TL;DR
- Unified, AI-optimized storage is the foundation for smooth, GPU-fed data pipelines
- Getting data AI-ready means storing it where AI can reach it, preparing and orchestrating it for AI use cases, and protecting sensitive enterprise data
- Dell's Data Engines streamline data ingestion, refinement, search, and analytics across structured and unstructured sources
- Built-in cyber resilience and governance keep mission-critical AI data secure and compliant
Global AI adoption is showing no signs of slowing down. Findings from Forrester’s 2025 State of AI Survey showing that over 70% of enterprises have generative or predictive AI in production.
Yet despite bullish adoption rates, many IT leaders still face acute problems when it comes to pushing AI projects from pilot to production. Indeed, many are still mired at the starting line.
A key factor here lies in data quality, or lack thereof, and this is by no means a new challenge for enterprises. Indeed, research from McKinsey in 2023 during the early days of the generative AI boom identified poor data as a key factor in AI project failures.
“Your data and its underlying foundations are the determining factors to what’s possible with generative AI,” the company said at the time.
More than three years on, and the situation is much the same. Findings from IDC’s 2026 CIO Agenda Predictions report show that “data debt” is becoming a recurring pain point for IT leaders.
What is data debt?
Data debt refers to a range of factors impeding AI innovation, including poor data quality or the fact that enterprises are contending with siloed datasets and infrastructure environments.
The stakes here are huge, according to IDC. Poor quality data undermines model performance, the report noted, and enterprises that fail to address this issue could face 50% higher AI project failure rates.
“These findings reinforce that scaling AI requires disciplined investment in data foundations and integrated platforms, and that postponing these fundamentals risks turning AI ambition into sustained operational friction,” the report states.
How to get your data into shape
With this in mind, IDC strongly urges IT leaders to sharpen their focus - and investment - in “unified platforms” designed to help support AI projects.
That’s where solutions such as the Dell AI Data Platform represent a key differentiator for enterprises either beginning their journey, or seeking to take AI innovation to the next level.
Here are three ways that the Dell AI Data Platform can help get your data AI-ready.
1. Store data where AI can use it
Fast, reliable access to data sources is critical in AI training and inference - which makes specialized, unified storage infrastructure, built for ease of use and scale, foundational to AI readiness.
The Dell AI Data Platform is integrated with a range of the company’s core products, including Dell ObjectScale storage, and Dell PowerScale – a scale-out NAS platform designed to simplify unstructured data management.
According to Dell, the system acts as a “federated control plane for accessing and processing data wherever it already lives”.
“It federates access to your existing databases, data lakes, object stores and file systems,” the company said in an April 2026 blog post.
“You connect to the data where it is, preserve your current governance and analytics tools and use the platform to orchestrate GPU-accelerated data preparation and retrieval across that landscape.”
Simply put, with Dell AI Data Platform, enterprises are able to consolidate access to storage infrastructure through one single platform – a key differentiator in tackling a recurring challenge when it comes to data access: siloes.
Research from IDC specifically highlights siloed data as one of the “top barriers” to AI innovation, mainly due to the fact that it impedes connections between storage pools and the GPU clusters which underpin AI training or inference processes.
Indeed, the Dell AI Data Platform is designed specifically to “break down these barriers” by streamlining critical data pipelines. The platform is highly flexible in this regard, enabling teams to break down silos across file, object, and structured data sources.
2. Prepare and orchestrate data for AI use cases
The Dell AI Data Platform boasts an array of features and capabilities designed for data preparation and orchestration. These ‘Data Engines’ are described by Dell as the “brain of the platform” and provide IT teams with intuitive automation capabilities aimed at reducing manual toil and streamlining processes.
“Data engines are central to accessing and preparing your information for AI,” the company notes in official materials. Simply put, these tools help users collect data from applications, devices, and storage locations, then “clean, organize, and enhance it”.
“This makes your data ready for analytics, model training, and real-time decision-making,” the company adds.
Chief among these is the Dell Data Orchestration Engine, which aims to help IT teams operationalize unstructured data for AI use cases. This feature works by automating data discovery, labeling, and enrichment, and is used to transform unstructured, structured, and multi-modal datasets.
Other data engines integrated within the Dell AI Data Platform include the Dell Data Search Engine. Powered by Elastic, this streamlines unstructured data ingestion, indexing, and instant asset search capabilities for IT teams.
In an example of this feature in action in an August 2025 blog post, Dell noted: “Editors use natural language search powered by Elasticsearch vector database to pinpoint the perfect scene, eliminating the need to hunt through countless folders.”
These capabilities were further enhanced through the use of Nvidia’s Omniverse libraries and AI models such as USD Search. Dell noted this helped enable “precise, context-aware searches across complex 3D asset libraries”.
Elsewhere, the Dell Data Processing Engine and Dell Data Analytics Engines play equally important roles in data preparation.
The first of these, as explained in a NAND Research Report detailing the Dell AI Data Platform, is the “workhorse that transforms data”. This helps transform “raw inputs into structured, usable data products” through data enrichment and tagging, thus delivering reliable, high-quality data for use in AI and analytics models.
The Data Analytics Engine, meanwhile, is designed to tackle data sprawl. Based on Starburst technology, this feature allows teams to run SQL queries across “multiple, diverse data sources” including databases, cloud storage, data lakes, and even legacy systems.
“Instead of moving or duplicating data, the data analytics engine queries data in place, reducing cost and complexity,” the NAND report notes.
Together, these engines give enterprises a complete data foundation for preparation and orchestration – so models train faster, retrieve better, and produce more reliable outputs.
Protect sensitive enterprise data
The Dell AI Data Platform provides enterprises with an array of built-in cyber resilience capabilities aimed at protecting users from threats such as ransomware and data poisoning.
This includes access controls, encryption, data masking, and automated threat detection tools, all of which are aimed at protecting mission-critical data in the event of a security incident or leak. In the event of an attack, data isolation features also safeguard data, according to Dell.
“This ensures your data remains protected and accessible, even in the event of a cyber attack, allowing you to operate with confidence,” the company said in official materials.
Running parallel to cybersecurity-related features, the Dell AI Data Platform also places a strong focus on compliance and governance features, as a NAND Research report notes.
“Cyber protection and compliance enforcement are embedded at the storage and metadata level, minimizing risks associated with AI model leakage, hallucination, or data corruption,” the report states.
You can find out more about the Dell AI Data Platform on the Dell website.
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