Explain one of your projects, describe your role, and outline the steps you take in data cleaning. Also, what do we mean by a database?

During my recent role as a Data Analyst at XYZ Corp, I led a project to build a customer segmentation model for the marketing team. I was responsible for data acquisition, cleaning, feature engineering, and model deployment. Using the STAR framework:

• Situation: The marketing team had fragmented customer data across multiple sources, leading to inconsistent segmentation.
• Task: Consolidate the data, clean it, and create a reliable segmentation model.
• Action: I first extracted data from CRM, ERP, and web analytics. I performed data cleaning in the following steps:

1. Deduplication: Removed duplicate records using unique identifiers.
2. Missing value handling: Imputed missing values with median or mode, and flagged records with excessive missingness for exclusion.
3. Outlier detection: Used IQR and z‑score methods to identify and cap extreme values.
4. Standardization: Normalized numeric features and encoded categorical variables with one‑hot encoding.
5. Validation: Created a data quality dashboard to track key metrics.
6. Documentation: Maintained a data dictionary and versioned the cleaned dataset.
7. Modeling: Applied K‑means clustering and evaluated cluster quality with silhouette scores.
8. Deployment: Integrated the model into the marketing automation platform.

• Result: The segmentation improved campaign ROI by 18% and reduced churn by 5%.

A database is an organized collection of data that is stored and accessed electronically. It provides mechanisms for data storage, retrieval, and management, ensuring data integrity, security, and concurrency control. Databases are typically managed by a Database Management System (DBMS) such as MySQL, PostgreSQL, or Oracle, which offers query languages (SQL), transaction support, and backup/recovery features.