Global Survey Reveals Unifying Data Across Various Platforms is Crucial For Effective AI

The astonishing rise of GenAI has made it clear that organizations must tap into their data’s hidden value if they want to compete in the AI era. The ability of an organization to find and expose data to all those who need it within their organization could separate winners from the others.

Foundry Media has released the findings of a new report on behalf of Cloudera to help us understand the foundational requirements for organizations looking to achieve effective AI.

The Data Architecture and Strategy in the AI Era study highlights that treating data as a critical asset is essential for an organization looking to harness the power of AI and a modern data architecture that is grounded in business strategy is the foundation.

The Foundry study was based on a survey of 600 IT decision-makers from around the globe. To ensure the data reflects the perspective of the organization, only those respondents who held senior positions with titles of director or above were included in the research.

The study shows that organizations see a wide range of benefits to modern data architecture including simplifying data and analytics processes (40%), gaining flexibility in handling different types of data (38%), and enhancing data governance and security (37%). One of the key requirements of modern data architecture is to have a single data platform that works seamlessly across public cloud and on-premises infrastructure.

Two-thirds of the respondents agreed that data lakehouses help reduce pipeline complexity, however only two in five enterprises currently use them. This indicates that organizations are finding it challenging to integrate new data management platforms.

The report also reveals that 90% of data leaders believe that unifying the data lifecycle on a single platform is critical for analytics and AI. It can provide the ideal springboard to advanced analytics and AI.

According to the study, another vital component for unlocking effective AI is embracing a hybrid data management approach. Only one-third of the respondents currently deploy hybrid or multi-cloud data architectures. However, an overwhelming majority (93%) of respondents believed that multi-cloud/hybrid capabilities for data and analytics are key for an organization to adapt to change.

Organizations that are using the cloud reported higher overall use of advanced analytics and AI compared to those that are entirely on-premises. However, moving to cloud comes with its own set of challenges including difficulty in migrating data across platforms and managing disruption to business operations.

On-premises infrastructure also offers advantages. The report shows that organizations whose infrastructure was primarily on-premises had a higher use of technologies in preparing data for analysis. That could be why one-fifth of organizations are planning to repatriate data from the public cloud to on-premises infrastructure.

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The findings are an indicator that having a hybrid platform could be the best solution as it offers the choice of where to deploy analytics and AI based on the changing needs of the organization.

“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.

Ricky further added, “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.”

The Foundry study shows that a flexible and scalable cloud management strategy can turn data into insights by facilitating analytics and AI. It also highlights that there is a shift towards seamless, secure data platforms to accelerate AI adoption and enable strategic business transformation. Organizations that are able to capitalize on these opportunities will be well-positioned to respond to the evolving nature of the AI landscape.

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