Crucial strategies for minimizing expenses, mitigating licensing dangers, streamlining processes, and preparing your data infrastructure for the age of AI.
Each year, data increasingly becomes a cornerstone, vital to nearly all corporate functions. With the AI revolution advancing rapidly, it’s imperative for CIOs and other tech-savvy business leaders to meticulously optimize their data infrastructure.
An inadequately structured data system can hinder even the most powerful data assets. Therefore, as your company finalizes its 2026 plans, remember the pivotal role your database strategy plays in ensuring both immediate and sustained business achievement.
In order to successfully navigate this evolving landscape, organizations require a practical and implementable framework. This subsequent five-point checklist provides initial guidance for achieving database excellence, assisting CIOs, CTOs, IT leaders, and data professionals in reducing licensing vulnerabilities, streamlining processes, and readying their data architecture for the AI age.
Adopt community-driven open source to lower expenses and prevent licensing complications
A critical strategic choice for business leaders managing their database systems is to adopt community-governed open source. Although open solutions are generally superior to proprietary ones, it’s crucial to recognize that not all open source is created equal. As evidenced by licensing shifts from ostensibly “open source” entities such as Redis and Elastic, single-vendor open source offerings can arbitrarily transition to proprietary or more restrictive models, trapping numerous users.
However, with open source projects managed by communities or foundations, like PostgreSQL and Valkey, this unpredictability is eliminated. Organizations can operate confidently, assured that their chosen licensing terms won’t be altered by corporate directives.
While open source effectively lowers the total cost of ownership, its benefits extend beyond mere expense reduction. It provides the essential flexibility, independence, and liberty needed to future-proof your enterprise. Crucially, community-led open source fosters swift, collaborative innovation, where industry demands guide feature enhancements, guaranteeing ongoing applicability and effectiveness as technology advances.
Utilize platform engineering to optimize your database environment
An proliferation of databases is now commonplace. Developers can readily access numerous database technologies, each tailored for specific tasks. Yet, when teams deploy databases autonomously, lacking uniform controls, it results in fragmentation. This leads to inconsistent performance, variable security protocols, and erratic access behaviors.
Platform engineering provides an answer. By approaching the data platform as a product—complete with a dedicated service catalog, protective guidelines, and lifecycle policies—businesses can empower developers with self-service database functionalities while maintaining overarching governance and uniformity.
Implemented effectively, platform engineering delivers:
- Uniform, version-managed blueprints for every supported database.
- Explicit delineation of responsibilities between platform and application teams.
- Automated provisioning with pre-validated setups ensuring compliance, security, and optimal performance.
- Integrated resilience capabilities—such as backups, failover, and encryption—eliminating the need for developers to recreate them.
Consolidate observability and administration for a comprehensive insight into your database infrastructure
In an environment where organizations utilize PostgreSQL alongside MySQL, MongoDB, serverless cloud DBaaS, and specialized analytics platforms, achieving clear visibility is both essential and challenging. Teams frequently employ distinct monitoring tools for each system, leading to blind spots and protracted troubleshooting.
Consolidating observability unifies a disparate ecosystem. Through integrated tooling, third-party services, or a combination of both, seek solutions that provide support for multiple database types. Many contemporary tools and services focus exclusively on a single database management system, but over-reliance on such specialized options will exacerbate fragmentation within your environment.
An effective observability strategy in today’s landscape should, at a minimum, feature consolidated dashboards spanning various database engines and standardized metrics to facilitate direct comparisons. The expenses associated with isolated data are escalating. Streamline your operations proactively to prevent an accumulation of increasingly expensive inefficiencies.
Get ready for an environment dominated by AI processing demands
Enterprises should prudently develop infrastructure anticipating a future rich with AI workloads, where open source sets the benchmark for managing all data types, from structured transactional data to vector data and more. Assess and implement open-source databases that are ideal for AI tasks and incorporate vector search functionalities (e.g., PostgreSQL with pgvector). Verify seamless integration with prevalent data science platforms (e.g., Python, Jupyter, TensorFlow, PyTorch). Furthermore, seek open-source options that encourage expandability and robust integrations, ensuring database functionalities can naturally evolve and broaden alongside technological advancements.
The primary advantage open-source solutions like PostgreSQL provide for AI workload preparedness is their inherent capacity to adapt, change, and extend functionalities in sync with market demands. Through community-driven innovation, your organization will consistently remain at the forefront of adopting novel database features.
Utilize automation to expedite processes and broaden data accessibility
Database teams face immense pressure, handling tasks like performance optimization, capacity forecasting, pinpointing sluggish queries, incident response, and reconciling environmental disparities. Conventional monitoring tools typically produce alerts, yet seldom offer genuine insights or predictive capabilities.
AI-driven operational tools and various automation methods are rapidly emerging as significant competitive advantages for businesses aiming to enhance their database operations. Advanced systems can identify irregularities across logs, metrics, and query behaviors, suggesting improvements even before human engineers identify the problem.
Furthermore, automation empowers diverse business teams (e.g., data scientists, engineers, analysts, product owners) to swiftly experiment, develop, and refine. In contrast, manual provisioning and rigorous governance procedures impede progress. Automation facilitates rapid, secure, and widely accessible entry to data systems.
Nevertheless, exercising prudence is crucial when considering automation within the database domain. Given that few contemporary workloads can withstand interruptions, organizations ought to favor automation that augments human capabilities, rather than supersedes them—for instance, tools centered on observability. Automation capable of analyzing logs to detect patterns and inefficiencies will prove indispensable for future database management. Conversely, other forms of automation, like “self-healing” databases, still present excessive risks for most enterprises.
Lastly, verify that all automation deployed in your environment is transparent, auditable, and allows for human supervision, consistently favoring openness whenever feasible.
Integrated, AI-prepared, and open database infrastructures will thrive
As you strategize your database choices in the coming months and years, it is paramount for businesses to emphasize adaptability, independence, and AI preparedness to stay competitive in the fast-changing tech world. Factors that could undermine agility or responsiveness, like vendor dependency or restrictive proprietary licenses, can severely compromise an organization’s capacity to develop future-proof infrastructure.
Concurrently, with SaaS expenses continuously climbing and the proliferation of databases and related tools across organizations, managing the total cost of ownership is now paramount. Nonetheless, AI readiness remains a top concern for most enterprises, as delayed adoption could lead to a competitive disadvantage.
Across all these endeavors, open and integrated solutions prove superior. Open-source tools and cohesive infrastructure distinguish between isolated systems plagued by significant friction and agile database operations poised for future challenges.
—
The New Tech Forum serves as a platform for technology leaders—including vendors and external contributors—to delve into and analyze cutting-edge enterprise technologies with unparalleled insight. Our selection process is subjective, focusing on technologies we deem significant and most relevant to InfoWorld’s audience. InfoWorld does not publish promotional materials and retains editorial control over all submitted content. For all inquiries, please contact doug_dineley@foundryco.com.
