Yellowfin: 5 Key Benefits of a Guided Approach to NLQ
Providing your analytics users the ability to get answers from their data is useful, but only if the solution can guide them to ask the right questions.
While self-service analytics is typically a key goal for modern organizations to derive more answers from data, only two out of five (40%) report their analytics users can analyze data without help from IT, according to Ventana Research.
To help those without training better analyze data, tools like natural language query (NLQ) allow you to ask questions about your data, receive a best practice chart or report that answers your query and provide a deeper level of understanding.
NLQ tools typically come as a free text search bar, but the latest type, Guided Natural Language Query, extends its potential by taking a guided approach to self-service BI. One such example is Yellowfin Guided NLQ, which helps you build your question, know what to ask, and how to pose it, step-by-step, acting as a simpler way for anyone of any skill level to explore their data and get more relevant answers.
In this free whitepaper, we explain:
- What Guided NLQ is - and the other types of natural language query
- How Guided NLQ works - with examples
- Why it will provide true self-service BI for everyone - through 5 key benefits
This guide will assist you in understanding the benefits of Guided NLQ and its significant impact in opening up true self-service BI for everyone - not just analysts.