Querying Your Analyze Data with Simply Ask (NLQ)

These suggestions are recommended by Analyze BI's knowledge, which studies the associations between all entities on the system (users, data, models, widgets, formulas, clicks, filters, and more) to understand the collective wisdom of the organization. This leads to more "human" and context-aware outcomes from the Analyze BI AI engine. Simply Ask offers an easy way for anyone, regardless of their role, to gain insights from their data. This article provides an overview of how to enable, design and filter your dashboard with Simply Ask.


Activating Simply Ask (NLQ)

Using Simply Ask requires specific permissions within the LMS. Prior to accessing your Analyze dashboard to use the feature. Confirm the account which will be using Simply Ask has the required access. From the Setup menu select Analyze Licensing.

A report of users will appear. Here you can assign a new license or confirm your existing user is a Designer if they want to add Simply Ask to a Dashboard they own.

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A Viewer will be able ask questions with Simply Ask if it is enabled on a Dashboard shared with them.


How to Enable Simply Ask:

As a Dashboard owner, you may enable Simply Ask on any Dashboard. Open the Dashboard menu with the three dot button to the top right and select Simply Ask (NLQ) then Enable Simply Ask.

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A modal frame will pop up confirming you want to enable Simply Ask.

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The Dashboard Designer now has access to Simply Ask where they can ask questions and view the visualizations. This provides an opportunity for the Designer to test the experience before sharing it with Viewers.

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When the Designer republishes the Dashboard and shares it with Viewers, the Viewers will also see the Simply Ask button and be able to ask questions.

A Dashboard owner can disable the Simply Ask feature. To disable Simply Ask, open the dashboard menu and select Simply Ask (NLQ), then Disable Simply Ask.


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Asking Questions

From a Dashboard where you have enabled Simply Ask. Select the Simply Ask button.

Enter your question in the Ask a Question about your data field or select one of the predefined questions. These predefined questions are generated by the Analyze BI knowledge-graph, based on you and your organization's experience.

Type questions into the search bar. If a word is underlined in blue this means it’s ambiguous and you’ll be prompted to choose which field you’d like to use. You can also pull fields from the Available Fields pane on the right hand side directly into your question. By default, the NLQ model includes all formulas, fields, and filtered fields that already appear in the dashboard.

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Once you’ve cleared out the blue lines by selecting fields and refining your question, you can click run. You’ll see the actual query below the search bar. Additionally, you can change the widget type just as you would if you were creating a widget from scratch.

If you are a Designer you will have the option to pin the Widget to the Dashboard, download the Widget, or favorite the query for later. If you are a Viewer you can only download and favorite the query. Favorites are saved per user. If one person favorites a query it will not appear for all users of the Dashboard.


If you want to choose another visualization, you can select one from the right side. You can choose from any of the visualizations that support your data.



Start typing your question and Analyze BI will provide knowledge graph-based suggestions based on the fields in your data. You can click these suggestions to add them to your question. While you type, Analyze BI provides the following real-time assistance:

  • Automatic Suggestions for the Next Word: The system provides suggestions for filters and break-bys. For example, the system suggests the time break-by categories, such as "by week" and "by day".
  • Spellcheck: When you mistype or misspell a word, the system underlines it with a red line. Click the word to get a list of suggested correct spellings or click Ignore . If you do not do anything, the system will apply the first suggested correction.
  • Ambiguity Resolution: When the word you entered might be associated with more than one column or value, the system underlines it with a blue line. Click the word for a list of possible columns or members and select the correct one.
  • Word Auto-Complete: When you begin typing a word, the system will suggest possible words based on what you have entered, which enables you to complete the query faster.
  • Synonym Translation: When you enter a word that is not part of the data model, the NLQ engine, which comes bundled with an English dictionary of synonyms and the words in your model, automatically translates this query into a synonym that does exist in the data model (for example, income / revenue, or nation / country) and returns the correct result. Of course, if you use specific terminology that’s a direct synonym in the English dictionary; you can add it as a synonym to the model (see Adding Aliases).


Applying Filters in your Questions

You can apply filters and sorting to your data, as follows:

Filter Type Query Example
Specific dates and ranges of different date levels (year, quarter, month, week, day) Course enrollment by month 2019
Common time-frame periods such as 'last 2 weeks' and 'this quarter Course enrollment in the last 2 weeks
Common holidays and public events, such as "Black Friday". Course enrollment Black Friday 2019

Top / Bottom ranking filter.

This type of filter enables you to include only the top / bottom ranking fields.

