Data Platforms Must Adapt To The Rise Of Conversational AI And LLMs
Jan 23, 2025 - forbes.com
The article discusses the impact of large language models (LLMs) on business workflows and the challenges they pose to data platforms. As LLMs handle conversational data, traditional data platforms, which were designed for structured data, must evolve to manage unstructured and complex data formats. Key challenges include handling unstructured data, scalability pressures, detecting and protecting sensitive information, ensuring data lineage, and navigating regulatory compliance. To address these issues, data platforms need to support unstructured data, adopt scalable infrastructure, integrate sensitive data detection and encryption, enhance data lineage and observability, and implement robust governance and access controls.
The article emphasizes that while LLMs present challenges, they also offer significant opportunities for businesses to extract value from conversational data. By modernizing data platforms, companies can mitigate risks and leverage LLMs for advanced analytics, improving customer experiences, operational efficiency, and unlocking new revenue streams. Organizations that successfully adapt to these changes will balance the power of LLMs with data integrity, privacy, and quality, positioning themselves as leaders in the AI-driven era.
Key takeaways:
```html
Data platforms must evolve to handle unstructured conversational data, enhancing storage, processing, and governance capabilities.
Scalability is crucial to manage the volume and richness of conversational data, requiring scalable storage and compute solutions.
Sensitive data detection and protection are essential to prevent regulatory non-compliance and reputational damage.
Modern data observability and governance frameworks are necessary to ensure data lineage, compliance, and secure access controls.
