How to Kick Start LLMsWith Data Products

Data fuels the engines of artificial intelligence, providing the raw material for machine learning and large language models (LLMs) to learn, predict, and evolve. However, significant challenges accompany harnessing the power of data, including ensuring data quality and privacy and overcoming integration hurdles. These challenges underscore the necessity for innovative approaches in creating data products that not only address these concerns but also unlock new opportunities for leveraging AI.

ChatGPT was released over a year ago, and in recent months, the market has seen a surge in the launch of hundreds of large language models (LLMs). According to estimates from the McKinsey Global Institute, generative AI could boost the economic impact of AI by 15 to 40%, heralding a substantial leap in productivity and innovation.

However, the question remains: why haven’t we observed a significant productivity increase in the industry? Despite having advanced technology available, why do so few enterprises implement AI use cases?

Simply put, a data product can be thought of as treating data as a product – similar to how we think about the process of building products today. A data product should be discoverable, accessible, understandable, trustworthy, secure, interoperable, and inherently valuable. It includes not just the data itself but also the accompanying metadata, documentation, data contracts, quality assessments, lineage, and access management.

Most enterprises cannot directly use general large language models (LLMs) available on the market because these models are mainly trained on publicly accessible data. This training approach often fails to encapsulate the specific enterprise knowledge and information necessary for informed business decision-making or application development. As a result, enterprises need to consider training or fine-tuning their LLMs locally to meet specific requirements.

LLMs generally outperform traditional machine learning (ML) models because they are trained on vast amounts of data. Essentially, the quality and volume of data directly influence the effectiveness of the model: superior data yields better results. Training with insufficient or low-quality data is often unhelpful and might even result in outputs that are inferior to existing analytical tools.

If you’re considering training a bespoke LLM that is tailored to your specific business context and want to ensure your valuable data remains secure by training locally, it’s important to start by building robust data products first.

How do you begin building your data products? There are primarily two types of data products: source-aligned and consumer-aligned. Source-aligned data products serve as the foundational elements for constructing other data products and training your LLM models.

Data experts in IT/BI departments are mainly responsible for building source-aligned data products, while each business domain is also empowered to build its own target-aligned data products.

Data products can be developed in a decentralized or semi-decentralized manner while still adhering to a centralized data management policy across the entire organization. This approach democratizes data management. It involves not only creating high-quality data products but also managing their lifecycle effectively. This includes ensuring that each data product has clear ownership, a defined data contract, and can be sustainably maintained over time.

While data products are developed and contributed to decentrally, it is crucial to promote data sharing and ensure that these data products are easily accessible, discoverable, and usable by those who need them. The platform facilitating this sharing is often referred to as a data product marketplace. Such a marketplace should support the discoverability of data products, help users understand the available data and its quality, and provide a mechanism for users to request access.

With a data marketplace, one can easily find and leverage all the available data products inside the organization, regardless of where they are stored to train your LLM models.

Yet, the path to train and utilize AI is fraught with ethical and technical challenges. AI data management, security, and the potential for bias in AI models are significant concerns. Addressing these requires a commitment to AI ACT practices, a clear and well-defined AI governance framework, and a good common foundation. Embracing open standards, investing in data literacy, and fostering a culture of innovation are essential steps in overcoming these challenges.

AI is unstoppable; the key lies in utilizing it wisely, effectively, and securely. Constructing data products is the initial step towards harnessing AI’s potential within your enterprise. One Data stands ready to assist you in this journey, providing the tools and support needed to transform your data into powerful AI-driven innovations.

What are your thoughts on the future of AI-powered data products? How do you see them transforming industries or even daily life? Share your insights and experiences in the comments below. Feel free to reach out to me to discuss how we can contribute to building this exciting future together!