2025 marks the year that generative artificial intelligence (AI) became a core industry topic and ushered in a "data renaissance." However, by 2026, its importance has transcended simply acquiring high-quality data. The question of how to enable AI models to truly understand and utilize the "correct" semantic layers of data has become prominent. This signifies the official start of the era of semantic data design, which includes knowledge graphs and ontology, and the ability to clearly define data context, semantics, and business identity.
Last year, the "agent AI" craze swept across the industry, with many companies hoping to automate their operations and optimize decision-making. However, most agent AIs failed to meet expectations, and the quality of the data they used and the appropriateness of the context began to be seen as the root cause. Research from Carnegie Mellon University points out that today's agents have not been adequately trained to handle complex tasks, and reasoning errors caused by data context can lower overall performance.
Against this backdrop, the maturity of data quality and data governance has become a crucial issue. While major cloud providers such as Amazon Web Services (AWS) continue to offer vast data ecosystems, their newly released data-related technologies and platform innovations have been limited compared to the previous year. In contrast, events such as IBM's acquisition of Confluent and Microsoft's release of HorizonDB based on PostgreSQL symbolically demonstrate the trend of restructuring the data technology stack.
Zero-ETL architecture and data sharing technologies have become mainstream by 2025. This represents an attempt to simplify complex and fragile data pipelines. Platforms such as Snowflake and Databricks have significantly improved business data accessibility by supporting data integration with SAP or Salesforce.
Another trend is the widespread adoption of vector data processing technology. Most mainstream data platforms have enhanced their vector retrieval and analysis capabilities. Oracle released query functionality that integrates structured and unstructured data, and AWS launched a vector-optimized S3 storage layer. This lays the foundation for the comprehensive application of AI in documents, images, and even distributed data within enterprises.
The most noteworthy change is the revaluation of the semantic layer. Originally used in BI tools or ERP systems, this layer standardized the meaning and interpretation of data around core concepts such as "metrics," "dimensions," and "details." Tableau, Databricks, Snowflake, Microsoft, and others are accelerating the introduction of semantic layers. Microsoft Fabric IQ, in particular, integrates enterprise ontology concepts into its existing semantic layer to ensure the contextual accuracy of real-time AI analysis.
Against this backdrop, the Open Semantic Exchange Initiative, spearheaded by Snowflake, aims to establish a common standard to ensure semantic interoperability among various AI and data platforms. This architecture is based on dbt Labs' MetricFlow, using YAML configuration files to comprehensively define metrics and dimensions. However, whether open-source projects can handle high-value semantic assets, especially the willingness of application vendors to share them, remains to be seen.
Looking further ahead, independent knowledge graphs and technologies like GraphRAG are gaining attention as the infrastructure for AI to accurately understand context. Neo4J, Google's Vertex AI RAG engine, and Microsoft's LazyGraphRAG are all dedicated to building the technological foundation for activating such models, and practical application cases are gradually increasing. Companies like Deloitte and AdaptX have already comprehensively promoted knowledge graph-driven AI applications in complex fields such as healthcare and security.
However, the biggest challenge remains the shortage of ontology modeling talent. With AI struggling to autonomously design semantic structures, the demand for knowledge engineers and semantic architects has surged. This is reminiscent of the "knowledge management" dilemma of decades ago; in the current trend, accurate semantic interpretation and business relevance are more critical than simple data collection.
Ultimately, the core of the AI era is not simply data accumulation, but data that can accurately understand semantics and context. 2026 is expected to be a turning point in the formation of semantic influence circles and the struggle for dominance among various platforms and applications. The collaborative sharing models of companies like Snowflake, Databricks, and SAP are shaping a competitive landscape around standards and ecosystems, indicating that companies that can provide the "right" data for AI will ultimately gain ultimate dominance.
