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Enabling Banks to Improve Analytics and Decision Intelligence Through Better Data

Celent

Banks are among the most data-intensive institutions—almost everything a bank does is based on digital processing and data analytics, including advanced analytics and models. For banks, their data is the business.

In recent years, the banking industry has experienced several simple but momentous trends that drive significant investment in data platforms and management:

  • Banking is increasingly digital and data dependent. Not only is more data used in processing, but more data is collected and created by banks—and increasingly from new sources.
  • Digital banking strategy is also increasingly cloud-first or must at least integrate on-premise data with data deployed or distributed in the cloud.
  • The insights gleaned from that data, and the potential to use that data in advanced analytics and AI, can be a game changer for banks. Data and AI are the building blocks for future banking products and services, and the quality and performance of quant and AI models becomes more critical.

However, banks still face significant challenges to manage data effectively. Celent research also shows that many banks have either started the journey or plan to implement a data fabric solution to address problems in managing data. It is in this context that Celent has written this Solution Brief about InterSystems IRIS data platform.

Další Zdroje, Které By Se Vám Mohly Líbit

Mar 03, 2026
White Paper
For banks seeking to modernize their decision-making capabilities, smart data fabrics offer a powerful path forward.
Mar 03, 2026
Solution Summary
A smart data fabric from InterSystems streams high-quality, real-time data into model development, deployment, and performance monitoring— overcoming the limitations of silos for operationalizing AML/KYC, CCAR/ CECL, credit, liquidity, and reporting at scale while maintaining data lineage and governance.
Mar 03, 2026
Solution Summary
A smart data fabric helps banks improve decision quality and business performance by eliminating data duplication, reducing data latency, and unlocking enterprise-wide intelligence.
Feb 27, 2026
Cutter Associates Interview
This video explores how asset management firms can overcome common data and infrastructure challenges that often slow down AI innovation. Kathy McDermott, Managing Director at Cutter Associates, is joined by Irene Galperin, Senior Advisor in Financial Services at InterSystems, to discuss why high-quality, accessible data is foundational for successful AI use cases. Together, they unpack key roadblocks such as data silos, legacy architectures, and data quality issues, then shift to practical solutions—highlighting how a data fabric architecture provides the trusted, unified data foundation that GenAI requires to help firms accelerate AI adoption without a disruptive overhaul of their existing data environment.
Dec 29, 2025
Crisil Coalition Greenwich
The move to T+1 settlement in North America was just the beginning. Now, the industry is buzzing about what it will take to reach true T+0—instantaneous settlement. But how close are firms to making this a reality?
Dec 08, 2025
Financial Services
What’s holding asset management firms back from moving beyond AI and GenAI prototypes and truly scaling AI innovation?
Oct 08, 2025
Market Note
This TabbFORUM report explores how AI is moving from cool novelty to core infrastructure, what’s working in production today, and where the next wave of adoption will determine lasting competitive advantage.
Jul 02, 2025
Celent
A Celent Research Survey of Over 100 Retail Banking Executives in North America and the United Kingdom Decision Intelligence in Banking
Apr 04, 2025
Optimizing Analytics Development in Financial Services
Smarter Processes, Smarter Decisions
Apr 04, 2025
Real-time Visibility & Integrated Decision Intelligence
Risk management has evolved beyond just mitigating threats—strong risk management practices can be a competitive advantage. In today’s volatile markets, asset managers are tasked with navigating unprecedented complexity, adapting to tighter regulations, and leveraging data to build resilience and drive growth. Yet, traditional methods fall short, leaving many struggling with disjointed systems, siloed data, and delayed insights.

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