The evolving landscape of artificial intelligence is shifting from broad, generic discussions to a more nuanced focus on foundational data quality as a key differentiator.
In a new whitepaper, sponsored by Epsilon, titled "Improve data quality to support quality AI outcomes," IDC’s Lynne Schneider explains why investing in data quality is essential for driving AI performance—and why having a strategy that refines the data your enterprise gathers into a clean and consolidated foundation for enrichment, analysis and action can lead to results.
“In marketing, the success of AI-driven initiatives is directly tied to the quality of the data that powers them. When marketers rely on clean, current, and well-organized data, predictive and generative AI tools can effectively segment audiences, personalize messaging, and optimize campaign performance,” Schneider writes. “On the other hand, if the underlying data is inaccurate, incomplete, or outdated, AI models may misidentify customer preferences, target the wrong segments, or deliver irrelevant content—ultimately leading to wasted resources and missed opportunities.”
Download this white paper to learn why building a foundation of high-quality data is essential to using AI to its fullest potential.
