The critical role of data integrity in generative AI

According to government research, around one in six UK organisations are currently implementing at least one application of artificial intelligence (AI), with usage expected to increase at a rapid pace. In fact, it’s predicted that the AI market will add approximately 800 million pounds to the UK economy by 2035.

These findings suggest that, more than ever before, business leaders need to assess the data flowing throughout their organisation, as this will directly impact the success of AI tools. When doing so, it is important to understand the crucial role of data integrity – and by extension, data enrichment – in powering these innovative systems, and how real-world applications can help make AI actionable.

One form of AI revolutionising the way in which business leaders source information, create new products, develop new content, and respond in real-time to emerging events, is generative AI. Generative AI drives transformation, focusing on machines’ ability to generate meaningful and novel creations. Fuelled by powerful machine learning (ML) models known as foundation models (FMs), generative AI taps into vast datasets and patterns to create outputs that mimic or easily retrieve data in a conversational way.

The implications of generative AI are vast, driving organisations of all sizes to explore and leverage FMs to transform their businesses and elevate the value they deliver to customers. The quest to harness the full potential of generative AI relies on finding high-performing FMs and trustworthy data to achieve outstanding results for diverse use cases. With the continued growth and transformative impact of generative AI, business leaders need to ensure that the data being fed into it has integrity. 

Data integrity and AI

Trusting data is the cornerstone of successful AI and ML initiatives, and data integrity is the key that unlocks its full potential. Data integrity means having accurate, consistent, and contextually relevant data – the kind of data business leaders can confidently rely on to make informed decisions and drive their organisation forward.

Yet, achieving data integrity is a complex task, and many organisations need help with data challenges that stand in the way. Data often resides in isolated silos, grows stale over time, lacks standardisation, may be riddled with duplicates, and does not leverage third-party data and spatial insights to add context, diminishing its integrity and reliability. Without data integrity, organisations risk compromising their AI and ML initiatives due to unreliable insights that do not fuel business value, but with it, the rewards are immense. A robust data integrity strategy helps companies to achieve and maintain trusted data, fuelling more dependable AI results, and allowing confident data-driven decisions to be made to help grow the business, stay agile, reduce costs, and manage risk and compliance.

Data enrichment is an essential part of the data integrity journey. Enhancing data with additional information, such as points of interest, property attributes, demographics, and risk information, increases the context and relevance of AI models’ outputs. This enrichment process involves various techniques, such as pre-processing, cleaning, and incorporating contextual embeddings.

Fine-tuning large language models on trusted third-party enrichment datasets allows them to learn from domain-specific patterns, making their outputs more accurate and relevant. Human review further ensures the accuracy and relevance of the dataset, addressing potential biases or errors in the training data and preventing misinformation, ethical concerns, security risks, and other negative implications.

Ultimately, when organisations thoughtfully leverage data enrichment as part of their overall data integrity strategy, they unlock the full potential of AI models, driving transformative solutions across your various domains.

Enhancing customer interactions with generative AI

In today’s digital world, the pressure is on to stay competitive, and that means providing the highest levels of customer experience possible. Companies can adapt FMs to generate fast responses to customer queries based on the latest information from their enterprise knowledge repository – resulting in highly accurate chatbots that can provide the right answers to customers quickly and seamlessly. 

However, large language models (LLMs) have certain limitations. Trained on general domain corpora, they might not be as effective on domain-specific tasks. To be truly accurate, a chatbot needs precise answers based on specific data rather than generic information. This is where retrieval augmented generation (RAG) and fine-tuning techniques come into play. RAG is a game-changer that combines the power of LLMs with external knowledge, while fine-tuning adapts the model to specific datasets, enhancing its performance with domain-specific nuances. By retrieving contextual documents from outside the language model and incorporating them during execution, RAG, complemented by fine-tuning, enhances the model’s performance.

PropTech companies, for example, can leverage LLMs with RAG and fine-tuning to access even richer and more robust information about a property. By simply asking a chatbot a question, they can receive precise and up-to-date responses about property details, neighbourhood safety, and demographics. The integration of RAG and fine-tuning streamlines the process, resulting in the ability to serve customers faster, research property information more efficiently, and ultimately increase sales and profit.

This is another instance where data enrichment can be harnessed to enhance the value of data used for generative AI models – and ultimately help produce greater context and relevance in the models’ outputs. Enriching data with additional attributes and variables, like points of interest and demographics, ensures accurate, contextually grounded responses to customer inquiries.

Together, data enrichment, RAG, and fine-tuning form a powerful trio that unleashes the full potential of generative AI – and they are also crucial pillars in generative AI’s evolution. As the PropTech industry, and others, continue to leverage ML solutions, integrating these powerful techniques will revolutionize customer interactions, streamline research processes, and ultimately boost business outcomes. 

Generative AI marks a significant paradigm shift

Generative AI enables machines to produce novel and contextually relevant content. With advancements in ML and the development of powerful generative AI models, there is a tremendous transformation in AI capabilities. However, the true power of generative AI can only be fully realised with data integrity. 

Trusting the data that feeds these models is crucial for delivering reliable, accurate results that empower organisations to make well-informed, data-driven decisions. Achieving data integrity is no easy feat, but with advancements in techniques, vast and diverse data availability, and cloud computing capabilities. 

As generative AI continues to evolve, the role of data integrity, including powerful data enrichment capabilities, and RAG and fine-tuning models will play a pivotal role in unlocking the true potential of AI models across various industries. When organisations embrace these powerful techniques, they elevate their AI initiatives to new heights, delivering trustworthy and dependable results that propel their business toward success.

Anjan Kundavaram

Anjan Kundavaram is the Chief Product Officer at Precisely. As Chief Product Officer, Anjan is responsible for driving the roadmap and delivery of Precisely’s newest launched Data Integrity Suite and for product, pre-sales and post-sales functions.

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