Data Quality Testing
In today’s data-driven world, poor data quality can lead to costly mistakes. From misguided strategic decisions to operational inefficiencies and poor customer experiences, the impact of bad data is far-reaching. Issues such as duplicates, data integrity failures, missing values, and inconsistent formats can create significant business challenges.
In this article:
Why Early Detection Matters
Fixing data quality issues at the source or integration level is cost-effective and minimizes business disruption. In contrast, correcting errors at the business level is expensive and can severely impact operations. Implementing data tests early ensures smooth processes and reliable reporting.
Benefits of Data Testing
- Trust in Data – Enables confident decision-making and reliable analytics.
- Process Efficiency – Automates quality checks and reduces manual work.
- Business Protection – Safeguards reputation and enhances customer satisfaction.
- Risk Reduction – Provides early warnings and ensures compliance.
Key Data Tests in Coalesce
To maintain high data quality, businesses should test for:
- Custom business rules
- Referential integrity
- Value ranges
- Uniqueness
- Data types
- Missing or null values
By implementing rigorous data tests, organizations can enhance data quality, minimize risks, and drive better business outcomes.
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Meet the Speaker
Deniz Polat
Consultant
Deniz is working in Business Intelligence and Enterprise Data Warehousing (EDW), supporting Scalefree International since the beginning of 2022. He has a Bachelor’s degree in Business Information Systems. He is a Certified Data Vault 2.0 Practitioner, Scrum Master and Product Owner and has experience in Data Vault modeling, Data Warehouse Automation and Data warehouse transformation with the tools dbt and coalesce.
