Control hasn’t just become harder to maintain in modern database environments, it’s actively slipping away. Despite growing investment in modern delivery practices, many organizations are still relying on manual processes to test and deploy database changes. Our data shows that 39% continue to use manual approaches, even as estates become more distributed and complex. In hybrid, multi-platform environments, this makes it harder to track what changed, prove consistency across environments, and release with confidence, widening the gap between speed and control.

For many organizations, the promise of simplification has quietly reversed. With 84% of organizations managing two or more database platforms, estates are diversifying faster than teams can establish standardized practices to manage them. Respondents report growing difficulty with data integration, persistent skills gaps, and rising challenges around security, compliance and access controls in multi-platform environments. There are more decisions to make, more variation in how work gets done, and more effort required to keep environments secure and aligned.

The risk of security incidents and compliance failures is rising, with the most fragile situations emerging when weak practices meet high complexity. As estates become more complex, 64% of organizations are struggling to apply consistent practices across environments, even as security and governance rise up the priority list.

In these conditions, risk often stems from an over-reliance on individual expertise and local knowledge, rather than security controls and data quality checks being systematically embedded into every database work.

AI is scaling faster than the practices needed to govern it, leaving many initiatives or transformation projects dependent on legacy databases, manual workflows, and inconsistent data practices. In the past year alone, the use of AI in database management has almost tripled – one of the largest year-on-year shifts in the survey. As AI accelerates database change and increases how often data is accessed and reused, these weaknesses create fragility across already complex environments. Without consistent change management and data protection, risk grows quickly and surfaces later as data quality, security, and compliance issues.

The quality of an organizations data directly impacts their ability to scale effectively. Businesses rely on shared data for analytics and AI, and need realistic, trustworthy data for development and testing, often across multiple environments. Without this, problems are detected far too late in the process, wasting both time and money.

In fact, the survey shows that most failures emerge as data is transformed and moved across environments, with 47% of respondents reporting data quality issues linked directly to transformation. These gaps in governance, visibility, and validation need to be fixed to avoid data quality growing into a major strategic risk.