Due to the abundance of sensitive data that enterprises need to manage, the predominant trend is the utilization of in-house Large Language Models (LLMs), rather than relying on public tools like ChatGPT. Imagine a scenario where a shipping company acquires a foundational model from entities like OpenAI and subsequently trains it using proprietary data. However, their aspirations extend beyond this.
According to Jennifer Belissent, Snowflake’s Principal Data Strategist, companies not only feed their internal LLMs with proprietary data but also seek to acquire external data sets tailored to their specific industry or market. Business leaders, she notes, require a broader perspective than what their internal data alone can offer.
Belissent emphasizes the importance of understanding regional trends, industry benchmarks, and contextual factors for businesses to uncover opportunities beyond their internal insights. This necessity positions businesses not merely as consumers but as potential contributors to the data market.
As an example, she cites ADP, a payroll software maker, whose ADP Insights data product analyzes pay growth across the United States based on criteria such as region, industry, gender, and more. The growing demand for data presents an opportunity for businesses to not only meet their internal needs but also to capitalize on selling their data to a market with a substantial appetite for information.