Since 2017, Redgate has surveyed thousands of data professionals worldwide to understand how the industry is changing, and to provide practical guidance for both organizations and practitioners.

In 2026, the research goes even deeper - connecting the day-to-day challenges teams face across platforms, processes, security, data, and AI. You can see where control is being lost, benchmark against your existing processes, and focus improvement efforts where they’ll have the greatest impact.

Methodology

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.

“As organizations modernize legacy infrastructure and scale operations, the challenge is no longer just about moving fast – it’s about moving smart.”

Chris Yates

Senior Vice President, Managing Director of Data and Architecture, Republic Bank

• DB DevOps dedicated tool (45%)
• Manually (39%)
• Built-in CI/CD vendor tools (38%)
• Data modelling / ERD tool (35%)
• Dev framework / ORM (33%)
• In some other way (5%)

Toptip1

How to scale delivery, without scaling risk

This whitepaper uncovers how secure, automated database change management resolves that tension and explains why growth fails without safeguards.

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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.

“Companies that make sure their database teams are involved at the beginning of projects to decide which systems are best avoid ending up in this “accidental” multi-platform environment, and become more intentional, so automation, maintenance, and skills development keep pace with reality.”

Tracy Boggiano

Senior Database Developer, Abarca Health

• 2025 (8%)
• 2024 (9%)
• 2023 (29%)

• 2025 (23%)
• 2024 (17%)
• 2023 (13%)

• 2025 (53%)
• 2024 (47%)
• 2023 (36%)

• 2025 (16%)
• 2024 (26%)
• 2023 (21%)

Toptip2

Reducing operational drag: tips for managing multiple database platforms

As estates grow across more platforms and environments, consistency becomes harder to maintain. This article outlines four practical steps teams can take to reduce variation in how databases are managed, strengthen shared practices, and lower the operational overhead of supporting multiple platforms.

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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.

“C-Level executives are now personally responsible for both having a data strategy, and keeping data secure. So it's really pushing the C-Level to be more responsible for the security of our estate, keeping our data safe, and reducing the amount of production data and testing systems.”

Thomas Kronawitter

Head of Data-Driven Applications & Services, Grenke GmbH

Toptip3

Reduce hidden risk with consistent visibility

Security and governance can no longer be siloed tasks. Combining performance, activity, and security monitoring gives teams a shared view of what’s happening across environments, making it easier to spot configuration drift, risky access changes, and compliance gaps before they become incidents.

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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.

“Everyone wants to move faster with AI, but few are truly ready for it. It isn't just about algorithms - it's whether your data, systems, and teams are prepared to support intelligent automation safely and effectively.”

Jeff Foster

Director of Technology and Innovation, Redgate

Toptip4

How to build the foundations for AI readiness

True readiness isn’t about predicting every change that AI will bring. It’s about being confident you can adapt when it does. That starts with clean, secure, well-governed data and teams that have the visibility, trust, and freedom to experiment safely. When those conditions exist, AI doesn’t disrupt your organization; it strengthens it.

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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.

“There’s clear progress in tooling and modernization. The bigger challenge now is consistency—making controls, governance, and delivery practices work reliably across increasingly complex environments. What stands out most to me is that many of these gaps are not isolated issues; they tend to surface together, especially where analytics, governance, data quality, and auditability intersect under regulatory scrutiny.”

Mri Pandit

Senior Manager, Navy Credit Union

• Data quality issues (47%)
• Difficulties moving and processing large volumes of data (36%)
• Data pipeline complexity or technical debt (33%)
• Lack of skilled data engineers or technical expertise (29%)
• Integration issues with source or target systems (24%)

Toptip5

How to improve the quality of data governance

As data moves through more tools, teams, and environments, consistent governance becomes critical to maintaining quality. With only 23% of organizations reporting formal data governance or quality frameworks, this guide outlines where to start, and how clearer roles, standards, and visibility help prevent quality issues from emerging during transformation.

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