Every Way Natural Language is Better Than SQL

Since the dawn of time, humans have communicated through gestures, drawings, smoke, or speech. Along the way, Structured Query Language (SQL) made its way into human life so we could speak to databases. However, it’s time to revert back to our natural language and rethink how we talk to our data.

Natural language is simply the way humans speak to each other—no SQL required. It’s also the word for non-technical language. SQL is a technical coding language that requires a technical background. Obviously, not everyone knows SQL which means not everyone can talk to their data. This is one of the biggest problems with data science today. Non-technical teams need to either know how to perform SQL queries or access to a technical team that does know, adding a level of bureaucracy and inefficiency to asking your data a simple question.

Natural language is the answer to this. If we can talk to our data in human language—the same way we would talk to another person—everyone can get insights from their dataset. 

SQL is used for strong data analysis and to run algorithms for decision making. Natural language queries do the same thing. You can get strong data analysis and run algorithms for decision making—but without knowing how to code. By using natural language instead of SQL, machine learning instantly becomes available to everyone. 

Here is an example of a SQL query.

What we mean by using natural language instead of SQL in machine learning is adopting no-code tools that allow your non-technical teams to be independent from your technical teams. Businesses have been adopting no-code tools slowly for the past 20 years. Thirty-five percent of website are built on WordPress. You can now build complex web applications with no-code tools like Bubble and Airtable. Businesses can do a lot more with less nowadays. That same principle has yet to be applied to machine learning.

Here is an example of a NL query.

Using natural language, non-technical users can quickly analyze data and run prediction algorithms simply by asking questions in plain English. This completely changes how businesses use machine learning.

Traditionally, the data science process involves non-technical users needing insights going to their technical teams and getting results back in a few weeks. And if you don’t know SQL, queries can take a long time. With a tool catered to your natural language, you can quickly analyze data and by typing it in the way you would talk to a human, there’s less room for error. Performing natural language analytics is faster and more efficient than SQL, allowing you to ask an unlimited amount of questions in an unlimited amount of ways in a work day and getting insights instantly. This totally destroys the SQL workflow as we know it. 

Just like content blocks revolutionized the way we build sites and emails, adding a search bar to a dataset provides the opportunity for non-technical users to be creative without code. Creativity in machine learning is a much needed skill for the future of work. Meaning, users need to know the possibilities on ML and how to get different kinds of value from it in creative ways. Any ounce of curiosity you have can be translated into your natural language on the search bar allowing you to to experiment and try new questions and commands out fast.

No-code tools have changed the way we look at outputs. Take this Parabola flow this Twitter user has built for example. With this kind of visualization, insights and automations can be quickly looked at and translated across many teams because it’s not in code. 

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With a natural language powered ML tool, you can visualize your natural language queries in graphs and plain text tables to see how they relate, compared to translating SQL to natural language after completing a SQL query. 

One big thing we’ve touched on is natural language democratizes machine learning and puts the power of it in the hands of everyone in an organization. This promotes cross-team collaboration and proper data governance among companies which avoids possible algorithm bias.

Natural language also allows teams to be “data independent,” meaning they’re able to make their own data insights independent from a data science team. This is HUGE for small and medium-sized businesses that can’t afford data science teams. 

Coding languages like Python and SQL still dominate machine learning, but with the adoption of no-code and natural language, non-technical users can understand and use ML to make business decisions.

Together we can ditch SQL and make machine learning more accessible to all if we think of data as something you can have a conversation with in human language.

Here's how to have a conversation with your data.