Cloudera study underscores the key to successful AI is in modern data architecture, unified data management, and versatile data platforms
Cloudera, the data company for trusted enterprise AI, today announced the findings from its survey, Data Architecture and Strategy in the AI Era. Conducted on behalf of Cloudera by Foundry Media, the survey polled over 600 data leaders and senior IT decision makers about the state of modern data architectures and how the rise of artificial intelligence (AI) is impacting data strategies. Most notably, the survey 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 generative AI is highlighting 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, there are foundational data roadblocks that 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. “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.”
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
- The key to modern data architecture is a single data platform that works seamlessly across public cloud and on-premises. 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 are holding back end-to-end data management needed 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 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.”
To download the full report, click here.
Methodology
The Foundry research surveyed more than 600 IT decision-makers from the U.S., Europe and APAC. The target company size was organizations with annual revenue of more than $500 million or more than 1,000 employees globally. All participants were data leaders and IT decision-makers with titles of director and above (or equivalent). They had to have a prominent role in the selection of data-related products and services, including, but not limited to, infrastructure.
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