What is Natural Language Query (NLQ)?

Principal Realizations

NLQ turns human language into a database you can access to provide responses to user questions.

NLQ mostly comes in two flavors: guided and search-based. Aiming to understand a user request, a search-based NLQ system links human words with the actions the NLQ process can execute. A guided method guides you across the process of developing questions.

While many NLQ systems struggle with properly interpreting user inquiries, LANSA BI offers you a guided NLQ that removes miscommunications without restricting the kinds of searches users can make.

NLQ in Effect

Imagine yourself as a sales manager just entering a meeting. You already have a database with comprehensive sales records covering the past five years at hand. You should be aware of which consumers, within 100 miles of Miami, bought goods worth more than $200 following 12:05 p.m but before 5:00 p.m in November 2023. How would you like this search carried out?

One could choose to apply a conventional solution. Starting with something like, you could run a manual SQL search, working from:

You then have to keep coding in more variables so the query finds results at the appropriate distance from Miami and time.

Conversely, give Option B some thought. “Which consumers living within 100 miles of Miami bought goods priced above $200 after 12:05 pm but before 5:00 pm in November?” you could ask. Natural language query (NLQ) enables this.

Natural Language Query (NLQ) is what ?

Natural language query (NLQ) reporting tools let users regularly ask inquiries about data using natural speech, hence enabling self-service business intelligence (BI). NLQ simplifies the analytics process in the framework of BI by accelerating and easing searches, therefore aiding the decision-making process.

At least on a high level, the method is rather simple. A user first types or voices a query. They might also be able to formulate a query by choosing a sequence of options. Then a BI system responds to the question using keywords or selections from a set of suitable responses.

Depending on how the NLQ system is designed, the response could be a written or spoken one, a chart, or a report. The user provides to other stakeholders a simple, practical response that is rather easy.

How does NLQ operate ?

NLQ converts natural human language into language a database can grasp and use to run searches. Various technologies and approaches enable NLQ close the distance between human voice and database searches.

Within the framework of the above NLQ meaning, artificial intelligence and machine learning take front stage especially in relation to:

• Natural language processing (NLP): NLP lets computers comprehend regular speech by applying human grammar and lexicography.
• Named entity recognition (NER): NER, or natural language processing, is a component of NLP capable of text category word identification.

The process of turning human language searches into structured queries is rather simple when these technologies are used in concert with a database:

1. An NLP/NER-powered machine finds the human query’s categories.
2. Query analysis helps the categories to link with database items and actions.
3. The question turns into a syntax the database program can grasp.
4. The database answers the query upon acceptance.

Particularly search-based, the above NLQ meaning and procedure help to explain how various forms of natural language searches function. Guided NLQ, on the other hand, is when the user is presented the categories and activities they can do while being guided through the query process. This can provide the query process accuracy and speed.

How might NLQ support corporate intelligence ?

NLQ democratizes analytics, hence it is quite important to include business intelligence into your apps. Natural Language Query should have a familiar feel if you know how to Google anything. All you enter are keywords, which the algorithm matches from its databases and offers responses.

NLQ also helps inexperienced users create useful reports they may show to highlight their ideas driven by data. Without data scientists, this enables non-technical users to obtain actionable information to guide decisions.

NLQ removes the heavy lifting from the query process, therefore freeing analysts, managers, and decision-makers to devote more time to methods of using their insights and finding fresh approaches to maximize their data.

How would NLQ benefit consumers of analytics ?

NLQ provides a straightforward, self-service BI technology that fits the emergence of augmented consumer trends, thereby helping analytics users. They have no need of depending on data professionals; they do not need technical knowledge to apply it to make wise decisions.

This has various advantages, including:

• Draws people who would like NLQ more than more technical search answers, such as coding or applying business intelligence dashboards.
• Better acceptance of self-service analytics for consumers who would often object to dashboards or other visual-based BI components.
• Users lacking great expertise have access to self-service analytics.
• Teams will get deep insights more easily since the tools can show responses in the form of BI reports or infographics.
• They act as a beginning point; replies you may then mix with other instruments, such as dashboards and storytelling.

