Tips for Cleaning Discarded Phone Numbers

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Sales Manager Sarah is staring at a mountain of data on sales, products sold, and more. Despite having everything available, she is still overwhelmed by the sheer volume of information. Her manager wants to analyze the top products sold for a week. Suddenly, she remembers the Natural Language Query (NLQ) in her CRM system and types in a simple query- “Give me a report on the top products sold from 19th March to 23rd March in a tabular format.” She quickly gets the results and reports to her manager.



The need for Artificial Intelligence (AI) and Machine Learning (ML) has made it easier for businesses to grow efficiently. We use different mechanisms to perform specific operations and NLQ is one of them. It allows users to interact with different applications in everyday language and make better business decisions. Let’s dive deeper into it.

Identify natural language queries
NLQ is one of the aspects of AI that makes human-computer interaction europe cell phone number list easier and more accessible using natural language. Instead of using programming languages, users can ask questions related to data or make requests in plain English. The system will respond quickly and provide you with accurate information. Please consider the two queries below:

SELECT LastName, FirstName, FROM Employees WHERE Department='Marketing';
Show me the names of the people in the Marketing team.
The first example introduces a traditional query language that may not be familiar to everyone. It often requires technical knowledge to construct such queries using this syntax. In contrast, the second example is a natural query that provides a more user-friendly approach, allowing people to interact with the database using natural human language.