

SELECT product_name, COUNT(*)
FROM product
GROUP BY product_name;

This query is vital for initial data quality checks, especially after data imports or updates.

2. Enriching Data with Joins

To get a comprehensive view of potential duplicates across related tables:

SELECT
    s.id, s.id_customer, s.sale_date, s.product_code
    , c.name, c.last_name
    , p.product_name
    , COUNT(*) AS duplicated
FROM sales s
LEFT JOIN customers c
    ON s.id_customer = c.id_customer
LEFT JOIN products p
    ON s.product_code = p.product_code
GROUP BY s.id, s.id_customer, s.sale_date, s.product_code, c.name, c.last_name, p.product_name;

This join operation helps in identifying inconsistencies across sales, customer, and product data.

3. Data Cleansing Operations

For updating incorrect data:

UPDATE <table>
SET column = 'new_value'
WHERE key = 'condition_value';

And for removing duplicate or erroneous entries:

DELETE FROM product
WHERE product_code = 10;

4. Advanced Duplicate Detection

Using subqueries and CASE statements for more nuanced duplicate detection:

SELECT a.product_name, a.brand, a.category, a.duplicated,
    CASE WHEN a.duplicated > 1 THEN 'Yes'
        WHEN a.duplicated = 1 THEN 'No'
    END AS is_duplicated
FROM
(
    SELECT
        product_name, brand, category,
        COUNT(*) AS duplicated
    FROM product
    GROUP BY product_name, brand, category
) a
WHERE a.duplicated > 1;

This query not only identifies duplicates but also labels them, facilitating easier data cleaning processes.

5. Data Quality Metrics

Assessing data quality through metrics like distinct price counts:

SELECT
    product_name
    , COUNT(DISTINCT unit_price) AS total_distinct_prices
FROM products
GROUP BY product_name;

This helps in identifying pricing inconsistencies, a common issue in e-commerce data.

Real-World Application

In my e-commerce data management projects, these SQL techniques have been instrumental in:

• Ensuring data consistency across sales, inventory, and customer databases
• Identifying and resolving product listing duplicates, improving catalog accuracy
• Detecting pricing anomalies, crucial for maintaining competitive and fair pricing
• Streamlining data cleaning processes, reducing manual intervention and error

Conclusion

This SQL toolkit demonstrates my ability to maintain high data quality standards in complex e-commerce databases. By effectively managing duplicates and inconsistencies, I ensure that downstream analytics and business decisions are based on accurate, reliable data. These skills are essential in today's data-driven business environment, where data quality directly impacts operational efficiency and strategic decision-making.

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