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Personalized Learning: The Data Deep Dive

In our previous blog post, we discussed the need to integrate data and functionality from multiple point solutions to deliver experiences to the workforce. In this post, we’ll go deeper on delivering a great learning and career development experience to employees and managers. Traditional learning management systems were good at making sure that employees and managers completed required training, which was usually in person. This is what HR needed, but it didn’t really address the full learning and development needs of the workforce. 

Today, we not only have multiple formal learning modalities, including in-person, virtual instructor-led, video, and self-paced, but we also need to include experiential and social learning. They are crucial for learning new jobs or roles. In addition, we need data about the skills people need and those they have. And we need to provide access to opportunities that let employees apply new skills, practice them, and grow.

Even though we have many amazing ways to deliver learning, choosing learning and career development opportunities that fit the needs of the individual worker has never been more complex. To cut through this complexity, we have to start with the right data and use that data to deliver a personalized learning experience.  

Personalized Learning Starts with the Right Data

Building a personalized learning and career experience for the worker requires a variety of different types of data, including:

  • Employment information: job role, organization, work location
  • Work experience: years of service or in job, previous employment history
  • Performance data: specific metrics, ratings, goal achievements
  • Talent profile information: current skills and levels of mastery
  • Personal interests: skill and career goals, desire to relocate
Data that Drives Personalized Learning

But, how do we get the right data and maintain it over time? Basic personal and employment information is maintained in the system of record, but other types of data require more work to find and keep current.

First, the system requires the worker to fill out a talent profile. Most organizations do not do a great job of leveraging all of the data they collected as part of the recruiting process to enrich the talent profile. Even fewer have good ways to keep the data in talent profiles fresh. According to Human Resource Executive, 75% to 80% of workers don’t complete their HR system’s talent profile. 

Many leverage third-party data sources like LinkedIn to make it easier, but the quality of that data can be suspect. And a growing source of insights exists outside of corporate systems altogether, in consumer learning apps like the Pluralsight Skill IQ, digital badges, micro-credentials, and in unstructured data on niche professional networks like GitHub. Today’s systems of record, which were all built primarily to standardize and automate HR processes, can’t possibly keep up.

So what does work? Providing an experience for the worker that not only enables enrichment of the talent profile, but provides value to the worker as they provide that data.

Let’s use the example of Laurie. She’s worked at Acme Corp for a little over a year in a customer service role.

Laurie gets a message asking her if she would do a quick self-assessment of her skills, so Acme can suggest learning and development opportunities for her when she has time.

Laurie answers yes and now she proceeds to answer a question about the five most important skills for her current customer service job. 

Based on the self-assessment, the system suggests personalized learning and development opportunities to help Laurie become better at her job. 

Collecting data in a way that works

This is a value exchange between Laurie and Acme. Laurie spent time between calls to self-assess her skills, which is valuable to Acme. And Acme provided value to her by suggesting learning and development that can help improve her performance. Using this type of approach to gathering data — asking for information and delivering value back — is critical to starting with the right data.

Using Data for Upskilling to Drive Career Growth

In our example, Laurie’s talent profile was enriched by doing a basic skills assessment. Now the system will use data to provide additional value to Laurie and, in turn, capture more data. In addition to suggesting personalized learning and development opportunities, a modern talent solution should use Laurie’s personal, employment, and skill data to make recommendations on possible future roles by comparing her data to other people in the organization that have similar backgrounds, experiences, and skills. Modern talent solutions use machine learning, or pattern recognition, to provide these kinds of personalized recommendations.

Let’s take it one step further and say that there are three future jobs or roles that are recommended to Laurie. Laurie can drill into more details about the jobs. She can find out more about the roles and responsibilities, the paths that others like her took to get to those future roles, coworkers who have experienced similar transitions and could be good  mentors, and more. The system can also ask another basic question at this point: “Are you interested in any of these future jobs/roles?” If there is an interest, that is data to be collected and used to continue to personalize the experience.

Let’s say that there was a future role Laurie was excited about. The system can continue to do more. For example, the system could ask Laurie to self-assess additional skills to suggest additional learning and development opportunities. This is a great example of upskilling. Those learning and development opportunities for Laurie could be specific training, but they also could be social: become a member of a specific community or subscribe to a specific learning content channel. Or they could be experiential: take on a gig that enables the worker to gain specific skills or work with someone who has gone down a similar path.

That’s a Wrap

This is the last post in our series. We started this series discussing designing for the workforce vs. HR. We laid out some of the fundamental differences between systems that were designed to automate HR processes vs. ones that were designed for the worker first. In the second blog post, we delved into designing for the workforce — if you truly do that, then systems of record are not the center of the universe (or your spaghetti diagram), they are really just another point solution that needs to be considered designing the experience. We also provided an example of a sample persona and journey to illustrate the point. Finally, in this blog post, we went a little bit deeper into using data to personalize the experience. We illustrated the symbiotic relationship between asking for a little data and providing value back before asking for a little more data to provide even more value back.

This is all possible today. Modern talent solutions are designed for the workforce instead of HR. They enable you to better leverage your solution portfolio to deliver better experiences to the workforce. They also leverage data to personalize and add value to the employee while gathering more data. If all your current talent solutions are only capturing data in forms and routing them for approvals online (or in your mobile device), then you are missing out on the art of the possible.

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