You’re collecting more data than ever—but slow queries, outdated reports, and scattered sources can drag everything down. 

Insights are only as fast as the infrastructure behind them. And as AI and machine learning reshape how organizations work, build, and sell, the need for fast, reliable analytics is only growing. 

If your data warehouse is slow, overloaded, or outdated, even the best AI models and analytics tools can’t deliver results. 

High-performance data warehousing ensures that your insights arrive in real time, at scale, so you can act while opportunities are still in front of you. 

Why data warehouse performance matters 

At the core of digital transformation is the ability to act on data—refining strategies, improving processes, and identifying new ways to grow. 

And even with rising cost controls, AI remains a top priority. A recent Gartner survey found that many CFOs are pulling back large-scale investments but doubling down on AI. As Alexander Bant, Chief of Research in the Gartner Finance Practice, put it: “Instead, they’re sharpening their focus (on AI)—shifting from broad experimentation to targeted enterprise use cases that offer measurable impact.” 

To make AI and analytics investments pay off, your data warehouse needs to keep up. That means being able to: 

• Handle complex, large-scale data volumes 

• Process thousands of requests in parallel 

• Maintain performance to power AI tools in real time 

Where traditional data warehouses fall short 

Automating manual processes creates a surge of valuable data, but not every data warehouse is built to handle it. Many legacy systems were designed for structured workloads, not today’s AI-powered demands. Common challenges include:

• Performance bottlenecks from slow query speeds and high latency 

• Limited scalability as data volumes and workloads grow 

• Rising costs from inefficient compute and storage usage 

• Vendor lock-in that limits integration with modern tools and hybrid environments 

In industries where speed matters— such as financial services (i.e., making investment decisions), healthcare (i.e., pulling patient data to determine treatments), retail (i.e., personalizing marketing campaigns), and manufacturing (i.e., reducing supply chain bottlenecks)—these limitations can stall real-time decisions and innovation. 

How to boost data warehouse performance for AI success

AI, machine learning, and business intelligence tools can only move as fast as the data infrastructure behind them. Without the speed and scalability to match, insights stall and opportunities get missed. 

To keep transformation moving, look for a data warehouse or analytics database that offers:

• Petabyte-scale performance: Run resource-intensive queries across structured and semi-structured data, at any scale. 

• Built-in analytics and ML functions: Reduce manual work with advanced capabilities like event pattern matching and spatial data mapping. 

• Elastic, workload-aware scaling: Separate compute from storage to assign the right resources to the right workloads. 

• Analyze data anywhere: Support hybrid environments and run analytics across data warehouses, lakes, and lakehouses. 

• Strong governance and security: Use role-based access controls, encryption, and auditing to keep data protected. 

Modern data infrastructure for AI-powered transformation 

Legacy systems can’t keep up with today’s transformation goals. A high-performance, modern data warehouse architecture, like OpenText™ Analytics Database, bridges the gap, powering: 

• Massive parallel workloads without slowdown. 

• Real-time insights for AI and analytics tools. 

• Innovation at scale, without vendor lock-in. 

With the right architecture, your data warehouse becomes more than a storage solution — it becomes a performance engine that drives measurable digital transformation outcomes. 

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