Unifying Data Lifecycle On a Single Platform is Critical for Analytics and AI

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The rise of artificial intelligence (AI) is impacting data strategies. Cloudera survey Data Architecture and Strategy in the AI Era revealed that 90% of IT leaders believe that unifying the data lifecycle on a single platform is critical for analytics and AI. 

The proliferation of GenAI highlights the importance of trustworthy data because AI insights are only as powerful as the data feeding them. 

However, the survey revealed respondents face obstacles in their AI journeys due to the quality and availability of data (36%), scalability and deployment challenges (36%), integration with existing systems (35%), change management (34%), and model transparency (34%). This demonstrates that while many organizations may be investing in AI, foundational data roadblocks must be addressed. 

“As more enterprises look to transform their businesses to build digital and AI ready solutions for their customers, they are choosing a hybrid and multi-cloud strategy, which in turn creates ‘data sprawl and architectural overruns’ across LOBs, functional units, business applications and practitioner teams,” said Cloudera Chief Strategy 

Officer Abhas Ricky. “To effectively leverage AI capabilities, organizations need to design and embed standardized, use case-centric data architectures and platforms that will allow disparate teams to tap into all of their data – no matter where it resides — whether on-premises or in the cloud.”

Additional key findings from the survey revealed three foundational requirements for organizations looking to achieve effective AI:

A modern data architecture grounded in business strategy 

A single data platform that works seamlessly across the public cloud and on-premises is the key to modern data architecture. When it comes to the benefits of modern data architectures, the most popular responses were simplifying data/analytics processes (40%), followed by gaining flexibility in handling all types of data (38%).

Unified data management 

Today’s organizations need flexible and scalable cloud management technologies that provide the tools to turn information into insights. When it comes to factors that hold back end-to-end data management required for AI model development, 62% of respondents said it’s the volume and complexity of data, 56% said data security, and 52% said governance and compliance.

Versatile and secure data platforms

From a long-term perspective, embracing a hybrid data management approach, including both on-premises and public cloud deployments, is the preferred data and analytics strategy – 93% of respondents agree that “multi-cloud/hybrid capabilities for data and analytics are key for an organization to adapt to change.”

“At its core, enterprises want to achieve top-line results from their data strategy and supercharge their AI initiatives at a price point that is not prohibitive to the bottom line,” added Abhas. “Organizations looking to get the most out of their data need to rapidly build and deploy a modern platform and AI architectures that support that mission.” 

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