Inside the Enterprise Data Stack: What Leaders Are Prioritizing in 2025

Data leaders are investing aggressively, but cracks in the foundation remain

Team8’s 2025 Data Village Survey, which we just released, reveals how the world’s top organizations are scaling their AI ambitions by investing deeply in data infrastructure, platform tooling, and operational governance. But while budgets are rising and AI is moving into production, challenges like data quality, observability, and cross-functional efficiency still dominate the conversation.

This report features contributions from a cross-section of senior data and analytics executives, including leaders from Citi, Highmark Health, Morgan Health, and Prudential. These voices offer a rare, inside-out view of how global enterprises are navigating the evolution of the data stack. 

The report is also grounded in insights from dozens of data leaders surveyed across industries such as technology, finance, healthcare, and industrials, offering a quantitative snapshot of where the data ecosystem is heading.

In this report, you’ll learn:

1. Data infrastructure budgets are unsurprisingly climbing
   
   More than half of respondents plan to increase data infrastructure spend in 2025, with enterprise-scale organizations reporting annual investments exceeding $10M. Some plan to cross $30M, particularly in financial services.
2. Lakehouse and governance take the lead in tooling priorities
   
   62% of respondents are prioritizing AI/ML platform investment, while 34% each are focusing on lakehouse architectures and data governance frameworks. Apache Iceberg adoption is rising fast across use cases.
3. Data quality is the number one pain point, for the second year in a row
   
   41% of leaders identified it as their top challenge. Observability, unstructured data, and efficiency followed closely, while areas like BI and ETL have moved down the stack in perceived urgency.
4. AI is maturing faster than expected, but the stack must catch up
   
   42% of organizations now have multiple AI applications in production. However, many still lack foundational tooling for monitoring, validation, and access governance.
5. Engineering talent and product thinking are in high demand
   
   AI/ML engineers, data engineers, and MLOps roles are the most difficult to hire. At the same time, 31% of leaders are prioritizing data product managers as the field shifts toward treating data as an internal product with defined customers and SLAs.

Click here to read the full reportand explore how today’s leading companies are scaling their data infrastructure and redefining AI-readiness.

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