Data Quality Framework
Key Takeaways
Implementing a robust data quality framework.
In today’s digital landscape, trusted data is the foundation of every successful decision.
This paper shows how to build a culture, process, and technology ecosystem that keeps your data clean, consistent, and valuable.
Turn data into a trusted business asset. Learn how a structured Data Quality Framework ensures decisions are made on accurate, reliable information.
Bridge the gap between business and IT. Discover how governance, clear roles, and shared ownership drive data accountability across the organization.
Focus on what matters most. Identify and prioritise critical data elements that directly impact business value and regulatory compliance.
Adopt a maturity-based approach. Move from reactive fixes to a proactive, continuously improving data-quality culture.
Enable innovation with confidence. Build a foundation of high-quality data that supports advanced analytics, AI adoption, and digital transformation.
FAQ: Data Quality Framework
What role do data cleansing and metadata management play in a data quality framework?
Data cleansing corrects errors, removes duplicates, fixes structural issues, enriches values, and validates datasets - making data usable for analytics, operations, and AI systems.
Metadata management complements cleansing by documenting lineage, definitions, and data usage, ensuring transparency and long‑term consistency. Together, they maintain trustworthy datasets and support continuous improvement through automated and manual processes.
Download the full report for detailed cleansing workflows and metadata requirements.
How does data profiling contribute to improved data quality?
Data profiling helps organizations understand the contents, patterns, structure, and acceptance criteria of their critical datasets.
Profiling outputs highlight issues early, guide rule creation, and help data stewards classify and prioritize DQ problems. This iterative process supports continuous improvement, ensuring DQ rules evolve as business needs and data sources change.
To explore the full profiling lifecycle, download the Thought Leadership report.
What are the key dimensions of data quality organizations should measure?
The six primary data quality dimensions are accuracy, completeness, consistency, timeliness, relevance, and validity. Each dimension supports decision‑making and data trust - for example, accuracy ensures real‑world correctness, completeness prevents rework, and consistency enables harmonization across systems.
These dimensions must be monitored continuously using validation rules, cross‑checks, standardized formats, latency controls, and defined compliance criteria.
Download the report for a full breakdown of the six key data quality aspects.
.webp "GFT Contact: Dean Clark")
