LinkedIn Learning - Power BI connector

The LinkedIn Learning Power BI connector is a specialized data connector designed and developed to establish a direct connection between Power BI and LinkedIn Learning APIs. Through this integration, users can access essential insights and engagement metrics, including course completions, hours of content viewed, and active learning days.

These insights can be aggregated, based on various entities, such as individuals, groups, accounts, or content objects like courses and videos. Organizations analyzing these metrics can effectively measure the effect and success of their learning and development initiatives.

Summary

Capabilities supported

• The Power BI Connector facilitates the extraction of learner engagement data and course information in a straightforward and practical manner.

Advantages

• No need to download or receive CSV files.​
• Ability to query data periods greater than one year.
• No requirement for coding solutions to connect to the LinkedIn Learning API for learner and asset data.

Limitations

• Learner license status isn't accessible in our reporting API. So this data isn't available in the Power BI Connector.

Prerequisites

The scenario outlined in this tutorial assumes that you already have the following prerequisites:

• A Microsoft Windows PC ​
• Microsoft Power BI Desktop: Download here
• LinkedIn Learning admin access

We recommend that you use the latest version of Power BI to ensure that you have access to the latest version of the LinkedIn Learning Connector.

Steps to configure LinkedIn Learning Power BI connector

The following steps outline the process for configuring the LinkedIn Learning Power BI Connector:

Authentication

To obtain your Client ID & Client Secret from LinkedIn Learning, follow these steps:

1. Sign-in to LinkedIn Learning, if you aren't already in the Admin screen, select Go to Admin, then select Me > Integrate.

   

2. Within the Access content and reports via API, the client ID and Secret that was generated for the Power BI Connector is locatable here.

3. Next, open Power BI, select File, then select Get Data.

4. In the search box, look for "LinkedIn Learning".

   

5. Select the LinkedIn Learning Connector.

   

6. When prompted input the Client ID & Client Secret that was generated within LinkedIn Learning into the Connection credentials section. Start_date and end_date datetime parameters are optional and not required for successful connection.

   1. Select Basic for the Authentication kind.
   2. Input the Client ID into the Username section.
   3. Input the Client Secret into the Password section.

Once successfully connected, the data tables that are available to load into Power BI are now visible.

Overview of data tables

To load data into tables, follow these steps:

1. Select on Get Data

2. Select the LinkedIn Learning data connector

3. Then select the tables you would like to load or create

   

API endpoint documentation:

• LinkedIn Learning API documentation Located Here
• Account Popular Courses Located Here
• Account Popular Custom Content Located Here
• Account Total Activity Summary Located Here
• Content Summary Located Here
• Learner Activity Summary Located Here
• Learner Course Activity Detail Located Here
• Learner Custom Content Activity Detail Located Here
• Learner Detail Located Here
• Learner Summary Located Here
• Organization Summary Located Here

All data tables, except for Learning Assets, represent learner activity reports. The Learning Assets table provides our full catalog of courses, including all languages.

Despite the multiple possible queries available in the learning assets API endpoint, all assets are loaded with complete information. More details on the endpoint can be found in our API documentation. Located Here

Data refresh options

In Power BI, there are two main options to refresh data to ensure that the latest up-to-date information is available:

• On-Demand Refresh: Allows users to manually trigger a refresh of the dataset whenever they need to update the data. On-demand refresh can be done directly from the Power BI Service interface or through the Power BI Desktop application. On-demand refresh is useful when immediate access to the latest data is required, or when the data source doesn't support scheduled refreshes.

  

• Scheduled Refresh: This option allows you to set up a refresh schedule for your dataset in Power BI Service. You can define how often you want the data to be refreshed, such as daily, weekly, or even multiple times per day.

  A scheduled refresh can be configured to run at specific times or intervals to ensure that the data is always current.

  This option is suitable for regularly updated datasets where the data source supports scheduled refresh.

  

Data refresh feature in the published dataset

To update the data tables, you can either schedule a refresh or perform an on-demand refresh.

1. Select the dataset name you wish to refresh.

   

2. Select refresh option:

   1. Refresh now
   2. Schedule refresh

   

Incremental refresh (optional)

Incremental refresh improves update performance and offers several benefits. This refresh method works by defining a data window for data archival, incremental refresh, and real-time data (when supported).

Additional information on incremental refresh. Located Here

• Incremental updates are configured table by table and require editing the query of each table in Power BI Desktop.

Setting up incremental refresh

To set up the incremental refresh, follow these steps:

1. Right-click on the "Specific table name"

2. Select "Edit Query"

   

3. Select "Manage Parameters"

   

4. Set up the parameters for RangeStart and RangeEnd.

   1. Select "New"
   2. In the Name section, enter datetime parameter name e.g RangeEnd.
   3. Ensure the "Required" checkbox is ticked.
   4. Enter values as follows:

      1. Type: Date/Time

      2. Suggested Values: Any value

      3. Current Value: Present date

      4. Repeat the same to create the RangeStart parameter but ensure that Current Value is set to the date from which the data needs to be queried.

         

5. After creating the parameters, move them to the top of the query section.

6. Then, select on the table name and then within applied steps, select "Source".

   

7. In the source function, set the parameters as shown: "= LinkedInLearning.Contents(RangeStart, RangeEnd)"

   

8. Repeat the process for all data tables, ensuring that the same parameters (RangeStart and RangeEnd) are set for all sources within the applied steps section for each table.

9. Next, configure the date range filtering.

10. In each table or query select Navigation within the Applied Steps section.

    

11. Create a datetime filter.

    1. Select the dropdown arrow of the "Week column."
    2. Select "Date/Time Filters."
    3. Select "Custom Filter."
    4. Enter values as follows:

       1. Select Basic.

       2. Choose "is after or equal to".

       3. Ensure parameter is set, then select "RangeStart".

       4. Select And.

       5. Choose "is before".

       6. Ensure parameter is set, then select "RangeEnd".

          

12. Save and close the Power Query Editor. This process might take time depending on the number of tables and the date range.

13. The next step is to enable the incremental refresh for each table.

    1. Right-click on the "Specific table name".

    2. Select "Incremental Refresh".

    3. Select "Incrementally refresh this table".

    4. Configure based on preference.

    5. Select "Apply".

       

Data columns / JSON

The following columns are used by the data connector and shouldn't be deleted, as doing so might cause errors during data refresh:

• epoch week
• week
• parameters
• total_pages_week
• page_index
• param_w_start
• page_data.paging

Additionally, the column page_data.paging contains the paging information returned by the API.

If you encounter the "expand" symbol while editing a query, it indicates that the column can be expanded to show more rows.

Expanding the column adds a step in the query settings.

• Expanding columns can be done when needed, which duplicates rows.
• Expanding columns is important for displaying skills in learning assets and custom properties in learner detail reports.

Congratulations! Your learning admins can now gain key insights and engagement metrics through their Microsoft Power BI application.

Common issues and troubleshooting tips

If the data update fails when attempting to refresh data:

1. Select on the warning sign to see the details of the error message to diagnose issue.

2. Review the details of the error message. Reviewing this message helps identify and address any issues preventing the data refresh.

Frequently asked questions

For further information, review our LinkedIn Learning Reporting API FAQ.

Extra resources

• LinkedIn Learning API documentation Located Here
• Microsoft Power BI: Located Here

Disclaimer

LinkedIn Learning is constantly investing in our reporting infrastructure. In the future, there might be changes to the Reporting API that might require adjustments to how data is queried. These updates might require you to make some updates on your platform. We intend to provide detailed guidance in advance of any potential changes to ensure that you have enough time to make necessary changes in your application.