Master Your Data in the AI Era: Modern Architectures for Competitive Edge

Why It’s Time for New Data and AI Architectures

Your generative AI (GenAI) /AI initiatives may not be ready unless your data is ready. For data to be ready now and for the future, your IT strategy and data architecture need to be redefined. Modern data and AI architecture can determine whether your company thrives or treads water in today’s high-pressure, digital economy. Your role as a champion of innovation and data strategist sets the stage for how your downstream teams quickly adapt to fast-paced changes. And it also exploits existing and emerging cloud technologies to create lasting business value.

A holistic, comprehensive data management platform can help. It not only enables your systems to work together; it can also meet your data and analytics demands. And by treating data as a strategic asset, you can improve customer experience, accelerate AI-driven innovation, and increase business agility.

It’s no secret that delivering trusted data across your organization will help drive better decisions that can reduce operating costs and give you a competitive edge.

To support data-led digital transformation, modern data architectures need to reflect a new approach in data management. Unified data management consists of five pillars:

1.Data Platform: A GenAI and AI-powered, modular and enterprise-scale intelligent data platform that supports on-premises and multi-cloud architectures provides you flexibility in building modern architectural patterns. This includes data fabric, data mesh and the ability to modernize your data stack into a more cohesive and composable data first stack.

2.XOps (DataOps, MLOps, LLMOps, and FinOps): Automating the orchestration of data pipelines and data delivery workflows by employing DataOps, MLOps, LLMOps and InfoSecOps supports continuous delivery, analysis and monitoring. FinOps focuses on cost optimization recommendations in the cloud modernization journey.

3.AI, GenAI and machine learning (ML): Starting from data prep to model selection, model tuning and deployment, these critical capabilities require addressing the challenge of exponential growth. Modern data and AI architectures will help organizations to build AI systems in a responsible and governed manner

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