Dubai 17 March 2025:
– Qlik®, a global leader in data integration, data quality, analytics, and artificial intelligence, today announced its recognition as a Leader in the 2025 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions, marking its sixth time receiving this recognition. Qlik sees this recognition as a validation of its commitment to helping businesses ensure the data quality and governance needed to drive AI confidently at scale.
“With AI models increasingly commoditized, the true differentiator is data quality,” said Mike Capone, CEO, Qlik. “Companies that invest in governance and integrity will extract the most value. This recognition from Gartner validates what we see—AI success depends on trusted data.”
AI models require consistent, complete, and trusted data to generate accurate insights, yet many organizations struggle with gaps, inconsistencies, and unstructured inputs that undermine AI’s effectiveness. Poor data results in unreliable models, biased predictions, and heightened operational risk.
“Organizations need more than standalone data quality tools—they need a complete data strategy that ensures integrity at every stage,” said Drew Clarke, GM & EVP, Data Business Unit, Qlik. “Qlik Talend Cloud enables enterprises to embed trust into their data, analytics, and AI workflows, turning fragmented data into a reliable asset.”
By enabling proactive, governed data pipelines, Qlik ensures AI systems operate with high-trust data from the start, allowing businesses to scale AI with confidence.
AI is driving demand for next-generation data quality solutions that go beyond basic cleansing. With innovations like the Qlik Trust Score™ for AI, retrieval-augmented generation (RAG) support, and automated data remediation, businesses can move from reactive fixes to continuous, AI-powered data optimization.
Key Capabilities of Qlik:
· Qlik Trust Score for AI: Assesses data readiness for AI applications, ensuring reliable and explainable outputs.
· Hybrid and Multi-Cloud Deployment: Ensures consistent quality across diverse environments.
· Unstructured Data Processing: Converts raw, unstructured content into actionable insights, ensuring seamless connectivity.
· Automated Remediation: Machine learning refines quality rules over time, reducing manual effort.
· Data Products: Ensuring that curated data assets are easily accessible to data consumers to accelerate their use-cases, including AI projects.
