Agentic Data Science

Large organizations typically rely on centralized data science and analytics teams to support business departments with data-driven questions. While widespread, this model has two major limitations.

First, these central teams are resource-constrained. Business requests often exceed their capacity, creating bottlenecks and delays. Second, data teams often lack the domain-specific knowledge needed to generate optimal insights. This leads to extended back-and-forth communication and further delays.

Introducing Agentic Data Science

Agentic data science aims to empower business users — essentially anyone — to independently derive insights from data. While the concept of self-service analytics has been around for years, it has mostly remained aspirational. Why?

• Low data literacy: Many employees are not comfortable working with data — even basic descriptive analysis in Excel is beyond the skill set of many, making self-service adoption difficult.
• Data privacy concerns: Even for data-literate individuals, access to sensitive datasets is often restricted for compliance reasons. The more people access sensitive data, the greater the risk of breaches. But without access, self-service isn’t possible.

The rise of privacy-preserving synthetic data and Large Language Models (LLMs) removes these barriers. With synthetic data, users can safely explore realistic datasets without exposing sensitive information. LLMs act as intuitive interfaces, allowing users to ask complex questions in plain language — no technical expertise required.

The MOSTLY AI Data Intelligence Platform brings the vision of agentic data science to life by enabling secure and flexible access to data, whether using production or synthetic sources.

Users can work directly with production data to generate insights without waiting on centralized teams. For use cases involving sensitive information, the platform offers privacy-preserving synthetic data to ensure data protection and regulatory compliance.

Powered by an intuitive LLM-based Assistant, the platform allows anyone in the organization to ask questions in natural language, explore data independently, and uncover valuable insights without technical expertise.

This approach delivers on the promise of agentic data science by making data truly accessible, safe, and actionable for everyone. Or as we like to say: Data for Everyone.

Self-service analytics may sound like a dream come true for some, while for others it may raise concerns. Will this reduce the need for data scientists? Will business users be left alone to tackle their most critical questions? In reality, the opposite is true. Self-service analytics, as enabled by agentic data science, creates value for everyone across the organization and brings multiple advantages.

• Faster time to insights: Business teams can now find answers on their own, reducing their dependence on central data teams. At the same time, data specialists experience less backlog and can respond more quickly to the requests that do reach them.
• Higher-quality analytics through focused expertise: Data analysts and data scientists are still essential. The difference is that they can now focus their skills on solving complex challenges, while routine questions are handled directly by business users through self-service tools.
• More informed and confident decision-making: While most organizations aspire to make decisions based on data, time pressure and limited access often lead to gut-driven choices. Self-service analytics helps reverse this trend by putting insights into the hands of decision-makers when they need them, improving the overall quality of decisions.
• Improved data literacy throughout the organization: The more people engage with data, the more comfortable and capable they become. An LLM-powered assistant not only delivers answers but also explains concepts, guides exploration, and helps users build lasting confidence in working with data.

Agentic data science empowers individuals across the business to engage directly with data, unlocking the full potential of self-service analytics and driving true data democratization.