Natural Language Processing
Knowi's Natural Language capabilities is a powerful way to enable self-service analytics to non-technical users, by asking questions in plain English to drive insights and visualizations quickly. This empowers any member and individual the ability to make better, data-driven decisions, any time.
Natural Language capabilities are available for use across all datasets and widgets within a dashboard. It can be accessed using the Natural Language/Self Serve Analytics icon on a widget. As you explore your data with natural language, Knowi recommends questions based on your input and the data source available. The technology works by translating your natural language query to Cloud9QL statement to resolve your request.
NLP Across Datasets
Using natural language processing at the dashboard queries across all datasets within your account. Auto-complete suggestions will be recommended as you type based on the associated dataset. For example, if you had multiple datasets powering a single sales and marketing dashboard, and you wanted answer on the latest marketing information, typing your query matches the requested fields and aggregation to the datasets the user has access to.
Enable NLP Usage Across Datasets
Using NLP across datasets is a customer user feature that must be enabled by an admin-role. To enable this feature for users:
From the Knowi panel, click Settings and navigate to the User Settings page
In the Account Settings tab, click the NLP ACCESS switch to ENABLE/DISABLE the feature
NLP Within Widgets
Unlike across datasets, for use within widgets, natural language queries are applied directly on the dataset powering that widget.
NLP is available on all widgets inside a dashboard from the widget menu and available to all users, regardless of user role/permission.
The following are examples of natural language query to get you started:
Simple field selection
"bounced, sent, customer" "bounced and sent and customer" "Show me all for Wells Fargo" "all for customer Wells Fargo" "all fields for customer like Wells Fargo" "bounced and sent for Wells Fargo"
"Sum of sent sum of open by customer" "Total sent by customer by week" "Total sent, Total bounced for Wells Fargo" "Average Sent, Total bounced by customer by campaign_name for newsletter"
"all for date after January this year" "average close monthly" "close by date for january this year" "close by date for january last year" "close by date between january and march this year"