Trusted data for AI is the core to AI success. According to Enterprise Strategy Group research, the majority (65%) of organizations feed 21% to 50% of their data into AI models, while an additional 22% of true data-driven organizations reported processing more than half of their data through AI models (see Figure 1),¹ highlighting the strong need for a data platform and tools that enhance data readiness, including data quality, governance, integration, real-time processing, scalability, and efficiency.

^1. Source: Enterprise Strategy Group Research Report, Data Readiness for Impactful Generative AI, April 2025.

Data Quality Matters

Enterprise Strategy Group research found that 74% of respondents rated the impact of data quality on the success of their organization’s AI initiatives as critical or very high.² It’s clear that organizations prioritize accuracy and effectiveness in AI initiatives. This supports the need for data platforms that can take all of the needed steps for data validation, cleansing, and enrichment to enhance AI outcomes. Improving data quality directly impacts analytics and AI performance and strategic results.

^2. Ibid.