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Natural Language Query in Data Analytics: Definition, Benefits, and Examples

Joanna He
Senior Director of Global Growth
Nov. 02, 2023

Imagine being a retail manager needing data insights. You'd typically request these from the IT or analytics department. The IT team would extract, clean, and analyze the data, then provide a report with images and charts. However, this process can be time-consuming, not ideal for time-sensitive projects. The above scenario is how traditional business intelligence and insight-gathering methods work. It's in total dependence on the experts for all insights and decision-making.


But what if you could access the analytics platform and chat your data using plain everyday language? Rather than overthinking how to learn SQL queries or waiting for the experts, you can generate insights using natural language. Well, it's possible thanks to combining AI and machine learning with business intelligence tools. This feature is known as Natural Language Query, and in this article, you will learn about the benefits and examples.


What is NLQ (Natural Language Query) in Data Analytics?


Natural Language Query in data analytics allows users to use simple everyday language to generate insights from data, bypassing the need for traditional SQL queries. The data analytics tool equipped with this function employs artifical intelligence (AI) and machine learning algorithms to analyze the text query, search for matching keywords, and deliver the result. It's as realistic as conversing with a friend or in-house data analyst.


This function simplifies the interaction with data in your organization, allowing more users to access insights faster and with detailed information.


How NLQ Benefits Data Analytics


Natural language query in data analytics offers businesses a solution for achieving the following results.


Increases Company-wide Analytics Adoption


Generating insight from data requires technical expertise. Therefore, most non-technical departments will not adopt data analytics in their operations because it requires extra training and learning. However, with the Kyligence Zen analytics platform's natural language query feature, you can interact with your data to obtain more straightforward explanations.


This function eliminates the complex learning curve to master analytics tools and fosters company-wide adoption. Since there's no technical expertise needed, different departments will be willing to integrate data into their processes and deliver better results.


Generate Simple Insights from Complex Data


Dashboards and charts don't always present insights in an easy-to-understand format, especially for non-technical users. However, using the natural language query feature on the Kyligence Zen analytics platform, you can chat your data for a more straightforward explanation.


For example, if you noticed the sales for Q3 dipped by 5% from the dashboard report, you can ask "why". All you have to do is enter "Why did sales for Q3 fall by 5%?".


The Copilot will provide a detailed analysis and explanation, showing you the different factors and touchpoints responsible for the dip. You can also ask specific questions about other sub-areas that might be confusing and get more straightforward explanations.


Better Sales and Marketing Execution


Data steers all aspects of business growth, including sales and marketing. Typically, you are dealing with fierce competition, innovative new products, customers changing needs and trends. Data analytics enable you to extract insights from customers' data, market trends and sales results to make accurate time decisions. However, achieving this feat quickly using the traditional dashboard method might be time-consuming and insight-limited.


Using Natural language query analytics tools, you can analyze your marketing metrics in real time to create personalized campaigns and strategies.


NLQ gives you a competitive advantage because you can ask more profound questions and identify relationships and root causes of downtrends. Marketing managers can save time, dig deeper into specific marketing results, and take necessary action quickly. Moreover, natural language query tools like Kyligence Zen also recommend solutions to data downtrends, which can guide your strategy process.


Example of Natural Language Query in Kyligence Zen


Kyligence Zen is a powerful analytics platform that allows you to analyze all your data from a single dashboard and inbuilt AI copilot features. It also has inbuilt templates for your analysis structuring, reducing the building time from scratch. Moreover, the Kyligence Copilot has a natural language query function enabling you to chat with your data, extract deeper insights and get valuable recommendations. 


To illustrate the example of NLQ in Kyligence, we will use the demo retail metrics available in the metrics template section.


Step One


Navigate to the metrics template on Kyligence’s site, which will display multiple templates from different industries.

Metrics templates on Kyligence Zen

Step Two


Next, click on the retail metrics template and click the “try the template” button and then sign in to Kyligence Zen to  reveal the available metrics like total sales, number of repeat purchases, etc. 


For this illustration, we will select the total sales metrics to reveal a chart showing the trends for sales over a period.

Retail metrics templates provided by Kyligence Zen

Step Three


Next, launch the Kyligence Copilot feature to reveal the chat box for text input on the upper right corner of Kyligence Zen. Imagine you want to run a customer reward program and need information on the customers with the highest purchases for 2018. Using the copilot natural language query feature, you can enter:


"List the top 5 customers with the highest purchase in 2018".

Output of top 5 customers with highest purchase from Copilot

The Copilot will analyze the query, search for keywords and provide a report containing a bar chart and text-based explanation of the results, as seen below:

Text-based explanation of the output

You can use Kyligence Copilot to perform in-depth root cause analysis with simple natural language queries. For instance, you may input the metrics you wish to analyze, such as "Demo | Total Sales - Retail.” After this, you could ask, "Why did the total sales drop from December 10, 2018 to December 14, 2018?". Kyligence Copilot will automatically generate a root cause analysis. It not only displays the related graph but also analyzes the reasons behind any data fluctuations you might observe.

Root-cause analysis of sales drop by Kyligence Copilot

You can dig deeper to extract more insight by asking questions like in the previous query.


Generate Deeper Insights from Your Data with Kyligence Zen NLQ Features


Natural Language Query simplifies data interaction and democratizes analysis, marking it as a powerful feature in data analytics. It allows users to generate insight using everyday languages, improving productivity and decision-making.


Rather than waiting for the experts to create a dashboard and report, you can easily chat with data and get responses in real-time. This will allow your organization to exploit new opportunities and identify root causes and relations in data that a traditional analysis process would've missed.


Kyligence Zen is one tool that provides users with a Copilot feature for natural language query tasks. You can generate simplified reports with recommendations from different data touchpoints with just a few words.


Sign up on Kyligence to start chatting with your team and unlock the full potential for business growth.