Have you ever walked into a ski shop and had only a vague idea about the technical aspects of this popular winter sport?
“how can I help you?” The sales assistant will ask.
Now this seems like an easy question to answer – but if you don’t have skateboarding experience, this can be a tricky question to answer. When you are a beginner, you don’t know what you don’t know. And if you and your sales partner don’t ask each other the right questions, you could easily end up buying items that aren’t suitable for your needs or abilities. Even worse – your fear of failure and being “discovered” as a poser may prevent you from even walking through the front door of a store.
guess what? Many people in your field 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 things that can make us feel… stupid.
Like a new customer at a ski store, your employees don’t want to ask silly questions — or risk exposing their little knowledge. Since no one wants to feel stupid when it comes to data analytics, it is not unusual for intimidated business users to put their trust in intuition and hope for the best.
The final result? Your expensive analytics and business intelligence software is not being used, and your analysts wonder why no one is asking for help using it. It is precisely this tension that inspired and motivated the creation of a Natural Language Query (NLQ).
NLQ enables anyone, including non-technical business users and savvy 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 directed search. (Over time, we should be able to literally ask a question – or at least write a question freely – but technically we still have a few years to go.)
Opening Search NLQ presents the user with an empty search bar. This approach has a lot of flexibility, but it requires the person who is querying the data to have a thorough understanding of the available data, as well as some basic knowledge of the syntax. If you’ve ever asked Alexa a question and gotten a poor answer, you can understand why today’s search-based NLQ tends to work better when the questions are simple. If you don’t ask your question exactly the right way, you can get a nonsensical answer.
Guided NLQ, on the other hand, removes barrier-to-input issues found in search-based NLQ by giving the user a choice of filters to use when making a query. Filters hide the complexity of the question’s syntax, language, and syntax and provide the engine with the context it requires to return actionable analytics. This low code/no code in BI approach allows even most of your non-technical employees to experiment with different combinations of filters until they get the answer they need to solve a business problem.
Guided NLQ allows personalized and authentic self-service. It allows even your lower technical staff to slice and shred data in real time, on their own, without having to wait for someone from your data analytics team to show them how to query the data. Router NLQ will free data analysts from spending time answering ad hoc queries and empower the business user by allowing them to:
- Explore data without fear.
- Query data without having to know anything about the technical side of data discovery.
- Have more productive conversations with members of your data analysis team.
Knowledge gaps create a significant barrier to entry for these new hires in data analytics and play a huge role in preventing business users from getting the insights they need from the data available to them. In most organizations, the time for the analytics team to respond to a query can be days, weeks, and in some cases, months. In today’s world of flexibility and speed, this is not good enough.
NLQ has the power to change the way your employees interact with their data. When you make data analysis accessible to employees with a mentoring NLQ, it becomes easier to foster a data-driven culture at the organizational level.