Making data accessible with everyday language

Published April 25, 2025 | 3 minute read
Written by Lane Goedhart

How Text-to-SQL is streamlining data access for non-technical teams

In most companies, data teams spend a disproportionate amount of time helping colleagues locate the data they need, rather than focusing on high-value analysis and strategic insights. This bottleneck not only frustrates data professionals but also delays decision-making for business units that rely on timely, accurate data to drive their initiatives. Text-to-SQL technology is primed to eliminate this workflow inefficiency by empowering non-technical teams to query databases directly, significantly reducing the dependency on data teams and speeding up the time to insights.

What is Text-to-SQL?

Text-to-SQL is a natural language processing (NLP) technique that allows users to query databases using plain language. Instead of needing to understand complex SQL syntax, business professionals can ask questions like, “What were our ad attributed sales the last month?” and the system automatically generates the corresponding SQL query to retrieve the relevant data. This capability allows non-technical users, such as marketing managers, sales leaders, and product teams, to directly engage with their data, simplifying the entire process of data access.

Reducing costs and lowering barriers

Traditional data access models often require companies to invest in training employees on SQL or to hire specialized analysts to manage and query data. Text-to-SQL eliminates the need for this technical expertise, drastically reducing training costs and resource allocation. By allowing employees to directly interact with databases, organizations can cut down on reliance on data teams, freeing them to focus on more strategic tasks. This leads to cost savings and allows businesses to scale data access efficiently without needing to expand their technical teams.

Unlocking insights across disconnected systems

In many organizations, data resides in various disconnected systems, making it difficult to get a holistic view of business performance. Text-to-SQL solves this challenge by enabling users to query across multiple platforms and databases from a single interface. Whether it’s CRM data, marketing campaign performance, or financial reporting, users can easily query and combine information from various systems. This ability to access and integrate data across silos gives teams the comprehensive insights they need to make informed decisions quickly and efficiently.

You don’t need to build Text-to-SQL systems — solutions are available

While developing a custom Text-to-SQL solution may seem like a complex and resource-heavy task, the good news is that organizations don’t have to build these systems from scratch. There are platforms available that provide these capabilities out of the box, such as AI-native workspaces that incorporate Text-to-SQL technology. These solutions can be easily integrated into your existing workflows, empowering your teams to start querying data in natural language without requiring technical expertise or significant upfront investment. Tools like ours (lemonado.io) are designed to help organizations unlock the power of their data with minimal overhead.

The future of data interaction

As AI and natural language processing continue to evolve, the capabilities of Text-to-SQL will only improve. We can expect more sophisticated models that handle increasingly complex queries, offering deeper and more accurate insights. Beyond historical data, future advancements will allow businesses to ask predictive questions, analyze trends, and gain actionable insights—all through simple, conversational queries. These advancements will make data even more accessible and actionable, helping businesses stay ahead in an increasingly data-driven world.

Contributors:

Lane Goedhart is a builder, avid chess player, and early-stage operator.
Founder Associate @ Lemonado