One of the key benefits of a learning analytics platform is the ability to easily combine your organizational data with your learning data so you can filter by and compare different organizational units. This combination of data also means you can give operational managers access to reports, using data permissions to ensure they only see data about their own people.
These learning analytics features depend on having organizational data imported into the learning analytics platform from the HRIS system. This post discusses the data requirements for HRIS tools, and offers advice on how best to get the data out.
What’s an HRIS, and how does it typically fit into a learning ecosystem?
HRIS stands for human resources information system, and it contains information about the people in your organization that’s relevant for HR processes.
The HRIS is usually connected to most systems in the learning ecosystem to provide information about learners so logins for that system can be set up for each learner. Sometimes that connection is direct, and other times HRIS data might be passed via another system (such as the LMS) in the ecosystem.
What kind of data does an HRIS store?
HRIS data includes information such as demographics, contact information, medical information, pay, tax and pension details, employment history, and any other information the organization holds about its people. Most of this information is not relevant from a learning analytics perspective, but the following three categories of data are typically imported into a learning analytics platform (LAP):
- The learner’s name and identifiers. This is essential to map data from other systems to uniquely match the actions taken against the individual.
- Employment status, start date, and termination date. This is useful particularly when analyzing onboarding training or looking at the impact of training on retention.
- Job title, manager, and organizational hierarchy data. This is useful for filtering and organizing reports based on certain parts of the organization or to support report drill-down.
Here’s an example of an organizational hierarchy visualized in Watershed. (Most real organizational hierarchies imported from HRIS are considerably more complex!)
HRIS Data and Your Learning Ecosystem
What does HRIS data tell me? What are the benefits of combining this data with other tools and systems in my learning ecosystem?
Human resource information system data is used to group and segment learners. This data can then be mapped to learning activity data using identifiers from your learning platforms, so you can group and segment that activity data too.
This lets you build up a full profile of the learner’s actions, whatever system or platform they have used. It also allows you to group and segment your reports to find detailed insights, such as comparing how one department uses an LXP compared to another department.
Grouping and segmenting activity data based on HRIS organizational hierarchy and manager relationships helps with reporting in three ways:
- Controlling data permissions. For example, a manager may only be allowed to see data about people she manages.
- Filtering data. For example, a manager responsible for multiple territories may want to look at a specific region.
- Comparison. For example, you might want to compare people with different jobs.
These permissions, filtering, and comparison use cases are relevant to all kinds of reports using various data from platforms in the ecosystem.
For example, the sales dashboard shown above is permission restricted, so a manager only sees data relating to people on their team.
What about future changes to the ecosystem—how will adding, replacing, or removing specific tools affect HRIS data?
When you add a new platform to the ecosystem, it is important that you include that platform’s primary learner identifier in the HR data. Or, even better, the new platform should use an existing identifier in the HR system. This step is important for mapping data about learners across all the systems they use.
How do I extract data from the HRIS?
When setting up a data integration between the HRIS and your LAP, you may be able to use an existing scheduled data import to the LMS. As they both have similar data requirements, the file designed for import into the LMS may contain all the data required for the LAP.
This process may mean exporting the data from the LMS, or just using the same file coming from the HRIS. When defining requirements for the file, the most important things are to ensure that the file contains all the:
- identifiers needed to map a learner across multiple systems, and
- relevant organizational hierarchy data required to organize and filter reports.
The process of importing HRIS data into an LAP may reveal some errors in the data that need to be corrected.
For example, you can use Watershed to build a permissions hierarchy based on a chain of line managers. In this scenario, Alice manages Bob, who manages Charlie. As a result, Alice can see Charlie’s and Bob’s respective data, even though Charlie’s HR record doesn’t mention Alice.
TOP TIP: We sometimes find that errors in the data can create a line management loop, such as people who are listed as their own managers or have their secretaries listed as their managers. Other times, there are gaps in the line management chain (perhaps where a manager has left and a subordinate’s record is left outdated), leading to orphaned branches of the hierarchy.
Going through this process enables the organization to clean up the data, which will benefit all systems that use it.
The Role of HRIS Data in Compliance and Skills
Is an HRIS a good system of record for compliance requirements?
Compliance reporting that looks at course completions involves comparing a list of people required to finish training with a list of people who have finished it. As a result, you can see who is done and who still needs to complete that training.
Typically, HRIS data is the basis for the list of people required to complete training. For example, people who recently joined the organization may be required to finish onboarding training, or people with a particular job may need to complete role-specific training.
HRIS data also can help segment compliance data. For example, a manager may want to see who has or has not completed the required training, or a senior manager may wish to compare different teams’ compliance levels.
How does a learning analytics platform help with skills data generated in an HRIS?
Similarly for skills reporting, HRIS data also is beneficial for segmenting. For instance, a manager can see team members’ skills, and a senior manager can look at the distribution of skills across the organization.
Skills data also may be particularly helpful for comparing job roles—especially when you’re recruiting for a particular position. You might find some unexpected insights regarding the skills held by people already in that role.
Up Next: L&D Data Requirements for BI Tools
The combination of data from your learning ecosystem with organizational data from the HRIS can be very powerful. It’s no wonder then, that in addition to using reports and dashboards in the learning analytics platform, some organizations also want to export the processed data into a BI tool.
At the same time, BI tool data can also be useful for learning analytics. In our next post, we explore the data requirements for two-way transfer between the BI tool and LAP.
About the author
As one of the authors of xAPI, Andrew Downes has years of expertise in data-driven learning design. With a background in instructional design and development, he’s well versed in creating learning experiences and platforms in corporate and academic environments.
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