The top 5 course names by course enrollment count

Aggregation functions:

Sum /Total, Count all, Count unique, Min, Max, Average, Median

Total course enrollments 2023
Break-bys of values by another category Sum course enrollment by department

Text filters

You can use the following filters:

Starts with, ends with, Containing, Equals, Doesn't Start With, Doesn't End With, Doesn't Contain, Doesn't Equal

Department by course enrollment where course name starts with ‘compliance’

Value (Member) filters

Single-select and multi-select filtering of specific values (members)


You can filter directly only by the categories that are indexed in the NLQ model. Either they are used as filters in the original dashboard or have been manually added as "indexed" in the NLQ model (see above). For example, let’s say that you have a Countries filter in your dashboard. Therefore, your query "Total course enrollment in France" will produce a result. However, if you create a query "Total course enrollment by John Marks" (where John Marks is a user’s full name), it will not produce a result, unless "Full name" exists as a filter in the dashboard or has been added manually as an indexed field to the model. If a value is not indexed, you can still ask about it - just not directly. You may ask about it using one of the text filters mentioned above: "like", "contains", etc. Instead of asking "Total course enrollment by John Marks", you can ask "Total course enrollment by User contains John Marks".

  • Sum course enrollment where course names are Course A
  • Total course enrollment of Course A
  • Sum course enrollment by department where department in (department A, department B, department C)

Dashboard formulas

Filters on dashboard measures (formulas)

MAU (Monthly Active Users) in Department B

(If MAU is a formula in your dashboard)

Dashboard filters

Filters currently applied on the dashboard

MAU in Department C

(If MAU is a filter currently applied on your dashboard)

Ascending and descending sorting Sum course enrollment by department sort by course enrollment DESC

Reserved keywords (for example: yesterday, >)

Full list of reserved words:

  • Top/ highest
  • Bottom/ lowest
  • Between
  • Larger/ greater/ more / >
  • Larger or equal/ greater or equal/ >=
  • Smaller/ less/ <
  • Smaller/ smaller or equal/ <=
  • Equal/ =
  • This day/ this week/ this month/ this quarter/ this year/ today
  • (Last/ past) day/ week/ month/ quarter/ year/ yesterday
  • (Last/ past) X day/ week/ month/ quarter/ year
  • X day/ week/ month/ quarter/ year ago
  • Next day/ week/ month/ quarter/ year/ tomorrow
  • Next X day/ week/ month/ quarter/ year/ tomorrow
  • Starting / from date
  • Until/ till/ to date
  • Like / contain
  • Start / begin with
  • End with
  • Sorting
  • Sort (default is descending)
  • Sort ascend / sort ascending / sort ascendingly
  • Sort descend / sort descending / sort descendingly
  • Date break by
  • By days / weeks / months / quarters / years
  • Daily / weekly / monthly / quarterly / yearly

Total course enrollment yesterday

Course enrollment count > 5,000


Example Questions to Ask:

The following questions follow the templates and suggestions highlighted above, they’re the best ways of receiving a response from our simply ask feature.

  • What’s the top 5 course name by course enrollment count?
  • Total complete course enrollment by date completed?
  • What's the average progress by full name?
  • Average time spent by course name?
  • Course enrollment by course type?


Saving Your Query for Later

After you have run your query, several additional options are displayed that allow you to save your query for later.

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Click to pin the current Simply Ask results to your dashboard as a widget (Designers only).

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Custom NLQ Model Creation

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Default Dates

The default dates are located at the top of the JSON file: "defaultDateTable": "Commerce","defaultDateColumn": "Date".

These are the dates that Simply Ask will refer to when you ask date-related questions, such as "What were my total sales last year". When you have more than one date column and you perform a query based on date, an ambiguity will arise. To avoid this, in the JSON file specify the default date table and column.

Adding Aliases

Aliases are synonyms - alternative ways to refer to column names. We recommend adding aliases in any case you expect that your dashboard users will refer to a field by a term that is more familiar to them, rather than by its actual column name.

For example: "Market" instead of "Country", "People" instead of "Employees", "Issues" instead of "Tickets", etc.

You can add as many aliases as you like. In the following example, when you perform a query with Simply Ask, you can also use "Quota" to refer to the "Quantity" column. { "table": "Commerce", "column": "Quantity","type": "numeric", "applySynonyms": true, "aliases": [ "Quota" ] },

Apply Synonyms

As described above, the NLQ model defines that synonyms should be supported for all items in the model. You can turn this feature off for individual columns by setting applySynonyms to "false". { "table": "Commerce", "column": "Quantity", "type": "numeric", "applySynonyms": false, "aliases": [ "Total Quantity" ] }

Adding and Removing Columns and Formulas

All columns and formulas that appear in the dashboard automatically appear in the NLQ model. You can add columns and formulas to the model's JSON file which enables users to ask about things that do not appear in the dashboard.

When adding columns/formulas, make sure to use the same format as the existing columns/formulas, and to set the "type" parameter correctly, based on the relevant field type (text, numeric). { "table": "gender", "column": "Gender", "type": "text","applySynonyms": true, "indexed": true }.

You can delete any columns/formulas you like from the NLQ model. However, NLQ will no longer recognize them and you will not be able to ask about them. This does not affect the dashboard.

Note: Additions are scoped to the specific data source the dashboard is pulling data from.


NLQ Model Updates

The NLQ model will automatically be updated when a dashboard is republished, the existing NLQ model will be overwritten with the up-to-date formulas, fields, and filtered fields in the dashboard.



The following limitations apply to the Simply Ask (NLQ) feature:

  • Only the US date format is supported (month first).
  • The NLQ model does not support dashboards that are linked to more than one data model.
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