The Natural Language Querying or "NLQ" tool enables users to ask analytical questions in simple English, and have the response presented as a result-set in the most appropriate visualization.
In effect, NLQ offers users a way to query data quickly and intuitively, without the need to navigate the details and elements of data models like dimensions, measures, or elements trees; without having to learn or understand how to use and place 'chips' in the drop zones; or even how to select and configure an appropriate visualization. Instead, Pyramid interprets the questions and automatically finds the relevant hierarchies, measures, and elements, places them into the appropriate drop zones, and selects the optimal visualization for the query.
Note: NLQ is available only with an Enterprise edition license and only works with ENGLISH.
Note: Pyramid's NLQ operates directly on ANY supported database (both SQL and MDX)
Discovery via NLQ
NLQ is one of 5 different Discover modalities that are available to users based on licensing, user profiles and system activated options. Arguably, NLQ represents the simplest way to perform data discovery in Pyramid because it does not require extensive training for users - relying instead on advanced AI functionality to interpret questions in the context of any presented data model and derive both the asked question and find an appropriate result.
Before it can be used NLQ must be enabled from the Admin console. Once enabled, NLQ is available to Pro licensed users from within Discover Pro and Discover Lite tools; it can be used when adding new content directly into Present and Publish; and it can be used from the 'analyze further' option in live running Present dashboards (accessible to both Pro and Viewer licensed users).
- Click here for more detail on how to access and launch queries using the natural language editor.
Asking Natural Language Questions
The NLQ engine is designed to accept plain English language questions that in turn drive data queries and visualizations of the results. At its most basic, the questions need to reference elements from the data set being targeted for analysis. However, there are numerous "smart" aspects to the engine that can both understand and impute analytic computations from various types of phraseology, word variations (aka word stemming) and key question structures that drive analytic concepts.
Understanding these nuances will allow users to drive smarter more intelligent questions and analysis.
- Question Basics: Learn the elements and basics of how to ask questions. For more advanced capabilities: