Service business

The Role of NLQ in Self-Service Business Intelligence

Have you ever walked into a ski shop with only a vague idea of ​​the technical aspects of this popular winter sport?

“How can I help you?” ask the seller.

Now, that seems like an easy question to answer – but if you don’t have any skiing experience, it can be difficult to answer this question. When you’re a beginner, you don’t know what you don’t know. And if you and your seller don’t ask the right questions, you could easily end up buying items that aren’t well suited to your needs or abilities. Worse still, your fear of failure and being “discovered” as a poser might even keep you out of the store’s front door.

Guess what? Many people in your industry probably feel the same way when they have to query data to make a business decision. The problem is that data analysts, like the ski shop assistant, have their own language and know a lot of technical stuff that can make us feel… dumb.

Like the new customer at a ski shop, your employees don’t want to ask silly questions or risk revealing how little they know. Because no one wants to feel stupid when it comes to data analysis, it’s not uncommon for intimidated business users to trust hunches and hope for the best.

The final result ? Your expensive business intelligence (BI) and analytics software sits idle, and your analysts wonder why no one is asking for help using it. It is exactly this tension that has inspired and motivated the creation of natural language queries (NLQ).

NLQ empowers anyone, including non-technical business users and smart analysts, to ask questions about their data and get instant answers in the form of best practice reports and visualizations. There are two types of NLQ: open search and guided search. (In time, we should be able to literally ask a question – or at least freely type a question – but technically that will take a few more years.)

Open NLQ search presents the user with an empty search bar. This approach is very flexible, but it requires the person querying the data to have a general understanding of the data available, as well as a basic understanding of the syntax. If you’ve ever asked Alexa a question and gotten the wrong answer, you can see why today’s research-based NLQ tends to work better when the questions are simple. If you don’t ask your question the right way, you may get an answer that doesn’t make much sense.

Guided NLQ, on the other hand, removes the barrier-to-entry issues found in search-based NLQ by giving the user a choice of filters to use when building a query. Filters hide the complexity of question syntax, language, and structure and provide the engine with the context it needs to return actionable insights. This low-code/no-code approach to BI allows even your most non-technical employees to experiment with different combinations of filters until they get the answer they need to solve a business problem.

Guided NLQ enables true self-service ad hoc BI. It allows even your less technical employees to slice and dice data in real time, on their own, without having to wait for a member of your data analytics team to show them how to query the data. Guided NLQ will free your data analysts from the time spent answering ad hoc queries and allow the business user to:

  • Explore data without fear.
  • Query data without needing to know anything about the technical side of data discovery.
  • Have more productive conversations with members of their data analytics team.

Knowledge gaps create a huge barrier to entry for employees new to data analytics and play a huge role in preventing business users from getting the insights they need from the data that’s at hand. their disposal. In most organizations, the time it takes for an analytics team to respond to a query can be days, weeks, and in some cases, months. In today’s agile and fast-paced world, that’s just not enough.

NLQ has the power to change the way your employees interact with their data. When you make data analytics accessible to employees through Guided NLQ, it becomes even easier to foster a data-driven culture at the organizational level.