The emergence of artificial intelligence (AI) has created unprecedented opportunities across industries and use cases, from healthcare to manufacturing to customer service. As organizations race to implement AI solutions, one critical factor often determines success or failure: the quality and readiness of their data foundation.
AI systems learn from data and use that knowledge to make predictions and decisions. This fundamental relationship means that flawed data inevitably leads to flawed AI systems. A strong data foundation ensures that AI systems are trained on accurate, relevant, and timely information.
In this research brief, AI and Data Infrastructure: A Modern Approach, completed in partnership with Qlik, Futurum explores the critical importance of data quality and the transformative benefits of open architectures in AI development, while also examining Qlik’s solutions for modern AI infrastructure.
Key themes from AI and Data Infrastructure: A Modern Approach include:
- Data quality as the foundation of trustworthy AI, requiring robust governance and adherence to six key dimensions to ensure accurate, consistent, and reliable outcomes, measured through the The Qlik Talend Trust Score™
- How open architectures enable flexibility, cost efficiency, and innovation by avoiding vendor lock-in and supporting seamless integration with diverse tools and platforms.
- A look at modern data integration solutions that ensure AI readiness by delivering accessible, scalable, and actionable data while empowering users with self-service analytics capabilities.
If you are interested in learning more, be sure to download your copy of AI and Data Infrastructure: A Modern Approach today.
