What is natural language search (NLS)?

Business leaders find themselves involved in a range of high-priority tasks, most of which require making critical decisions.

Let’s say you’re the sales head of a global organization. You’re ready to make an important decision about next quarter’s sales strategy, but you must first look at the right data set. You know it exists somewhere in your organization’s databases, yet it’s not within the arm’s reach. Someone must find it, clean it, and make a report before you can act.

Now, imagine if you could talk to your data warehouse; ask questions like “Which country performed the best in the last quarter?” or “What product sold the most in North America?” and instantly receive a detailed breakdown, complete with charts and insights. Believe it or not, striking a conversation with your data warehouse is no longer a distant dream, thanks to the application of natural language search in data management.

What is natural language search?

Natural language search is an artificial intelligence (AI) based technique that relies on natural language processing (NLP) to enable you to interact with machines without having to use complex, syntax-based queries or commands. Instead, you use simple language to search for required data within your database, as if speaking with a fellow human being.

It’s evident that the future will be heavily AI-driven. To fulfill their need for faster decision-making and a more inclusive approach to utilizing data, organizations will need to incorporate AI and machine learning (ML) models and techniques. The shift to AI promises to streamline operations and create a setting where everyone is equipped to make informed decisions.

Speaking of shift, it won’t be long before we see someone saying, “Gone are the days when organizations relied on SQL experts to work with data.” And they can’t be blamed because when you can use simple English for question answering (QA), why would you spend time and rely on queries that must be 100% syntactically accurate before you can execute them?

What about natural language query (NLQ)?

Natural language search has a very specific use case in data management and analytics, where it’s used to query structured data. In fact, when used for these purposes, you will find that it’s often referred to as natural language query (NLQ)—yes, another term using the “natural language” prefix.

No wonder it’s common to confuse natural language processing (NLP), natural language search (NLS), and natural language query (NLQ)—let’s put an end to this confusion once and for all.

• The umbrella term that includes all the other sub-fields is NLP. It is a sub-field of AI that enables computers to understand and generate human language.

• Natural language search (NLS) is what you use to search for data, structured and unstructured, using any language spoken by humans, such as English or French.

• Natural language query (NLQ) can be thought of as a sub-type of NLS technique that enables you to query structured data stored in databases and data warehouses.

An example to understand natural language search

Previously, search engines used to fetch results by matching exact keywords input by users with the information stored in the database. The downside of this approach is that users or website owners can easily manipulate the search engine results page by stuffing keywords into their content, negatively affecting the user experience.

However, search has changed significantly with the use of techniques like semantic search and NLS. Instead of only matching the keywords, search engines now also take into account the context and meaning of the query to provide the most relevant results.

Businesses are now using this technique in data management to simplify and accelerate the process of acquiring insights. An example would be using natural language to query a data warehouse. For example, instead of using queries like:

SELECT SUM(sales) FROM orders WHERE region = ‘Europe’ AND date BETWEEN ‘2023-04-01’ AND ‘2023-06-30’;

where missing a single quote (‘) can lead to the infamous syntax error, you can directly ask it something like “What were our total sales in Europe last quarter?” and proceed to decision-making.

The technology behind natural language search

In addition to NLP, NLS uses other ML and AI models to comprehend the intent behind your queries, or questions, to be precise.

Modern data management and integration platforms are powered by advanced AI features, with natural language search being one of them. As you input your question, the NLP technique breaks it down into phrases and contexts to identify the base forms of every word.

After processing the input, the system relies on machine learning algorithms to learn from past interactions and improve its ability to predict user intent and refine search results over time. Deep learning takes this a step further by enabling the system to understand different meanings of a word based on the context it’s used in and reduce ambiguity.

Semantic search is another key technology behind natural language search. As the name suggests, it helps further improve the accuracy of the result by using knowledge graphs and entity recognition to connect together related terms.

To ensure that your natural language search system is working correctly, you should get similar results to a question asked differently. For example, if the sole source of revenue for your organization is the sale of products, then “What was the total sales figure last quarter?” and “What was the total revenue last quarter?” should provide the same results.

