This 52-page report, geared towards a better understanding of the impact of artificial intelligence (AI) on modern data architecture, is based on a survey of 259 data professionals. It covers six dimensions that extend beyond specific technology implementations, to provide a well-rounded picture of data architecture focus, investment, and requirements:

1. Research focus
2. Business value
3. Future commitments
4. Use cases
5. Data source requirements
6. Chosen cloud platform

The impact of AI is evident, as generative AI (GenAI) with large language models (LLMs) is seeing most of the research focus, business value, future commitments, and use cases, followed by data lakehouses and cloud data warehouses (though the latter two do not always follow in that order).

However, the report’s top challenges are maintaining LLM accuracy while reducing hallucination risk (36.7%), the cost of implementation and operation (35.5%), and integrating enterprise data or data silos for improved GenAI (35.1%), which suggests that enabling AI-ready data, while controlling costs, is top-of-mind when pursuing an AI strategy, even with the advent of data lakehouses and cloud data warehouses.

Read this report to delve into these and many other details, for a highly comprehensive blueprint of a data architecture that can support success in this demanding AI era.