The top data trends shaping business strategy in 2025

New developments in data technology for 2025 show a tension between consolidation of existing tools and expansion driven by artificial intelligence capabilities.

Key industry shifts; The data technology landscape is experiencing dual forces of consolidation in traditional infrastructure while AI drives unprecedented expansion of capabilities.

• Companies are actively simplifying their data architectures, with many enterprise customers explicitly requesting fewer, not more, tools
• Major platforms like Snowflake and Databricks are becoming dominant as enterprises select their primary architecture
• Business Intelligence (BI) tools are consolidating around solutions that balance central and distributed control, such as Omni

Financial pressures and efficiency; Cost considerations are driving significant changes in how organizations approach data management and infrastructure.

• CFOs are demanding greater return on investment for both data and AI initiatives, marking a shift from the relatively unrestricted budgets of 2010-2022
• New transformation architectures like SQLMesh and Tobiko Data are enabling potential cost reductions of up to 50%
• Companies are actively seeking ways to reduce cloud data warehouse expenses through innovative architectural approaches

Emerging architectural trends; New approaches to data storage and processing are gaining traction, though adoption faces some hurdles.

• Novel cloud data storage formats are emerging but adoption is slower than anticipated due to limited enterprise tool availability
• Workload-specific query engines like MotherDuck and Datafusion are rising in prominence
• Scale-up architectures are becoming more popular, allowing developers to leverage local computing power for most workloads

AI integration and automation; Artificial intelligence is fundamentally changing how organizations handle data operations.

• The majority of SQL queries are expected to be AI-executed in the near future
• Data modeling is becoming crucial for ensuring accuracy and preventing AI hallucinations
• Data observability tools like Monte Carlo are growing in importance as AI systems become integrated into production applications

Model efficiency and cost considerations; Smaller AI models are proving more practical for enterprise applications.

• Enterprise implementations are focusing on models with 10-70 billion parameters
• These smaller models offer similar accuracy while reducing inference costs by up to 600x
• Recent innovations, such as those from DeepSeek, suggest continuing improvements in performance and cost efficiency

Looking ahead to 2025; While the data technology landscape is consolidating around major platforms, the integration of AI capabilities is simultaneously expanding possibilities and creating new efficiencies, though organizations will need to carefully balance these opposing forces to maximize value while controlling costs.