Integration with your organization’s systems

To enhance the way your teams interact with organizational data, natural language search needs to be deeply integrated into your data management platform, as well as any data repositories, such as:

• Document management systems: To quickly find and analyze data hidden in documents, such as PDF reports or invoices.

• Data lakes and data catalogs: To search vast repositories of unstructured and semi-structured data without knowing file names.

• CRM and ERP systems: To ensure that everyone in your organization is up to date with important customer and inventory data.

• Business intelligence (BI) tools: To democratize data analytics and speed up decision-making.

• Chatbots and virtual assistants: To streamline access to information and improve self-service capabilities for employees and customers.

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Benefits of natural language search

You’ll find that the main benefits of using natural language to search for the required data in a data storage system are simplicity and speed. Here are additional advantages:

Increased Accessibility

Natural language search opens data access to everyone in your organization. No longer are technical skills like SQL or coding required to retrieve insights. Non-technical users, including salespersons and HR personnel, can ask questions and immediately get the answers they need.

Enhanced Data Exploration

When you can talk to your data interactively, you can ask follow-up questions or drill down into specific data points. For example, after asking “What were our top-selling products last month in Europe?” you can probe further and get granular details by asking “Which countries contributed the most to these sales?”

Reduction in IT Dependency

Overburdened IT has long been a problem in several organizations, primarily because of a lack of a straightforward method to access and manage data for non-technical team members. Natural language search capabilities built into modern data platforms neutralize this problem and make it a simple matter of question answering.

Use cases across industries and functions

Given the benefits it offers, many organizations across industries are already using natural language to make their data work for them:

Retail

When it comes to analyzing customer buying trends, inventory levels, or marketing campaign performance, nothing eclipses the question-answering technique. As a marketing manager, you can directly ask your data questions like “How did our summer sale perform compared to last year?” and instantly receive a breakdown of sales data and customer insights. By the time someone gathers the data and compiles the results, you’ll already have made a well-informed decision.

Healthcare

AI is already aiding practitioners by providing them with EMR/EHR summaries and saving them the hassle of studying them manually. With NLS, this goes a step further—you can query the system to extract relevant information from these summaries. In fact, if there wasn’t a regulatory requirement to document everything, one could question the very need for summaries when you can directly retrieve the exact data point you need.

Read more: intelligent document processing in healthcare.

Finance

If you’re in the finance industry, natural language search can help you analyze investment portfolios, risk assessments, and customer transactions. As a financial analyst, you could get the data you need by asking straightforward questions, such as “What were the top-performing sectors in the last quarter?”. The ability to gain such insights without relying on reports or visualizations eliminates unnecessary tools and makes your data stack leaner.

The future of data management with natural language search

The advancements in AI and the simplicity it offers will only lead to more and more organizations adopting natural language in the foreseeable future. With the ability to seamlessly interact with data through conversational queries, natural language search makes data and insights more accessible and empowers teams to be more agile and informed. It is set to become an essential tool for businesses looking to extract more value from their data while reducing the needed effort.

The incorporation of multi-modal search, where users can interact with data via other means, such as voice or images, will make meetings with decision-makers more interactive. Instead of scanning visualizations or reports, business leaders can get the answers they need in real time.

As technology continues to improve, we can expect to see NLP algorithms that are highly accurate and efficient when it comes to understanding human language. For example, while there’s still a long way to go, AI systems with improved emotional intelligence will be able to make highly personalized product and service recommendations, improving the overall buyer journey.

As exciting as these advancements sound, there are some important factors to keep in mind, namely data quality, privacy, and security—get those right, and you’ll have a reliable AI assistant to help manage data. In other words, you need robust AI governance to ensure all three.

Conclusion

As evident, the role of natural language search goes beyond making search easier. When implemented right, it enables you to unlock deeper insights just when you need them—not after spending hours sifting through data. This feat alone makes it a significant advantage in a very competitive space.

So, are you ready to unlock the full potential of your data? Try Astera Intelligence and make AI and NLP simplify data management across your organization. Or, if you want to discuss your use case, contact us today!