What Modern Enterprises Must Demand From Data Quality Platforms
In the age of AI, data quality has become one of the most consequential must-have capabilities to promote trust in data and AI for modern enterprises. As organizations scale generative and agentic AI, data quality solutions now sit at the forefront of enterprise success in the AI adoption race. The Forrester Wave™: Data Quality Solutions, Q1 2026, reflects this dramatic shift from traditional solutions centered on rule-based cleaning and monitoring to now incorporating automation, multimodal insights, and capabilities that can ensure data is reliable for AI use.
In this evaluation, we assessed 10 market-leading vendors, helping steer enterprise buyers towards the right solution for their data quality needs. Buyers evaluating these platforms must navigate an entirely new set of strategic trade-offs and technical expectations aligned with following market trends:
AI is redefining data quality expectations. Leading platforms are coalescing generative and agentic AI across profiling, classification, validation, and remediation to improve scale and efficiency. However, these capabilities still depend on strong governance, transparency, and trust frameworks.
Observability is the new frontline of data integrity. Enterprises now require end-to-end visibility across pipelines, systems, data, and users simultaneously. This means that static validation approaches are becoming outdated, giving way to real-time monitoring and diagnostics. Observability is non-negotiable for supporting modern AI environments.
Multimodal data support expands AI readiness. As models continue to consume logs, images, documents, and other unstructured and semi-structured data sources, buyers are seeing the need to prioritize platforms that can profile and evaluate previously under-explored data types. This required vendors to adopt new technologies and leverage AI for data quality.
Unified platforms are replacing fragmented tools. The most competitive solutions offer broader ecosystems that combine governance, privacy, metadata, and lineage. The move towards an integrated platform rather than standalone tools for each function helps reduce operational complexity.
What Does This Mean For Data Quality Solutions Buyers
For buyers navigating the plethora of data quality solutions today, it is imperative to keep in mind the dramatic shifts this market has seen in the last few years. As data environments become more complex and AI initiatives move from experimentation to scale, assumptions that guided earlier solution choices no longer hold. Therefore, enterprise buyers should look beyond singular, foundational capabilities and instead opt for solutions that:
Provide end-to-end observability across pipelines and architectures
Support structured, semi-structured and unstructured data for modern AI workloads
Deliver data quality as part of a unified platform
Balance AI-driven automation with strong governance enforcement
Let’s continue the conversation
For a deeper dive into the market and what enterprises are looking for, Forrester clients can read the full report, The Forrester Wave™: Data Quality Solutions, Q1 2026. If you’d like to discuss how this market is evolving, what existing users are saying about their products, or how to assess which vendor best fits your enterprise, book an inquiry or guidance session with me.
