Welcome to the Data Quality Repository! This repository is dedicated to improving and ensuring the quality of data across various domains and systems. Here, you'll find resources, tools, and scripts aimed at identifying, analyzing, and resolving data quality issues.
Data quality refers to the condition of a set of values of qualitative or quantitative variables. High-quality data needs to be accurate, complete, reliable, and relevant, providing timely insights for decision-making processes. Good data quality is crucial because it directly impacts the outcome of data analyses and the effectiveness of data-driven decision-making.
- Data Profiling Tools: Scripts and tools to analyze datasets to discover their structure, detect anomalies, and summarize their characteristics with statistics.
- Data Cleansing Utilities: Automated scripts to clean data by correcting inaccuracies, filling missing values, and removing irrelevant or duplicated data.
- Data Validation Framework: A framework to implement comprehensive validation rules that ensure all incoming data meets the required standards before it is processed.
- Monitoring Dashboards: Visualization dashboards to continuously monitor data quality metrics and KPIs, making it easier to spot trends and issues.
- Documentation and Guides: Best practices, standards, and guidelines for maintaining high data quality in various scenarios.
- Case Studies: Examples and case studies on how improving data quality has positively impacted business outcomes.
We encourage contributions from the community! Whether you're fixing a bug, adding a new feature, or improving the documentation, your help is appreciated. Please send email to enrique.davila@gmail.com; enrique_davila@epam.com
This project is licensed under the terms of the MIT license.