With every passing year, data continues to grow more important and more central to practically every aspect of corporate operations. As the AI race continues full-steam ahead, it will be mission-critical for CIOs and other technical business decision-makers to get things right when it comes to their data infrastructure.

Without the right plumbing, even the best, most robust data estate can become rusty. So, as your organization wraps up its 2026 planning, don’t lose sight of just how essential your database strategy is to both your near-term and long-term business success.

To navigate this shifting environment, enterprises need a pragmatic, actionable framework. The following five-part checklist offers a starting point for database success, helping CIOs, CTOs, IT executives, and data leaders reduce licensing risk, simplify operations, and future-proof their data infrastructure for the AI era.

Embrace community-led open source to reduce costs and avoid licensing risks

One of the most important strategic decisions that business leaders can make regarding their database estates is to embrace community-led open source. While “open” is almost always preferable to proprietary, it’s important to remember that not all open source is equal. As we’ve seen with the licensing changes made by “open source” organizations like Redis, Elastic, and others, single-vendor open source solutions can become proprietary (or simply more restrictive) on a whim, leaving many end users trapped.

With community or foundation-led open source projects, such as PostgreSQL and Valkey, this kind of uncertainty is removed, allowing organizations to rest easy knowing that the licensing they initially opted into will not be changed at the insistence of a board.

And although open source is an excellent way to drive down the total cost of ownership, going open source isn’t solely about cost cutting. Open source also offers a degree of flexibility, autonomy, and freedom that is essential to future-proof your organization. And perhaps most importantly, community-led open source comes with the power of rapid, crowd-sourced innovation, where industry needs drive feature development and ensure continued relevance and efficacy as technology evolves.

Lean on platform engineering to streamline your database stack

Database sprawl is now the norm. Developers have easy access to dozens of database technologies, each suited for different workloads. But when teams deploy databases independently, without consistent controls, it creates fragmentation. And that means uneven performance, inconsistent security standards, and unpredictable access patterns.

Platform engineering offers a solution. By treating the data platform as a product with its own service catalog, guardrails, and life-cycle policies, enterprises can provide developers with self-service database capabilities while retaining governance and consistency.

When done right, platform engineering offers:

• Standardized, version-controlled templates for each supported database.
• Clear definitions of what the platform team owns versus what application teams own.
• Self-service provisioning with pre-approved configurations for compliance, security, and performance.
• Built-in resilience features—backups, failover, encryption—that developers don’t have to reinvent.

Centralize observability and management for a unified view of your database estate

In a world where enterprises run PostgreSQL alongside MySQL, MongoDB, serverless cloud DBaaS, and specialized analytics engines, visibility becomes both mission-critical and hard to achieve. Teams often run multiple monitoring tools (one for each system), which creates blind spots and slows down troubleshooting.

Centralized observability brings coherence to a fragmented landscape. Whether it be via adjacent tooling, third-party services, or both, look for solutions that offer multi-database support. Many tools and services today go all-in on one database management system, but leaning too heavily on such specialized solutions will only further segment your ecosystem.

A modern observability strategy should include unified dashboards across multiple database engines and normalized metrics to enable apples-to-apples comparisons, for starters. The cost of siloed data is rising. Centralize your operations before the increasingly costly inefficiencies pile up.

Prepare for a world where AI workloads reign supreme

Organizations would be wise to build for a world in which AI workloads are abundant and open source is the gold standard for handling all data types from structured, transactional data to vector data and beyond. Evaluate and adopt open-source databases that are well-suited to AI workloads and include vector search capabilities (e.g., PostgreSQL and pgvector). Ensure compatibility with popular data science ecosystems (e.g., Python, Jupyter, TensorFlow, PyTorch). And look for open-source solutions that promote extensibility and integrations to ensure database capabilities can grow and expand organically with the evolving technological landscape.

The best thing that open-source solutions like PostgreSQL have to offer for AI workload readiness is the ability to evolve, transform, and expand functionality in step with industry needs. With community-led innovation, your organization will never find itself left behind in the pursuit of new database capabilities.

Leverage automation to accelerate operations and democratize data access

Database teams are under enormous pressure, burdened by performance tuning, capacity planning, diagnosing slow queries, responding to incidents, and managing cross-environment differences. Traditional monitoring tools generate alerts but rarely insights, and certainly not predictions.

AI-powered operations tools and other forms of automation are quickly becoming a major competitive differentiator for organizations looking to accelerate their database ops. Modern systems can detect anomalies across logs, metrics, and query patterns and recommend optimizations before human engineers spot the issue.

Automation also enables teams across the business (e.g., data scientists, engineers, analysts, product owners) to experiment, build, and iterate quickly. But manual provisioning and heavy governance checks slow everything down. Automation enables fast, safe, democratized access to data systems.

Caution is paramount, however, when looking at automation in the database space. Few modern workloads can tolerate downtime, so organizations should prioritize automation that enables, rather than replaces, humans—such as tools focused on observability. Automation that runs through logs and identifies patterns, inefficiencies, and the like will become vital to modern database management in the near future. Other forms of automation, on the other hand (such as “self-healing” databases) still pose too much risk for the average organization to tolerate.

Finally, ensure that all forms of automation used in your environment are transparent and auditable, and allow room for human oversight, once again opting for openness wherever possible.

Open, integrated, and AI-ready database systems prevail

When making your database decisions in the months and years ahead, it’s vitally important that businesses prioritize flexibility, autonomy, and AI-readiness to keep pace with today’s rapidly evolving tech landscape. Any factors that might compromise one’s agility or nimbleness, such as vendor lock-in or restrictive proprietary licensing, can seriously jeopardize one’s ability to future-proof infrastructure.

At the same time, as SaaS costs continue to soar and the number of databases and adjacent tools within organizations continues to balloon, managing total cost of ownership becomes mission critical. With that being said, AI readiness remains a priority for most organizations, which risk falling behind the competition as a result of late adoption.

In all these pursuits, open and integrated solutions prevail. Open-source tools and integrated infrastructure are the difference between siloed systems that suffer from immense drag and streamlined database operations that are ready for whatever the future may bring.

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