Want AI? Here’s how to get your data and infrastructure AI-ready

Artificial intelligence (AI) is reshaping our world. In business, this puts CIOs in one of the most pivotal organizational roles today. CIOs are responsible for much more than IT infrastructure; they must drive the adoption of innovative technology and partner closely with their data scientists and engineers to make AI a reality–all while keeping costs down and being cyber-resilient. 

That’s a big role. And it’s getting bigger. AI and its unprecedented potential to transform business is skyrocketing a CIO’s impact and responsibility within an organization.

For CIOs, building an AI-enabled organization at scale can be challenging. However, organizations are more prepared than they might think, thanks to data they already have. The key is to make data actionable for AI by implementing a comprehensive data management strategy. That’s because data is often siloed across on-premises, multiple clouds, and at the edge. Getting the right and optimal responses out of GenAI models requires fine-tuning with industry and company-specific data.  And companies need the right data management strategy and tool chain to discover, ingest and process that data at high performance.

Without data, there is no AI, and breaking down these silos is the first step towards bringing AI to your data wherever it lives. Successful data management encompasses the ability to handle extreme data growth, regardless of data type and location, while managing the lifecycle of this data appropriately. Traditionally, data management and the core underlying infrastructure, including storage and compute, have been viewed as separate IT initiatives. For CIOs to set up their organizations for success and bring AI to their data, a more holistic view of IT infrastructure and data management is needed. 

Building an AI-enabled solutionIn today’s rapidly evolving IT environments, crafting an AI-ready solution can be a competitive advantage. Beyond the traditional considerations of speeds and feeds, forward-thinking CIOs must ensure their compute and storage are adaptable. That includes solid infrastructure with the core tenets of scale, security, and performance–all with optimized costs. This infrastructure must be suited to handle extreme data growth, especially with unstructured data. An estimated 90% of the global datasphere is comprised of unstructured data¹. And it’s growing rapidly, estimated at 55-65%² year-over-year and three times faster than structured data. 

Unstructured data is often not AI-ready, yet it holds some of the greatest value for organizations. Data scientists and IT teams must work together to prepare all their data and make it actionable, leveraging scalable, high-performance infrastructure to drive AI forward. Once AI-optimized compute and storage have been established, CIOs can begin to implement a modern data lakehouse as part of their overall data management strategy. Data scientists and engineers spend nearly 80% of their time on data preparation³

rather than actual analysis. A modern data lakehouse empowers the utilization of open data formats and distributed datasets across core, edge, and multicloud environments. This eliminates the hassles of data silos and makes data accessible for model training, analytics, and real-time inferencing.  As a result, data scientists and engineers can query their data across environments and gain AI insights faster.

You might be interested in Dell’s upcoming 30-minute online event about “AI Anywhere on Data Everywhere” on December 7^th at 11 a.m. CT. I’ll be there and would love to have you join me.