The concentration on generative AI puts data quality into sharp focus. Grounding LLMs with trusted private data and knowledge is more essential than ever.
Focus on AI data quality
With generative AI applications taking center stage, there's a heightened focus on AI data quality.
Yet ensuring AI data quality in terms of completeness, compliance, and context is fraught with challenges. Our recent survey of 300 enterprises revealed that data quality is one of the top concerns for companies building AI apps. This isn't surprising.
Why? AI teams recognize the critical role data plays in building trust for generative AI within businesses. Leveraging Retrieval-Augmented Generation (RAG) frameworks to ground Large Language Models (LLMs) with reliable internal data and knowledge is critical.
AI data quality challenges
So, what makes AI data quality such a challenge? There are 3 main culprits:
1. Fragmented data: The nemesis of generative AI
Enterprise data is often siloed in dozens of systems. Customer data, for example, is typically fragmented across CRM, billing, customer service, interaction management, call recordings, and the list goes on. This fragmentation makes it incredibly difficult to serve a real-time and reliable customer view to the underlying LLMs powering customer-facing generative AI apps.
To overcome this challenge, you'd need a robust data infrastructure capable of real-time data integration and unification, master data management, data transformation, anonymization, and validation.
The more fragmented the data, the steeper the climb towards achieving AI data quality.
2. Data lost in translation: Due to low-quality metadata
Imagine a brilliant translator struggling with instructions in a cryptic language. That's essentially what happens when generative AI apps encounter data with sparse metadata. Metadata, the data that describes the data, acts as a crucial bridge between your organization's information and the LLM's ability to power your generative AI apps.
Rich metadata provides the context and understanding LLMs need to effectively utilize data for accurate and personalized responses. Unfortunately, many organizations face the challenge of maintaining stale data catalogs. The dynamic nature of today's data landscape makes it difficult to keep metadata current.
This lag results in a communication gap between the data and the LLMs, ultimately hindering the quality and effectiveness of your generative AI initiatives.
3. Data privacy vs insights: A balancing act with AI data quality
Data privacy regulations are necessary safeguards for sensitive information, but they can damage data quality. While anonymization and access controls are crucial for compliance, these measures can hinder the maintenance of the referential consistency of the data.
Referential consistency refers to the accuracy of relationships between different data points. When anonymization techniques, like static or dynamic masking, disrupt these relationships, the data quality suffers. Masked data is less reliable and meaningful for both users and LLMs.
Essentially, the very measures designed to protect data privacy can inadvertently undermine the quality of the data itself, and prevent generative AI from extracting valuable insights.
4. Data quality in isolation: Not great for cross-functional collaboration
Traditionally, data quality initiatives have often been lone efforts, disconnected from core business goals and strategic initiatives. Such isolation makes it difficult to quantify the impact of data quality improvements to secure the necessary investment. As a result, data quality struggles to gain the crucial attention it deserves.
Generative AI apps rely heavily on high-quality data to minimize AI hallucinations and generate accurate and reliable results. This dependence creates a compelling opportunity to showcase the tangible benefits of data quality and secure the necessary resources for continuous improvement.
Why data lakes are wrong for RAG
Traditional approaches leverage ETL/ETL and data governance tools to ingest multi-source enterprise data into centralized data lakes, which enforce the necessary data quality and privacy controls. RAG frameworks for grounding LLMs with enterprise structured data involve writing hundreds (or even thousands) of RAG AI functions that query the data lake to respond to all the anticipated user prompts.
Despite the advantages of data lakes in scalability, accessibility, and cost, there are also significant risks and limitations in using data lakes for generative AI, including:
-
Data protection
The risks of sensitive data leaking to the LLM or to unauthorized users are substantial. -
High cost
Cleansing and querying the data at high scale are compute intensive, escalating the associated data lake costs. -
Analytics focus
Data lakes are less appropriate for real-time conversational generative AI use cases that require fresh, clean, and compliant data.
Read more about the pros and cons of using data lakes in RAG for structured data.
A paradigm shift is needed to make data AI-ready
At K2View, we propose a paradigm shift for making data AI-ready: Micro-Databases™.
Imagine a data lake for one – a dedicated Micro-database for each customer, employee, or product. This "data lake of one" is continuously synced with the source systems, continuously cleansed according to a company’s data quality policies, and its data is continuously protected. Millions of these Micro-databases, instantly accessible by RAG queries, empower generative AI apps to personalize and ground their LLMs with the highest quality data.
But the solution for making data AI-ready extends beyond technology.
Organizations must prioritize data quality by establishing KPIs directly linked to generative AI success. Building multi-disciplinary generative AI teams that include data quality engineers fosters collaboration and ensures all aspects, from data preparation to application performance, are aligned.
Learn how the K2view suite of RAG tools, GenAI Data Fusion, is setting the
standard for AI data quality with complete, compliant, and contextual data.
