In this era of data democratization, organizations are aggressively pursuing data self-service initiatives to empower users across departments with seamless access to data. However, many are not factoring in the needs of a new digital stakeholder: generative artificial intelligence (GenAI) foundational models or large language models (LLMs). These models, pivotal for innovative AI applications, have similar data-access needs as people; that is, they need the data to be organized, searchable, and in a language they can “understand.” So, to foster inclusive data democratization, it’s imperative to consider their requirements.
Understanding the Landscape: GenAI and Retrieval Augmented Generation (RAG)
Retrieval augmented generation (RAG) revolutionizes the integration of enterprise data with LLMs without the need to directly train the models on specific datasets. RAG also mitigates the likelihood of AI hallucinations, while enhancing response quality, by leveraging a two-step process of retrieval and generation. When implemented correctly, RAG makes LLMs draw upon relevant, contextually appropriate information from diverse data sources, which reduces the risk of AI hallucinations. Additionally, incorporating timely enterprise data through RAG enriches the LLM’s understanding of the subject matter, leading to more accurate, insightful responses. Ultimately, RAG has the potential to elevate the overall quality of AI-generated content by grounding it in enterprise context. However, the journey towards adopting GenAI is fraught with challenges, particularly when scaling RAG to meet diverse enterprise needs.
Challenges in Adopting GenAI for Enterprises
Let’s imagine a customer service chatbot tries to provide accurate responses by drawing data from multiple systems like ERP, CRM, and support ticketing systems. Here in lie the challenges:
- Data Distribution: Relevant data is fragmented across disparate systems, and stored in varied formats, necessitating a variety of different access methods.
- Data Accuracy and Currency: Organizations need accurate, relevant data so LLMs can provide timely, precise responses.
- Security and Explainability: Organizations need to provide data integrity and transparency in AI decision-making processes.
- Risk Management: Organizations need to address risks associated with GenAI and LLMs, such as bias and privacy concerns.
Addressing the Challenge: The Role of Logical Data Fabric
A logical data fabric emerges as a solution to streamline enterprise data usage for GenAI projects. Enter the Denodo Platform, a leading logical data management solution, used by many organizations to build a logical data fabric that can greatly enhance the capabilities of a data ecosystem. Here’s how the Denodo Platform tackles the aforementioned challenges:
- Unified Data Access: The Denodo Platform provides a centralized data access point, connecting to disparate data sources in real-time to establish a unified semantic layer that delivers data with the context needed for easy understanding by both traditional users and AI models.
- Simplified Data Discovery: The Denodo Data Catalog simplifies data exploration and discovery by providing a rich set of metadata describing and categorizing the data.
- Policy-Based Access Control: The Denodo Platform enables granular access control policies, for streamlined data security and compliance with regulations. This feature safeguards sensitive information while facilitating data access for AI models.
- Adaptability: By decoupling data access from underlying sources, the Denodo Platform future-proofs the data landscape, enabling organizations to seamlessly adapt to evolving business needs and emerging technologies.
- Improved Observability: The Denodo Platform provides transparency and accountability with comprehensive data lineage and monitoring capabilities, crucial for regulatory compliance and a better understanding of AI algorithms’ behavior.
The Denodo Platform supports GenAI applications by providing a reliable data foundation and facilitating contextual data retrieval through its semantic layer. Explore Denodo’s whitepaper “The New Era of Gen AI: Enabled by Logical Data Management” to better understand the significance of contextual data for GenAI effectiveness, learn about RAG mechanics, and discover how a data fabric approach accelerates GenAI initiatives. Download the whitepaper today, for a transformative journey towards data-driven innovation.
