TL;DR
- Data quality has a direct impact on the success of AI deployments
- Intuitive data management and automation capabilities help bridge gaps, clean up data, and deliver tangible results
- High-performance infrastructure underpins any successful AI project
AI is by no means a plug-and-play solution and requires significant investment and expertise across a range of areas.
From networking and storage infrastructure to data management and analytics capabilities, AI projects can be daunting for enterprises – and many are encountering acute challenges in moving from pilot to production.
MIT’s 2025 report found that 95% of AI pilot projects fail, while separate McKinsey research shows that just 7% of companies have successfully scaled AI across their organization.
A key factor lies in "AI readiness", according to McKinsey. Put simply, many organizations have not done the necessary groundwork to ensure that their data is cleaned up and prepared for AI use.
Other considerations, such as security and governance, are also often overlooked during the early stages of AI adoption. This creates a confluence of issues that impedes progress and, in many cases, ultimately results in failure.
Given that AI systems rely heavily on unstructured data, which Forrester describes as the “messy stuff” including video, text, images, and even behavioral signals, it’s no surprise that IT leaders encounter challenges when embarking on AI adoption projects.
This is where solutions such as the Dell AI Data Platform can help enterprises make sense of their data estate in preparation for AI adoption.
The Dell AI Data Platform allows IT leaders to consolidate their IT environments within a unified platform, with tools designed to prepare, manage, secure, and maximize the value of their data
How do I clean up my data?
When embarking on an AI adoption project, one of the first questions many IT leaders ask is how to clean up data in preparation for its use.
Data quality is paramount in this process: if you put garbage in, you will get garbage out. Nearly two-thirds of CEOs cite low-quality or disconnected data caused by siloed infrastructures and fragmented technology stacks as a major barrier to scaling AI, according to Dell’s summary of Futurum Group and Kearney research.
“High-quality data determines how well an AI model can perceive, predict, and act—all critical performance criteria,” Dell notes. “Without sound data, your AI foundation will collapse.”
According to a recent eBook published by Dell and Nvidia, there are several steps IT leaders can take to begin cleaning their data, including identifying recurring issues.
"Accelerating AI outcomes starts with clean, well-labeled, and accessible data," the eBook states. Put simply, IT leaders should assess and audit the quality of their data from the outset.
Common problems with data quality include:
- Duplicate datasets
- Missing values
- Syntax errors
- Irrelevant data
- Inconsistencies
These are all questions IT leaders must consider when auditing their data, but assessing data management capabilities is equally important. This allows enterprises to establish a clear baseline in terms of their expertise and readiness.
To underscore the point, Dell’s own guide to AI and data, ‘The IT Leader’s Guide to Feeding AI High-Quality Data’, says: “Establishing organizational standards for data structure, consistency, and completeness helps ensure the models your business relies on are learning from the right signals.”
How can the Dell AI Data Platform help?
The Dell AI Data Platform includes tools designed specifically for this process, such as the Dell Data Processing Engine. Powered by Apache Spark, this engine helps enrich and transform new data sources.
According to Dell: “Data is efficiently organized into Parquet columnar format, optimizing storage and query performance.
"This engine supports a wide range of use cases, including ETL, advanced analytics, and machine learning, enabling teams to derive insights and build models faster."
The Dell Data Search Engine also plays a key role in discovery, retrieval, and making unstructured data usable. This solution allows users to parse and index unstructured data, making it more easily searchable by adding metadata tags.
What does clean and structured data look like?
In this context, clean data is accurate, consistent, free from errors and duplication, and ready to be used in AI systems.
A fundamental distinction is that clean, structured data is easier to identify and retrieve because it is organized using labels, definitions, and consistent structures.
What kind of infrastructure and tools do I need to prepare my data?
The data pipelines that feed generative and agentic AI applications are a key component in the success of any adoption project, however. Clean, usable data will have little impact if an organization is still contending with fragmented, disparate siloes across its IT architecture.
That is where the Dell AI Data Platform is a key differentiator for enterprises. As a unified platform, the solution allows organizations to consolidate data sources and create a more comprehensive view of their greatest asset in the age of AI.
The Dell Data Orchestration Engine is a critical tool in this regard. This platform helps consolidate data ingestion, preparation, retrieval, and inference within a single end-to-end pipeline.
This not only has a direct impact on data flows and broader AI innovation, but also delivers benefits in governance and security. Visibility is crucial to ensure that mission-critical data remains safe and secure.
“Simplifying how data moves, how it’s processed, and how it’s governed ensures systems can scale without adding complexity,” Dell notes. “A unified, flexible approach enables faster experimentation, better performance, and long-term adaptability.”
From a broader infrastructure perspective, best-in-class AI storage solutions are also a vital component of AI innovation. Dell PowerScale, the core storage foundation of the Dell AI Data Platform, is designed specifically to support enterprises throughout their adoption journey.
The storage platform offers the flexibility and scalability required to meet evolving needs as AI projects mature and expand in scope. PowerScale is intricately woven within the Dell AI Data Platform, once again providing users with a unified ecosystem designed for end-to-end project delivery.
If you think the Dell AI Data Platform is the right solution for your business, find out more on the Dell website.
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