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The surprising rise of GenAI has made it clear that organizations need to leverage the hidden value of their data to compete in the AI era. An organization’s ability to find and publish data to everyone in the organization who needs it can be the difference between winners and everyone else.
Foundry Media announced the findings of a new report on behalf of Cloudera to understand the fundamental requirements for organizations seeking to achieve effective AI.
A study of data architecture and strategy in the age of AI reveals that treating data as a critical asset is essential for organizations looking to harness the power of AI, and that a modern data architecture based on business strategy is the foundation. It is emphasized.
Foundry’s research is based on a survey of 600 IT decision makers around the world. To ensure that the data reflects the organization’s perspective, only respondents who held senior positions with titles of director or higher were included in the survey.
The survey found that organizations are looking to modernize their business, including simplifying data and analytics processes (40%), gaining flexibility in handling different types of data (38%), and strengthening data governance and security (37%). We found that we were seeing a wide range of benefits to our data architecture. One of the key requirements for modern data architectures is having a single data platform that works seamlessly across public cloud and on-premises infrastructure.
Two-thirds of respondents agreed that a data lakehouse helps reduce pipeline complexity, but only two in five companies currently use a data lakehouse. This indicates that organizations are finding it difficult to integrate new data management platforms.
The report also reveals that 90% of data leaders believe consolidating the data lifecycle into a single platform is important for analytics and AI. It can provide an ideal starting point for advanced analytics and AI.
According to the study, another key element to leveraging effective AI is adopting a hybrid data management approach. Only one-third of respondents currently have a hybrid or multi-cloud data architecture in place. However, the overwhelming majority of respondents (93%) believed that multicloud/hybrid capabilities for data and analytics are key to helping organizations adapt to change.
Organizations using the cloud reported higher overall usage of advanced analytics and AI compared to those using entirely on-premises. However, moving to the cloud comes with its own set of challenges, including the difficulty of moving data between platforms and dealing with business interruptions.
On-premises infrastructure also has advantages. This report shows that organizations whose infrastructure is primarily on-premises use technology more when preparing data for analysis. This may be why one in five organizations plans to move data back from the public cloud to on-premises infrastructure.
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Our findings indicate that a hybrid platform that allows you to choose where to deploy analytics and AI based on an organization’s changing needs may be the best solution.
“More companies looking to transform their businesses to build digital and AI-enabled solutions for their customers are opting for hybrid and multi-cloud strategies. “Data sprawl and architectural overruns are occurring,” said Abhas Ricky, chief strategy officer at Cloudera.
Ricky continued, “To effectively leverage AI capabilities, organizations need standardized, case-centric data architectures and platforms that make all their data available to disparate teams, no matter where it resides. must be designed and incorporated,” he added. On-premises or in the cloud. ”
Foundry research shows that a flexible and scalable cloud management strategy can accelerate analytics and AI to turn data into insights. It also highlights the ongoing transition to seamless and secure data platforms to accelerate AI adoption and enable strategic business transformation. Organizations that can capitalize on these opportunities will be well-positioned to respond to the evolving nature of the AI environment.
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