Adapting Data Governance to the age of LLM's

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Dear Reader…

As enterprises adopt Generative AI and in particular deploy Large Language Models (LLMs), the purview of data governance is undergoing a significant transformation. The integration of these technologies into business operations opens up new forms of value, as well as a different set of risks. The vast amounts of data involved requires an evolution in your approach to management and governance. This week, we will explore the key implications for enterprises.

Data Governance as a Critical Building Block

Governance is no longer just about compliance and risk management; it has evolved to enable analytics, AI, and business value through trusted data. With GenAI, the focus shifts towards ensuring that data is accurate, trusted, and accessible in a self-service model. This requires a more agile and flexible approach, tailored to the needs of specific business use cases and models such as Retrieval Augmented Generation or RAG as discussed last week. Using RAG requires real-time data governance, dynamically applying policies to relevant data in LLM workflows. This ensures that data interactions are secure, private, and compliant with regulatory requirements.

Unstructured Data Management

LLMs rely heavily on unstructured data such as documents, images, and videos, which are often stored in siloed systems and lack proper cataloging and context. Implementing appropriate management systems is crucial to manage and utilise this data safely, ensuring data quality, consistency, and security. Using a hierarchical and tag-based categorisation approach to group data based on its type, source, or purpose makes data retrieval more efficient and maintains consistent classifications - reducing the likelihood of hallucinations.

Traceability & Transparency

Because LLM’s deal with data from multiple channels, it can be challenging to track the data lifecycle. It is essential to have a data lineage and traceability process in place, once again to reduce inaccuracies in answers, but also to figure out where the model has gone wrong. Tagging and segmenting data based on its sensitivity, importance, and relevance to the organisation becomes essential. Examples include tagging data with metadata that indicates details such as owners, purpose, security, and compliance requirements.

Bias and Ethics

LLMs can introduce biases if trained on segregated or non-representative data. Particular attention needs to be paid to discovering where hidden bias may exist in particular data sets. As we saw with the Shirley Card a few weeks ago - improperly calibrated skin tone can lead to misclassification of people as primates! Policies for data quality, fairness, and responsible use become even more significant as neural networks infer answers from data sets.

Semantic Layers for Enhanced Governance

A semantic layer can bridge the gap between business logic and data language, refining responses generated by LLMs and ensuring accuracy and relevance. This layer provides context and specificity, reducing the chances of dangerous hallucinations and speeding up AI development cycles. Likewise segregating sensitive data with appropriate audit trails provides an additional layer of security.

Investing in AI-Ready Infrastructure

Running GenAI applications requires high-performance computing capacity, efficient storage, and appropriate security procedures. Investment in AI-ready infrastructure to support enterprise demands is key from a long term planning and budgeting standpoint.

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Finally…

The integration of LLMs and GenAI into enterprises necessitates a robust data governance framework that addresses the unique challenges of these systems. By aligning practices on unstructured data management, data lifecycle traceability, bias and ethics, real-time governance, semantic layers, and AI-ready infrastructure, organisations can adapt to the demands GenAI. This not only mitigates risks but also unlocks the full potential of AI to drive new forms of business value and deliver digital business innovation.

Tips for Deploying a LLM: Practical experiences…

Next up we talked with Max Theseira an Enterprise Digital Transformation Consultant and Adjunct Professor who has been actively investigating how to use LLM’s for Enterprise business applications. In his interview which you can watch below he provides advice on how to adapt to Gen AI as it permeates the professional workplace.

Checkout the full interview @thedataradioshow 

Here are Max’s top tips:

Tip #1: Data Quality is key

Understanding the importance of Data Quality was one of the firsthand experiences in the complexities of working with Large Language Models (LLMs). The old saying of “Garbage in = Garbage out” applies to the data inputs, including maintaining and updating vector databases, and the challenges involved in keeping the LLM accurate and relevant. This process highlighted the technical intricacies that are often hidden when using pre-built models that we have ready access to in the cloud.

Tip #2: Understand your Model Capabilities

He discovered that not all LLMs are created equal. Understanding how to differentiate between models like LLaMA, which excel in certain tasks, and others that are better suited specific types of applications. Understanding this allows you to optimise the use of models for specific purposes, such as creative tasks versus more structured, programmatic functions. The adage that not all models are created equal is true. Each model depending on the intended application performs better, faster and more sustainably. These different considerations are key.

Tip #3: There is a gap between Theory and Application

As any architect or modeller knows there is often a big gap between concept and realisation. By focused on a real-world use case, you stand a better chance of learning how to integrate LLMs into business processes, such as automating document understanding or improving decision-making through AI-driven insights. Try experimenting with a real scenario to figure out where the gaps lie.

Tip #4: Prompting is the next Programming Language

Despite not being a programmer, Max successfully managed to deploy and train a LLM. He learned that you don’t need to be a coding expert to build and use LLMs effectively. This underscores the need to become familiar with different AI tools as it will enable professionals explore new ways to add value as businesses evolve into Digital Enterprises.

Tip #5: AI Literacy is scarce today

AI literacy is key at all levels of an organisation. AI is not just as a technical tool but a transformative force that requires thoughtful integration into business strategies and operations.

So what is the value for a Data Engineer in Deploying Your Own LLM?

For you deploying your own LLM provides a deeper understanding of the underlying mechanics of AI models. This includes managing data pipelines, optimising model performance, and troubleshooting issues that arise during training and deployment.

Equally the process of creating and fine-tuning an LLM requires critical thinking and problem-solving skills. Data engineers can develop their ability to address challenges like data quality, model drift, and performance bottlenecks, which are crucial for maintaining the effectiveness of AI systems in production environments. Tinkering with deploying a LLM for a specific use case helps you understand where different models and architectures might be applicable where pre-built models might not perform as well.

Likewise having hands-on experience with LLMs opens up new career opportunities for data engineers. As AI becomes increasingly integrated into various industries, the ability to build and manage sophisticated models is a highly sought after capability. This expertise positions data engineers as key players in driving AI innovation across industries and enterprises.

Lastly, this gives you the opportunity to engage in more strategic roles influencing the direction of AI initiatives, advise on best practices, and lead projects that integrate AI into core business functions. This opens up new career possibilities where you move from execution roles to becoming a strategic leader.

Thank you

That’s a wrap for this week.