Natural Language Query (NLQ) is Key for Self-Service BI

Modern business intelligence (BI) and analytics software solutions offer more ways to explore data and find insights than ever before. However, ensuring everyone in the enterprise can use analytics independently, when they need it, remains a challenge.

For analytics to be adopted across the full enterprise, your tools need to be tailored for all users.

While many BI solutions today provide streamlined experiences, some data literacy and knowledge is required to fully understand them. Dashboards, for instance, are considered self-service BI tools, but they are often still too technical for the average person to use effectively.

Today, only two out of five (40%) organizations report their people can analyze data without getting help from IT, according to Ventana Research. To better enable business users to interrogate their data independently, certain BI vendors now provide a natural language query (NLQ) ability that sits alongside their dashboards and reporting. Rather than having to search for answers in a traditional dashboard or chart, NLQ allows analytics users to ask questions of their data directly.

An Overview of NLQ

Natural language query is a self-service BI tool that provides the ability for an analytics user to ask a question using non-technical language and get an automatically generated answer instantly. Using data modeling and machine learning technologies to parse questions for key terms, NLQ scans related databases and generates a tailored report or chart that provides relevant insight.

The integration and implementation of natural language query capability, as well as the complexity of the questions and the types of data supported, varies between vendors. The most common and traditional approach is search-based NLQ, which places a free text search bar somewhere within the user interface (typically on a dashboard) that allows BI users to begin typing a question and get a list of reports and charts generated quickly as potential answers.

A smaller number of vendors also include support for voice commands in their NLQ solution.

As NLQ becomes an evolving part of BIs, however, some vendors have their natural language offering account to evolve for additional barriers: A user still has to know what question to ask, and how they can use it to avoid inaccurate or irrelevant results to their question.

The new approach is called guided natural language query, or guided NLQwhich takes the concept of NLQ further by programming the solution itself to guide the user as they type a question, helping them structure it using pre-defined sequences and suggested prompts.

This eliminates the burden of knowledge or any potential problems in accurately interpreting the semantics (language) of a user’s question, as the system itself assists them in building it, from start to finish. They don’t need prior technical knowledge or need to go to IT to get started finding answers from data.

When employed correctly, guided NLQ can help make the use of analytics more pervasive and open up analytics for every type of business person in the enterprise—not just experts.

Self-Service That Suits Every Enterprise User

The aim of NLQ is to help workers not accused to traditional, structured BI tools find insights and information they need to inform and make business decisions, while also being a helpful tool for advanced users throughout the enterprise.

For non-technical analytics users, NLQ is an easy way to get detailed answers from their data faster than exploring a dashboard or chart and use it as a springboard for their ad-hoc reporting.

Similarly, advanced users can leverage NLQ, whether guided or traditional, to quickly generate a report or chart and further build upon it.

Generated insights using NLQ can be added into existing analytic content, such as dashboards, reports and data stories, to enhance them with additional detail, relatively quickly and easily.

Finally, NLQ can be embedded into the interfaces of your users’ workplace applications, to make analytics far more ubiquitous in their day-to-day workflows. They can ask a question, get an answer, and start exploring data much faster and more easily than via dashboards alone.

Ultimately, having an awareness of your users’ varying self-service BI needs, and how the new wave of tools such as Guided NLQ can help to further open up analytics to more people, will help you ensure your analytics tools are adopted and usable by everyone throughout the enterprise.

Leave a Comment