Multiple formal learning modalities, including in-person, virtual instructor-led, video, and self-paced, exist today, but we also need to include experiential and social learning. In addition, data about the skills people need and those they have is necessary. Learning professionals must provide access to opportunities that let employees apply new skills, practice them, and grow.
Even though there are many amazing ways to deliver learning, choosing learning and career development opportunities that fit workers’ needs has never been more complex. To cut through this complexity, we must start with the right data and use it to deliver personalized experiences.
Starting With the Right Data
Making the learning and career experience more personal and relevant to the worker requires various types of data, including:
-Employment information—job role, organization, work location
-Work experience—years of service or 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
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 the data they collect as part of the recruiting process to enrich the talent profile. Even fewer have good ways to keep the data in talent profiles fresh.
Many organizations leverage third-party data sources like LinkedIn to make it easier, but the quality of that data can be suspect. A growing source of insights exists outside corporate systems in consumer learning apps (such as the Pluralsight Skill IQ), digital badges, microcredentials, and in unstructured data on niche professional networks like GitHub. Today’s systems of record, which were built primarily to standardize and automate HR processes, can’t possibly keep up.
Instead, organizations should provide an experience for the worker that enables enrichment of the talent profile and adds value to the worker as they share that data.
Let’s use the example of Laurie, who’s worked at Acme Corp for a little more than a year in a customer service role.
Laurie gets a message asking her to do a quick self-assessment of her skills so Acme can suggest learning and development opportunities for her to do when she has time. Laurie answers yes, then 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 learning and development opportunities to help Laurie become better at her job.
Using Data for Upskilling to Drive Career Growth
Let’s take it one step further and say that three future jobs or roles are recommended to Laurie. She can use the system to discover more details about those jobs. She can find out more about the roles and responsibilities, the paths that others like her took to get to those future roles, co-workers who have experienced similar transitions and could be good mentors, and more.
If there were a future role that excited Laurie, the system could continue to do more. For example, it could ask her to self-assess additional skills so it could suggest additional learning and development opportunities. Those suggested learning and development opportunities could be specific training or they could be social, such as becoming a member of a specific community or subscribing to a specific learning content channel. Or they could be experiential: take on a gig that enables her to gain specific skills or work with someone who has forged a similar path.
This content was written in partnership with Leapgen.