Cloudera, a data company specializing in trusted enterprise AI solutions, unveiled insights from its latest survey, "Data Architecture and Strategy in the AI Era," conducted in collaboration with Foundry Media. The survey asked data leaders and senior IT decision-makers about the current state of modern data architectures and the evolving impact of artificial intelligence (AI) on data strategies.

The survey's most striking revelation is that 90% of IT leaders emphasize how critical it is to consolidate the data lifecycle onto a single platform for analytics and AI purposes.

As the adoption of generative AI continues to rise, the significance of reliable data becomes increasingly apparent.  AI insights are only as robust as the data fueling them. However, respondents identified several obstacles hindering their AI initiatives, including concerns related to data quality and availability (36%), scalability and deployment challenges (36%), integration with existing systems (35%), change management (34%), and model transparency (34%).

This proves there are still foundational data roadblocks that organizations must address if they want to unlock the full potential of AI investments.

"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. "In order for them 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."

To achieve this, organizations need to start with a modern data architecture. Implementing a unified data platform that seamlessly operates across public cloud and on-premises environments is what’s needed. Benefits of modern data architectures include simplification of data and analytics processes (40%) and increased flexibility in handling diverse data types (38%).

Organizations must also prioritize unified data management. They need agile and scalable cloud management technologies to transform data into actionable insights.  Most respondents identified challenges such as the volume and complexity of data (62%), data security (56%), and governance and compliance issues (52%) as hindering end-to-end data management necessary for AI model development.

Finally, versatile and secure data platforms are needed for long-term success. A hybrid data management approach that combines on-premises and public cloud deployments is a top choice for 93% of respondents.

"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 that are 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. Cloudera is committed to helping customers tackle their toughest data and AI challenges as the industry's only hybrid, multi-cloud data platform for data anywhere."