Private Data: The Key to Unlocking AI’s True Potential

By Mark A. Johnston, VP Global Innovation & Strategy

In the era of AI, private data is emerging as one of the most valuable assets. Unlike publicly available data, private datasets provide unique opportunities to fine-tune large language models (LLMs) and produce results that competitors can’t match.

The value is clear—customized outputs, better personalization, and competitive advantage. But it comes with challenges.

Why Private Data Matters

Public data sources are widespread, but their use is limited. The more organizations adopt AI models, the harder it becomes to stand out. Private data gives you the opportunity to create a proprietary model, built on information no one else has.

This brings two main benefits:

• Unique Outputs: You can create responses and insights that competitors don’t have access to.
• Evaluation Accuracy: Private data can serve as a "holdout" dataset to test how well your LLM performs on unseen information. This helps you evaluate whether your model is fine-tuned properly.

But accessing and using private data requires strategy.

The Problem of Access

Many companies struggle to access internal private data efficiently. Personalized AI solutions require seamless data integration, but some teams face internal roadblocks. Instead of direct access to data, they have to navigate slow, manual processes, such as submitting queries or requests for data retrieval. This manual approach creates bottlenecks, preventing personalization at scale.

The Case for Automated API Generation

What they need is an?automated API generator, like those already available today. Tools such as DreamFactory, Hasura, and PostgREST can pull data directly from internal systems, learn the schema, and update it when the data changes. These solutions streamline access to private data, eliminate manual delays, and enable teams to focus on more valuable tasks. Implementing such tools would provide immediate, scalable access to data, enhancing the ability to personalize AI-driven models efficiently.

How LLMs Can Use Private Data

LLMs can use private data in multiple ways to deliver significant value:

• Fine-Tuning for Specific Use Cases: Pre-trained LLMs are broad in scope, but private data allows for fine-tuning that adapts the model to specific industries or tasks. This results in more accurate and specialized outputs.
• Generating Unique Insights: LLMs trained with private data can produce outputs unique to your business, making your model’s responses distinct and valuable.
• Improving Personalization: Private data enables LLMs to offer tailored recommendations, improving customer experience through more relevant interactions.
• Serving as a Holdout Dataset for Testing: Private data can be used to test LLMs on unseen datasets, ensuring that the model performs well in real-world scenarios.
• Enhancing Decision-Making: By analyzing private data, LLMs can generate data-driven insights to help with strategic decision-making.
• Custom Output for Proprietary Solutions: Private data ensures that LLMs generate outputs that are exclusive to your organization's needs, providing a competitive edge.

Using private data allows LLMs to move beyond generic functions and deliver customized outputs that align directly with your organization’s goals.

AI-Driven Personalization

Personalization has proven its worth, but AI-powered personalization offers a new level of potential. Fine-tuning LLMs with private data allows for highly tailored insights, often significantly more impactful than what general models provide.

Why? The data becomes more relevant. When the model has access to internal data, it produces custom outputs tailored to your needs. The more personal the data, the more valuable the insights.

Limitations of Using Private Data with LLMs

While LLMs can benefit greatly from private data, there are several limitations that need to be considered:

• Data Privacy and Security Concerns: Handling sensitive data raises privacy issues, especially in regulated industries like healthcare and finance. Without proper security, organizations risk breaches and non-compliance with laws such as GDPR or HIPAA.
• Data Quality and Consistency: Private data often varies in quality. Inconsistent or incomplete data can lead to unreliable outputs, so data must be cleaned and structured before use.
• Limited Data Availability: Smaller datasets can lead to overfitting or inadequate performance. LLMs require a substantial amount of data for fine-tuning, and limited data can hinder their effectiveness.
• Computational Costs: Fine-tuning LLMs with private data can be resource-intensive and costly, particularly for organizations without access to robust cloud infrastructure.
• Data Silos and Integration Issues: Private data may be fragmented across systems, making integration difficult. Breaking down these silos is essential but can require significant time and resources.
• Model Interpretability and Explainability: LLMs can be difficult to interpret. This lack of transparency can pose challenges when using private data, especially in fields where explainability is crucial.
• Regulatory and Compliance Issues: Different regions have varying regulations around data usage, and navigating these laws can be complex when using private data in AI.
• Ethical Concerns: Using private data can raise ethical questions, particularly around consent, bias, and fairness. Failing to address these concerns can lead to biased or discriminatory outputs.

These challenges must be carefully managed to ensure that the benefits of using private data outweigh the risks.

How to Get the Most Out of Private Data

Here’s a practical approach to leverage private data for AI:

• Set Clear Goals: Identify whether your private data will serve for?model evaluation?or to?generate custom insights. Knowing this will shape how you integrate and use the data.
• Invest in Data Integration: Work with your database teams to create automated, sustainable processes. Explore tools that simplify the API creation process for internal databases.
• Focus on Custom Output: Fine-tune your models on data specific to your business or industry. The more tailored your data, the more unique the AI-generated outputs become.

Ask Yourself

Are you using private data to improve your AI’s performance? Is it for testing, fine-tuning, or creating unique, valuable insights?

The answers will define how you use AI and whether your models can offer something no one else has.

Private data is your asset. How you use it will determine how far you can take your AI.

Is your private data being fully utilized to create valuable AI insights, and do you have the right processes in place to ensure it’s secure and scalable? Reach out if I can help you: [email protected]