Why It’s Time to Move Beyond Legacy Platforms

What market trends and transformations are keeping you up at night? For Financial Service Institutions (FSIs) the list ebbs and flows as wildly as winds in storm. And the needs, from customer expectations to regulator demands to dealing with even more diabolical fraud risks, aren't slowing down.

Unfortunately, many banks, insurers, capital markets firms, and credit card providers find themselves unable to shift and adapt quickly enough. A significant reason: their core analytics still run on legacy enterprise data warehouses (EDWs) designed decades ago. These platforms were built for predictable, batch-oriented reporting; not today’s data-intensive, real-time financial services environment. 

So, what are you doing to modernize and keep up? A critical function to focus on is your enterprise data warehouse (EDW). This is no longer simply an IT refresh. It’s a strategic business imperative.

The Unfortunate Limits of Legacy Data Warehouses in FSI

Legacy enterprise data warehouses were built for a different era, one defined by batch processing, structured data, and static reporting. While they continue to support basic regulatory and financial reporting, they are increasingly misaligned with how modern financial services operate. Think brick cell phone. It works, but...

As data volumes, varieties and velocities explode and analytics become real time and AI-driven, FSIs are running into fundamental constraints:

• Performance bottlenecks at scale
  Risk calculations, fraud analysis, stress testing, and customer analytics now span billions to trillions of records. Legacy EDWs rely heavily on pre-aggregates, indexes, and tuning which cause query performance to degrade (by as much as 30% in some studies) as data grows and questions become more complex.
  
• Costs rise faster than value
  Licensing models tied to cores or nodes, combined with over-provisioning for peak workloads, make legacy EDWs expensive to scale. Storage inefficiencies further inflate costs as institutions retain years, or decades, of historical data.
  
• Rigid architectures limit agility
  Modern FSI analytics requires working with semi-structured data, streaming inputs, and evolving schemas. Legacy platforms struggle to adapt, forcing teams to bolt on additional systems and fragment their data architecture.
  
• Operational overhead slows teams
  Keeping legacy EDWs performant requires constant tuning, manual optimization, and specialized DBA expertise. Gartner estimates this unnecessary overspend to be as much as 40%! This operational burden delays insights and diverts skilled teams away from higher-value analytics initiatives.
  
• AI and Advanced Analytics are afterthoughts
  Most legacy warehouses were never designed for machine learning or advanced analytics. As a result, data must be extracted and moved to external platforms—introducing latency, security risk, and governance complexity.

For financial institutions competing on speed, insight, and trust, these constraints directly impact revenue, risk management, and customer experience.

What EDW Modernization Really Means for Financial Services

Enterprise data warehouse modernization is not just about migrating data from one platform to another. For FSIs, it means:

• Enabling real-time and near real-time analytics across massive datasets
• Supporting regulatory reporting and ad‑hoc analysis on the same platform
• Running advanced analytics and AI directly where the data lives
• Lowering total cost of ownership (TCO) without sacrificing performance or control
• Maintaining enterprise-grade security, governance, and auditability

Additional food for thought: A Nucleus Research analysis shows that organizations achieve $3.44 in benefits for every dollar invested in data warehousing technologies, with an average payback in ~7.2 months.

This is where OpenText Analytics Database (Vertica) stands apart from legacy warehouses and even many newer cloud-only alternatives.

OpenText: A Modern Analytics Database Built for FSI at Scale

OpenText Analytics Database (Vertica) is a high-performance analytics database purpose-built for large scale, mission critical analytics. It has been trusted for years by leading financial institutions for fraud detection, risk analytics, trading surveillance, and regulatory workloads.

1. Extreme query performance on massive financial data

The platform’s massively parallel processing (MPP) and advanced columnar architecture are designed for complex analytical queries across trillions of rows. FSIs can:

• Run intraday risk and exposure calculations without reaggregates
• Analyze years of transaction history in seconds
• Support thousands of concurrent users without query degradation

This performance is delivered without constant tuning, freeing teams to focus on insights instead of infrastructure.

2. Lower, predictable TCO for long term analytics workloads

Legacy EDWs often become cost centers as data grows. OpenText changes the economics of analytics:

• High compression dramatically reduces storage costs
• Efficient compute utilization minimizes infrastructure sprawl
• Fewer indexes and aggregates lower operational overhead

For financial organizations running large, persistent datasets such as transactions, trades, or customer interactions, OpenText Analytics Database delivers a significantly lower TCO over time.

3. AI-Ready analytics without data movement

FSIs are increasingly embedding AI into core workflows: fraud detection, credit scoring, AML monitoring, and personalization. OpenText Analytics Database enables this by:

• Supporting in-database machine learning and advanced analytics
• Allowing data scientists to work directly on governed, production data
• Eliminating costly and risky data movement to external platforms

This shortens the path from model development to production—critical in fast moving financial markets.

4. Deployment flexibility that fits FSI reality

Unlike legacy and cloud-only warehouses, OpenText Analytics Database supports true deployment flexibility, enabling FSIs to run the right workload in the right environment:

• On‑premises for strict regulatory or data residency requirements

• Private or public cloud for elasticity and modernization
• Hybrid architectures to support phased migrations and cloud repatriation

FSIs can modernize on their own terms—without being locked into a single deployment model.

5. Enterprise-grade Security and Governance

Security and compliance are non‑negotiable in financial services. OpenText Analytics Database is built with:

• Robust role‑based access control
• Encryption at rest and in transit
• Fine-grained auditing and data lineage support

This ensures sensitive financial data remains protected while still being accessible for analytics.

From Legacy EDW to Modern Analytics Platform: A Practical Path

FSIs don’t need a disruptive “rip and replace” approach. Many institutions modernize incrementally:

1. Offload high-volume, performance intensive analytics from legacy EDWs
2. Consolidate risk, fraud, and regulatory workloads onto OpenText Analytics Database
3. Enable AI and advanced analytics directly in the data warehouse
4. Gradually retire legacy platforms as confidence and value grow

This approach reduces risk while delivering measurable business impact early.

The Bottom Line: EDW Modernization Is a Competitive Advantage

In financial services, data is not just an asset—it’s a differentiator. Legacy data warehouses can no longer keep up with the speed, scale, and intelligence the industry demands.

By modernizing the EDW with OpenText Analytics Database, financial institutions gain:

• Faster insights on massive datasets (up to 25x faster for some)
• Lower and more predictable analytics costs 
• AI‑ready infrastructure for the next generation of use cases
• Flexibility to adapt to regulatory and market change
• Enterprise-grade security and governance to protect sensitive financial data

EDW modernization isn’t about keeping the lights on. It’s about enabling the future of financial services analytics.

Interested in learning how leading FSIs are modernizing analytics with OpenText Analytics Database (Vertica)? Let’s start the conversation.

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