AI and data have a symbiotic relationship

In a world increasingly driven by data, the effectiveness of artificial intelligence (AI) systems hinges on the data it leverages. The accuracy of AI-based applications is directly influenced by the quality, relevance, and diversity of their underlying datasets. 

Think of building an AI model like constructing a rocket for space exploration. The model is your rocket—packed with complex engineering and state-of-the-art technology, from sophisticated algorithms to the computing power needed to run them. But just like a rocket can’t launch without fuel, your AI model won’t perform without data—and not just any data. It needs clean, high-quality data to effectively power those algorithms and lift off. 

Data is the fuel for AI. If your data is clean, accurate, and relevant, your AI can soar to incredible heights, making accurate predictions and delivering powerful insights. But if the fuel (data) is contaminated with errors, inconsistencies, or irrelevant information, your AI might sputter, go off course, or even crash before it leaves the ground. 

Data engineers frequently cite separate data silos, data access, data governance and security, and inconsistent data quality as primary obstacles in Generative AI (GenAI) initiatives.  

As unstructured data takes center stage for GenAI, data engineers must have full visibility into their entire data estate and access to the necessary digital fuel for model training, fine-tuning, as well as retrieval augmented generation (RAG). While structured databases have long benefited from data catalogs that provide a clear, searchable inventory of the database tables, columns, data types, and relationships; exploring unstructured data remains relatively uncharted territory. Unstructured data, therefore, needs to be transformed to a format where features can be extracted from. Otherwise, when using a model that has not been trained on your business or industry-specific content, it may give inaccurate responses and undermine your AI initiative. 

But data preparation for GenAI is more than just accessing data; it's also about ensuring data recency. Organizational data is rarely timeless; it represents a snapshot in time that can quickly become outdated, leading to information losing its relevance and context. For example, when your organization launches a new product, introduces features or capabilities, integrates products into new solution sets, or retires offerings that no longer meet market demands, it’s essential your data repositories reflect these changes. 

To enrich GenAI applications, you must break down data preparation into four key components: data catalog, real-time information streaming, data governance and classification, and security. Each of these components play a vital role in providing the high-quality fuel needed for your AI data pipelines. 

The four parts for improving GenAI results 

1. Data Catalog. By extracting metadata from all your data estate and creating a global metadata namespace, you can streamline data access and preparation. A global metadata namespace acts as a centralized, searchable interface that organizes datasets using metadata and user-generated tags. This allows data engineers to quickly discover relevant data without sifting through siloed datasets. The result: focusing less on data wrangling and more on meaningful analysis and model building.
   

   However, metadata abstraction alone isn’t enough to completely mitigate the root causes of data silos. To fully realize the benefits of a global metadata namespace, organizations must establish a unified storage operating system (OS) as its foundation. A unified storage OS eliminates the need for maintaining multiple storage systems with unique requirements for administration, maintenance, patching, and upgrades. Without this foundational layer, abstracting metadata merely creates a veneer over existing silos, leaving their underlying inefficiencies intact. 

2. Data governance. Adhering to data privacy laws like GDPR and CCPA is non-negotiable for AI systems. But massive datasets used to fine-tune AI models and enrich RAG databases may include sensitive information, such as personally identifiable details, confidential medical records, and financial data. Even seemingly benign information—like customer purchasing behaviors or plans for upcoming products—can be harmful if exposed to competitors. For data engineers, managing these datasets involves consolidating, categorizing, evaluating, and sharing data responsibly while preventing unauthorized access and complying with regulatory requirements.
   

   For example, if you’re building a customer-facing chatbot for your company, there is no need to incorporate data from human resources, finance, or corporate strategy; but with lack of data governance, it’s hard to ensure that doesn’t happen. Even small imperfections in the dataset can lead to substantial prediction errors, compliance challenges, and regulatory issues. 

3. Real-time data streaming. Real-time streaming addresses a crucial challenge for AI: data staleness. About two-thirds (63%) of AI projects fail because they use outdated and expired data sets in AI modeling. Models that use outdated data risk generating inaccurate outputs, which is commonly referred to as hallucinations. Such inaccuracies undermine user confidence and can lead to costly errors or poor decision-making.
   

   Data recency with real-time data streaming ensures a continuous flow of up-to-date information, enabling AI models—including those leveraging RAG—to process and analyze data instantaneously. By doing so, these systems can make precise, data-driven decisions and respond dynamically to evolving conditions. 

4. Security. All of this is anchored by data security. Carrying over the security posture that includes access control lists (ACLs) and user permissions is a fundamental requirement. But a comprehensive data security plan goes beyond user access.
   

   You need a ransomware protection solution that offers cutting-edge capabilities to safeguard your data against the ever-evolving threat of ransomware. Your AI data repositories need to proactively detect anomalies in data access patterns and user behavior, flagging potential ransomware attacks in real-time and taking protective measures to ensure data integrity.

There are ways to improve infrastructure performance and AI accuracy without adding more GPUs and storage infrastructure. Using smaller data sets with more relevant and high-quality data is the answer. By leveraging a data catalog, data governance, and real-time data streaming in an AI data platform, you can enhance performance, safeguard sensitive data, ensure ethical use, and maintain regulatory compliance. AI data preparation strategies for unstructured data ensure that the data is refined, providing only the context needed for the specific use case, this significantly reduces the resources required for fine-tuning models or creating vector embeddings for RAG. 

Implementing a comprehensive strategy for data preparation enables you to unlock the full potential of GenAI. 

Data catalog, data governance, real-time information streaming, and security are not simply tools, but a strategic enabler for AI success. By improving data discovery, quality, classification, and collaboration, you establish a solid foundation for AI models to deliver precise, reliable, and actionable outcomes.  

Ready to take your AI data management to the next level? 

Hear more about next-generation data management for AI. Also, watch the discussion of this topic in the AI Intelligence video series (coming in February). 

If you missed out on our webinar where we talked through the survey results of IDC’s AI maturity model white paper, you can watch it on demand. 

To explore further, visit the NetApp AI solutions page.