In the ever-evolving landscape of data management, businesses want solutions that make accessing and analyzing data easier for both technical and non-technical users. One such proposal is text-to-SQL, the idea of which is to bridge the gap between natural language and structured databases. The idea is that users can ask questions in everyday language (for example, "How were sales last quarter?") and get back SQL-generated answers.
However, as enterprises scale and their data becomes more complex, the limitations of text-to-SQL are becoming increasingly obvious. That’s where knowledge graphs step in, offering a more powerful and flexible approach to data management. On Tuesday, September 24, 2024, Rob Giardina of Claritype and I will be giving a webinar about this (and related topics) but I wanted to take a minute to talk about some of our motivations. (You can register for the full webinar or, if it's already passed, get the on-demand version, here.)
The Promise and Limitations of Text-to-SQL
Text-to-SQL technology was designed to simplify data querying by enabling users to ask questions without needing to write SQL code. For example, users could ask, "What were last month’s top sales regions?" and the system would generate an SQL query that retrieves that information from the database. In cases where the data is simple and the queries are straightforward, text-to-SQL can work well, making things easier and saving time.
However, as data environments become more complex, we start to see some of the shortcomings of text-to-SQL. First, it struggles with highly sophisticated or multi-step queries that require complex joins or nested logic, particularly when working across several datasets. In particular, the technology often fails when it has to deal with ambiguous column names, unusual data structures, or incomplete datasets. The result can be incorrect or incomplete results, which kills user confidence.
More importantly, though, text-to-SQL doesn’t understand the context of the data it’s querying. Data engineers build databases to store data in a way that optimizes speed and storage, and not necessarily in a way that reflects how the business understands its own processes. A user might ask, “Who is our most profitable customer?” but that information isn’t neatly stored in a single table. It requires pulling and interpreting data from multiple places--something that text-to-SQL can't handle without the business context that’s crucial for decision-making.
Enter Knowledge Graphs
Knowledge graphs can provide a solution to many of these challenges. Unlike traditional databases, knowledge graphs represent data in a web of relationships, mimicking how we understand real-world connections. In a knowledge graph, entities like “customers,” “transactions,” or “products” are connected by their relationships, allowing for more intuitive and context-aware querying.
Where text-to-SQL falls short in interpreting complex data relationships, knowledge graphs excel. They offer a contextual layer on top of traditional datasets, enabling more sophisticated queries that reflect the business's operational reality. For example, in a knowledge graph, the concept of a “customer” is not just a row in a table, but an entity linked to various transactions, interactions, and behaviors. This network of connections makes it easier to ask and answer complex business questions such as, “Who are our top customers considering not just sales, but customer support interactions and engagement history?”
The Future of Data Querying – Beyond Text-to-SQL
While text-to-SQL tools will still have their place in simple environments or for basic queries going forward, enterprises with complex, large-scale data needs will need more powerful solutions. Knowledge graphs, in combination with other advanced technologies like large language models (LLMs), will ultimately become the foundation of how businesses interact with their data.
As LLMs advance, the ability to ask even more sophisticated, human-like questions will continue to grow. But without knowledge graphs or similar structures, LLMs alone will struggle to provide accurate answers across complex, multi-system environments. Combining the two technologies, with LLMs handling the natural language aspects while knowledge graphs provide the structured data context, can provide precise and reliable answers to even the most complicated queries.
Conclusion
In the world of enterprise data management, text-to-SQL technology, while helpful, is simply not enough for today’s complex data environments. Its inability to handle contextual understanding and complex queries limits its effectiveness in large-scale enterprises. Knowledge graphs offer a superior alternative, allowing businesses to create interconnected data models that not only reflect the complexity of their operations but also facilitate more insightful, business-oriented querying.
As companies continue to grow and their data landscapes evolve, the combination of knowledge graphs and advanced AI technologies will be crucial for unlocking the full potential of their data. Businesses that invest in these technologies now will be better positioned to make faster, smarter, and more accurate decisions in the future.
Want to know more about how you can use AI to transform your data into knowledge graphs, or what to do with it once you do? Join Rob and I next Tuesday!
