For AI-powered business growth, build a strong data foundation

The advancement of artificial intelligence and machine learning capabilities has made modern data management a critical foundation for businesses seeking to leverage AI effectively.

The data imperative: High-quality, accessible data is fundamental to developing robust AI and machine learning systems that can deliver meaningful business value.

• Poor data quality or insufficient volume makes it impossible to build effective machine learning algorithms
• Common challenges include data silos, lack of standardization, and privacy regulation compliance
• Modern data management solutions help overcome these obstacles by integrating technologies, governance frameworks, and business processes

Key applications and benefits: Modern data management systems enable organizations to efficiently derive real-time insights while ensuring data security throughout its lifecycle.

• Organizations can assess data quality, identify gaps, and organize information for AI model building
• These systems help address IT talent shortages by providing expertise many organizations lack internally
• Real-time insights support more effective strategic decision-making across the enterprise

Industry partnership spotlight: The collaboration between SAS and Intel demonstrates how combining advanced analytics with high-performance computing can enhance data management capabilities.

• Intel’s latest processors provide the computing power needed for complex data analysis and machine learning tasks
• SAS contributes advanced analytics capabilities and data management expertise
• The partnership offers complementary tools for data discovery, real-time insights, and multi-environment management
• Security is embedded at the chip level for enhanced data protection

Strategic implementation guidance: Organizations can maximize their AI investment returns by taking a measured approach to modern data management adoption.

• Start with small, focused projects rather than attempting enterprise-wide transformation
• Leverage state-of-the-art technology while maintaining cost control
• Partner with established technology providers to access proven solutions
• Focus on making data “fit for purpose” rather than pursuing perfect data quality

Looking ahead: While modern data management represents a significant shift from traditional approaches, the acceleration in value realization makes it an essential investment for organizations seeking to compete in an AI-driven business landscape.

• Organizations can implement recommendation engines and automated machine learning pipelines more quickly
• Value demonstration can occur in weeks or months rather than requiring multi-year projects
• The combination of advanced analytics and high-performance computing will continue to drive innovation in this space