In 2026, the battle for "correct data" in artificial intelligence begins... Semantic-based design will determine the outcome

2025 will be the year when generative artificial intelligence (AI) becomes a core industry topic and initiates the “Data Renaissance.” However, by 2026, its importance will surpass merely acquiring high-quality data, and the comprehensive issues of how to enable AI models to truly understand and utilize the semantics of “correct” data across (semantic layers) will become prominent. This marks the official beginning of the era of semantic data design, which includes knowledge graphs and ontologies, capable of clearly defining data context, semantics, and business identity.

Last year, the “intelligent agent” AI craze swept the entire industry, with many companies hoping to achieve business automation and decision optimization through it. However, most intelligent agents AI did not meet expectations, and the quality and contextual appropriateness of the data used began to be seen as fundamental reasons. Research from Carnegie Mellon University pointed out that today’s intelligent agents have not yet received sufficient training to handle complex tasks, and reasoning errors caused by data context can overall reduce performance.

Against this backdrop, whether data accuracy (Data Quality) and governance systems (Data Governance) have reached maturity has become an important issue. Major cloud providers like Amazon Web Services (AWS) still offer extensive data ecosystems, but their new data-related technologies and platform innovations are limited compared to the previous year. Conversely, events such as IBM acquiring Confluent and Microsoft releasing HorizonDB based on PostgreSQL symbolically demonstrate the trend of reconstructing the data technology stack.

Zero-ETL architectures and data sharing technologies have become mainstream in 2025. These are attempts to simplify complex and fragile data pipelines, for example, platforms like Snowflake and Databricks significantly improve business data accessibility by supporting SAP or Salesforce data integration.

Another trend is the popularization of vector data processing technologies. Most mainstream data platforms have enhanced vector retrieval and analysis capabilities. Oracle released query functions that integrate structured/unstructured data, and AWS launched vector-optimized S3 storage layers. These developments lay the foundation for AI to comprehensively utilize documents, images, and even dispersed enterprise data.

The most noteworthy change is the revaluation of the value of the semantic layer. Originally used for BI tools or ERP systems, this layer revolves around core concepts such as “metrics,” “dimensions,” and “details,” standardizing the meaning and interpretation of data. Tableau, Databricks, Snowflake, Microsoft, and others are accelerating the introduction of semantic layers, with Microsoft Fabric IQ even integrating enterprise ontology concepts into existing semantic layers to ensure contextual accuracy for real-time AI analysis.

Under this trend, the open semantic exchange initiative led by Snowflake aims to establish a universal standard to ensure interoperability of semantic layers across various AI and data platforms. This architecture is based on dbt Labs’ MetricFlow, which defines metrics and dimensions through YAML configuration files. However, whether open-source projects can handle high-value semantic assets, especially given vendors’ willingness to share, remains uncertain.

Furthermore, independent knowledge graphs and technologies like GraphRAG are gaining attention as infrastructure for AI to accurately understand context. Neo4J, Google Vertex AI RAG engine, Microsoft LazyGraphRAG, and others are dedicated to building the technical foundation to activate such models, with practical applications gradually increasing. Companies like Deloitte and AdaptX are fully promoting knowledge graph-driven AI applications in complex fields such as healthcare and security.

However, the biggest challenge remains the shortage of ontology modeling talent. In situations where AI cannot autonomously design semantic structures, the demand for knowledge engineers and semantic architects has surged. This recalls the “knowledge management” practice dilemmas from decades ago. In current trends, precise semantic interpretation and business relevance are more critical than mere data collection.

Ultimately, the core of the AI era is not just data accumulation but data that can accurately understand semantics and context. 2026 is expected to be a turning point for the formation of semantic influence circles and the struggle for dominance among platforms and applications. The sharing and collaboration models of companies like Snowflake, Databricks, and SAP are shaping competitive patterns around standards and ecosystems, indicating that enterprises capable of providing “correct” data for AI will ultimately hold the dominant position.