Natural language query (NLQ) is an AI analytics capability that allows users to interact with and analyze complex databases using layman’s terms.
There is no need for a query language like SQL, advanced analytics skills, or even code. Users can type (or, in some advanced cases, even speak) their query in everyday language, and NLQ will perform a series of text-to-SQL functions to retrieve the desired data.
How Natural Language Querying Works in Modern Databases
This is how NLQ turns simple language into structured queries that deliver database-driven insights:
Step 1: First, the user poses a question in plain language. Say, “How did our XYZ sales figures compare to last month’s?”
Step 2: Next, the NLQ interprets the intent of the question by:
- Parsing the query into its grammatical parts
- Identifying word meaning and context
- Finding core entities like names, places, and dates
- Mapping the interpreted findings to SQL functions and database structures
Step 3: Third, the NLQ creates a SQL query from the intent of the natural language query (derived in Step 2).
Step 4: The NLQ executes the SQL query and presents the findings in human-readable form. This could be a graph, a chart, or natural language. Some even generate narratives and provide additional context.
NLQ turns complex data retrieval and analysis into a conversational exchange.
Key Benefits of Using NLQ in Enterprise Environments
Businesses are just beginning to explore the benefits of utilizing natural language queries in their enterprise environments. Here are just a few:
- The democratization of information: Once, only highly trained data analysts who knew SQL or other query languages could plumb databases for key information. That information would then have to be interpreted and presented to key stakeholders, risking dilution or loss at every step. Now, anybody can ask anything about a complex database and, using NLQ, can get an answer they understand.
- No more lag time for business-critical data: Data drives decisions. When a large amount of processing must take place before that data is found, much less usable, key decisions lag and opportunities are lost. Using NLQ, businesses are given the ability to act immediately on market trends, make real-time connections they could not make before, and gain greater understanding of complex business data that can provide needed context.
- Optimization of business-generated intelligence: By virtue of operating in a digital environment, businesses are generating useful and usable information daily, even by the second. Without the means to interpret this data, many companies (especially smaller, more resource-strapped ones) can let that valuable information slip through the cracks. Regardless of whether they have the technical teams to run complex queries, organizations of all maturity levels can leverage NLQ to derive key insights from data they already have, —and that has likely been underutilized until now.