Forms of Natural Language Questions

Natural language query processing falls into two main types as noted above.

Search-Based Natural Language Learning

Search-based natural language querying lets you write questions into a search box matched for elements within databases. Usually found in the user interface of a BI platform, search-based NLQ tools are Every system is unique, hence the terminology users can use, the types of data the system supports, and the volume of data you can handle will all vary.

Based on searches, usually included in a BI platform, NLQ allows a user to type a question and obtain a rapid response. This can occasionally be troublesome since the NLQ system might not be able to answer a large spectrum of inquiries. You also might not receive enough direction on how to organize your queries – and with some systems, you hardly get any help at all.

Directional NLQ

Guiding NLQ helps the individual posing the question by providing lists of the kind of questions they might ask and encouraging them to select among the available actions on the data.

Returning to the initial example, consider a user wishing to investigate consumer data from a given location. Perhaps they started with choosing “Customers.” The guided NLQ system can then provide querying choices such as “dwelling within,” “onboarded before,” “onboarded after,” or “doing business with us for.” Next the user would select “living within.” The system might then present a series of options representing categories already matched with each consumer, including “less than 200,” “less than 250,” and so on.

Natural Language Inquiry Examples

Although NLQ has several uses, below are some of the most often used natural language query databases serve:

NLQ in Commercial Analytics

Natural query language can help to simplify and increase accessibility of the business analytics process for a larger spectrum of users.

For example, assume human resources has to find the top performers on your sales team in order to support the development of a performance bonus scheme.

NLQ natural language queries let HR use simple questions to identify which colleagues have the best sales. They can also probe a little farther, challenging the system regarding contextual elements as well. They can find, for instance, whether the amount of time each staff member has spent with the organization affects performance or if past years of experience have more bearing.

Customer Support and Chatbots: NLQ

NLQ is not new to the customer service scene, particularly with relation to chatbots. Amazon Q is one of the most lately included improvements to the NLQ-powered customer service game. Designed to assist Amazon Web Services (AWS) users, it offers a conversational interface allowing people to submit requests and query the bot.

For the latest current updates to AWS services or documentation, for example, ask the bot. It can also guide the design of tailored solutions by telling you how to create apps and even examine codes.

NLQ in Virtual and Voice Assistants

For several years, NLQ has also been a pillar in the voice and virtual assistant space. If you have ever used Siri or Alexa, for instance, you have made use of their NLQ technology. When you use Siri to operate on your iPhone, say, the answer you get back results from an NLQ-powered search.

This is the reason you might find many of Siri’s answers more constrained or basic than those of programs like ChatGPT. You might have observed, for instance, that Siri occasionally becomes repetitious. Asking the query, “How are you today?” you get the same response—that of “Not too shabby.” Thanks for asking after just a few tries.

This is so because Siri uses the possibilities in her NLQ-powered replying system from a quite small pool of responses.

Why Should NLQ Be Used ?

For consumers lacking the technical knowledge of data scientists, NLQ gives the query procedure accuracy and efficiency. It helps companies to:

• Empower Analysis: Provide more people the ability to examine corporate data.
• Improve Efficiency: Spend more time using insights than time searching questions.
• Faster training sessions for newly hired data analysts will help to speed up the onboarding process.
• Weave analytics closer into the fabric of your corporate culture.

The Difficulties of NLQ

NLQ systems present one of the main difficulties since they offer either minimal or no direction on which questions to ask while using them. Regarding how to utilize the instrument, there is likewise, if any, scant information accessible. Users may so find themselves utilizing language that the system misinterprets, producing erroneous, confused information.

Users may be compelled to seek assistance from data analysts or other professionals in order to address this difficulty. This reduces the time-saving and simplicity of use advantages of NLQ. Still, the new NLQ strategy seeks to remove these obstacles.