Creating Stories with Embedded Analytics

Objective

After completing this lesson, you will be able to create a story in Embedded Analytics.

Stories

A story is a presentation-style document that uses charts, visualizations, text, images, and pictograms to describe data. The following image shows a story that displays credit data in a table and chart. Only users that belong to the Administrator or Author user group can create a new story.

A story is a presentation-style document that uses visualizations.

Widgets are the display objects in a story. Examples of information shown in a widget include tables, charts, images, and text.

To create a story:

Open Embedded Analytics.
Select the Create (+) icon on the toolbar.
When prompted, select the Optimized Design Experience and select Create.
Select the data model.
A new story with a blank canvas is displayed.
To add a widget such as a chart or table, drag a selection from the Widgets section in the left panel.
If you add an object such as a chart or table that requires you to select data fields, a builder tool will appear on the left side of the canvas.
Use this builder to select the information you want to display in the story. For example, for transactions, you may want to display the Order ID, date, product ID, customer name, and value.
Choose the Save icon on the toolbar to save and name the story.

To see how to create and share a story, watch the video .

Exercise: Create a Story in Embedded Analytics

ExerciseStart Exercise

Extracting data from Stories

Data can be extracted from a story by exporting the entire story or by exporting the contents of a table or chart.

Embedded analytics stories can be exported to a PDF or a PowerPoint (PPTX) file by selecting Export… from the File menu.

The File menu in embedded analytics, with the Export menu item selected.

To export records from a table, select the Action menu on the table and select Export. Table records can be exported to CSV, XLSX, or PDF. You can also select from a list of delimiters including comma, semicolon, tab, and more. Table exports have a limit of 3 million cells or 60 columns.

A table in embedded analytic. The action menu is selected, and the Export option is highlighted.

Data Models

When you create a new story, you are first prompted to select a data model. Data models determine which data fields are available for use within each story. Each story in embedded analytics is based on a data model.

The following table shows some common data models, a description, whether this data model is available for a payee to view, and whether the period type is based on the fiscal or the Gregorian calendar.

Embedded analytics has over 20 data models that are included in every environment. You may notice that many of these data models, such as Credits, Incentives, and Payments, are based on the output of compensation rules. However, some data models are more specialized, such as Model Measurements or Plan Optimizer.

Most data models are available for payees to view as long as they have access to embedded analytics. Other data models, such as Transactions and Classified Transactions, are visible only to users with access to all data.

The table below shows a sampling of commonly used data models and their descriptions.

Data Model	Description	Viewable by Payees?
Transactions	Displays all transactions.	No
Credited Transactions	Includes all of the credits that are generated for the end-user along with the related transaction and order-specific details	Yes
Uncredited Transactions	Displays transactions that have not yet been credited and have not contributed towards any payments for a given period.	No
Payee Transactions	Displays transactions by payee based on assigned credits	Yes
Credits	Contains the results of the crediting stage, and for each payee, shows whether they got credit and how much credit they received.	Yes
Incentives	Includes the amount of incentive earnings for each payee, quota values and rates for each of the items being measured.	Yes
Commissions	Displays commissions whose incentives are calculated based on credits rather than measurements.	Yes
Measurements	Displays the values that represents achievement for each payee.	Yes
Deposits	Displays the total payout earned for each payee in a period, including Earning Groups and Earning Codes.	Yes
Payment Summary	Displays payee payment information, including earnings from prior periods.	Yes
Balances	Displays payee balance information for the period.	Yes
Model Credits	Displays credits for a period created using the Modeling feature.	No
Model Measurements	Displays measurements for a period created using the Modeling feature.	No
Model Incentives	Displays incentive earnings for a period created using the Modeling feature.	No
Model Deposits	Displays deposits for a period created using the Modeling feature.	No
Disputes	Displays disputes submitted by payees. Contains volume of cases, categorization by dispute status, dispute type, assignment, etc.	No
Plan Documents	Displays the status of distributed plan documents.	No
Pipeline Metrics	Enables Admins to see different types of pipeline runs (Compensate and Pay, XML Import, etc.) that have been executed in the system.	No

Team Management

Create a Team in Embedded Analytics

A Team in Embedded Analytics is a group of users assigned for a particular purpose. They are useful when you have a group, such as a sales team, with whom you’d like to share stories or folders regularly without having to select the members one at a time. Only Administrators or Authors can create Teams.

A Team can have an associated folder. This is a very easy way to share contents of a folder to the same team regularly. If you create a folder in a Team, the folder can only be deleted by deleting the Team.

The Teams dialog box in embedded analytics.

To create a Team:

Select Teams from the Files menu.
Select the Create icon to open the Create Team dialog box.
Add team members and select Create.

Summary

Embedded Analytics data is displayed using stories, which consist of tables, charts, visualizations, and text.
Data models in Embedded Analytics determine which data fields are available in a story.
Data can be extracted by exporting a table to a CSV, XLSX, or PDF format. Up to 3 million cells or 60 columns can be exported at a time.
Teams in Embedded Analytics are groups that share stories or folders.

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