As a product manager, the purpose of providing your users with self-service analytics is clear.
You want everyone who uses your application’s embedded analytics tools to be able to:
🎯 Access, analyze and explore their data while using your product.
🎯 Discover helpful insights that can help them make informed, data-led decisions.
🎯 Extract more value from your product experience, so they don’t have to go elsewhere.
One big challenge, however, is in ensuring they can leverage your analytics tools independently.
Simply put, interrogating data for answers is hard for mainstream business people not trained in business intelligence (BI) solutions. Drag-and-drop,visual-based tools like dashboards can still be too technical for the average user, requiring specialized skills, or too simplistic to answer more complex questions. Unless your analytics can guide your users toward how to explore data and find insights, or what to look for, they may not be able to use it, in the way that you envision.
If your analytics isn’t tailored for every type of user, especially non-technical users, often-times they either don’t end up using your analytics at all or have to ask others for help - which means your analytics is not truly ‘self-service’. According to Ventana Research, only two out of five (40%) organizations report their users can analyze data without expert assistance from IT.
To better open up analytics for mainstream users, without the need to call others for help, are a new wave of modern BI tools - one such solution is natural language query (NLQ). NLQ is a BI capability that allows users, of all skill levels, to directly ask a question of their data using a search box provided within a part of the analytics interface.
They can type in a query using free-text, and the NLQ solution scans the keywords, matches them with elements in related databases, and presents a data-led answer, typically a chart or report - without waiting for help.
But what if someone doesn’t know what to ask of their data, or how to ask a question?
One type of NLQ, called guided natural language query, proactively guides the user as they begin to type a question, ensuring everyone can get answers, and your analytics is truly self-service.
Guided natural language query: An overview
Guided natural language query is a specific approach to NLQ technology that specifically guides analytics users in how to build their data questions. It helps them structure what they want to ask by providing generated lists of relevant options, and prompts them with suggestions as they type.
This means anyone can use and benefit from the tool, from non-technical BI users, to analysts.
Guided natural language query, like traditional NLQ, varies between vendors in terms of the level of support it provides each user. Some only provide pre-defined, static suggestions in drop-down menus, while other tools auto-complete the search box field as the user types their question.
In Yellowfin, our approach is to make NLQ a fully guided self-service experience, end-to-end.
Yellowfin Guided NLQ: Bringing true self-service BI for all
It takes the concept of guided natural language query a step further by having the tool proactively help the user structure their data questions, from start to finish, and includes thousands of comprehensively modeled question types and sequences for the user to select from as they type in their question, enabling anyone to ask complex (or simple) questions of their data.
Having Guided NLQ can help product owners ensure all their users can make use of their product’s BI tools, without having to ask experts for help generating a chart or report for them, and make the use of your analytics more pervasive among your customers overall.
Want to learn how Guided NLQ can benefit your analytics?
In this free whitepaper, we explain:
- What Guided NLQ offers for your product experience, via 5 key benefits
- How Guided NLQ works - with demonstrated examples
- Why Guided NLQ can provide true self-service BI for every type of BI user