Large language models (LLMs) are no longer just a technological innovation, but a key tool in the way companies query and analyze data. From facilitating information extraction to accelerating decision making, these models are transforming the day-to-day work of data teams. 

A recent report by McKinsey points out that integrating AI into data flows can reduce the time spent on generating insights by up to 40%, improving operational efficiency by 30%. However, along with these opportunities come challenges, especially in terms of data privacy, security and reliability. 

From manual consultation to conversational interpreting 

Until recently, interacting with databases required technical expertise. SQL, BI tools and structured queries were the norm. Now, thanks to GPT-4, Claude or Gemini, users can ask questions in natural language and get answers in seconds, without the need for technical intermediaries. 

For technology leaders, this is a revolution: entire departments can harness the potential of data without relying exclusively on specialized teams. In sectors such as banking and insurance, LLMs are already automating audits and detecting fraud in real time. In retail and e-commerce, they can analyze shopping trends and personalize recommendations with unprecedented accuracy. 

The challenge of biases, privacy and "hallucinations" 

But it's not all that simple. Despite their potential, these models present significant challenges. Privacy and security are two of the main concerns, as many LLMs require access to sensitive data. Regulations such as GDPR in Europe or CCPA in California are forcing companies to develop more secure strategies, such as federated learning or differential encryption .

There is also the problem of reliability. LLMs are not infallible and sometimes generate incorrect answers or misinterpret data, known as "hallucinations". In critical sectors such as healthcare or finance, this can have serious consequences, so human validation remains essential. 

An MIT study reveals that 72% of data leaders consider that the lack of transparency in AI models is an obstacle to their adoption in strategic decisions. The key is to develop more auditable tools, with traceability and explainability in their processes. 

Generative AI and the future of data analytics 

As LLMs integrate with advanced platforms, the boundary between traditional analytics and automated insight generation becomes increasingly blurred. The most innovative companies are already experimenting with AI-based data wizards, capable of interpreting real-time information and providing strategic recommendations. 

Events such as Big Data & AI World 2025 will play a key role in this evolution, bringing together industry leaders to discuss the impact of generative AI on business intelligence. The big question is no longer whether these models will change data analytics, but how to adapt them to gain competitive advantage without compromising security and ethics. 

LLMs have changed the way we interact with data, but their true impact will depend on how they are implemented. Combining their potential with good governance and validation practices will be the key to leveraging them without risk.