Need to Know Who Your High-Potentials Are? Look to Your Learning Data.
Resumes and performance reviews don't capture all the information you need to make the best workforce transformation decisions.
When evaluating whether someone is right for a role, decision-makers have traditionally focused on historical data sources like resumes, performance appraisals, and past projects. The idea is that records of past performance are reliable indicators of future performance.
Yet past performance is far from foolproof. We've all heard stories of — or maybe even worked for — people who excelled as individual contributors, got promoted because of it, and struggled as managers.
Moving to a new role, no matter how similar it is to a previous one, will always require some new skills, even if it's just adapting to the cultural dynamics of a new team. Yet the data points we rely on to assess employees are often backward-looking. They may give us a general idea of how an employee will behave — e.g., someone with a great work ethic will bring that to every role — but they don't tell us whether the employee has all the required skills to succeed.
And in the era of workforce transformation, talent leaders need forward-looking data points more than ever. After all, the crux of workforce transformation is to turn the workforce you have into the workforce you need through upskilling and reskilling. You are, in short, changing employees' skill sets to prepare them for brand-new roles and challenges. That means records of past performance are not enough on their own to gauge which employees have the highest potential to thrive in the workplace of tomorrow.
If you want to identify your top employees and arm them with the skills they need to unleash their full potential for the benefit of your organization, you need to add some new information to your data pool. Specifically, you need to start looking at learning data.
What Makes a High-Potential Employee?
Before diving into why learning data matters, we must define a "high-potential employee." What distinguishing characteristics separate them from the rest?
Each organization will likely have some unique criteria that reflect its company culture and particular way of doing business. Still, there are some universal qualities that all high-potentials share: intent to achieve, a demonstrated capability to perform, and mastery of relevant skills.
Put another way, if you want to know whether a particular employee has high potential, you need to answer these questions about them:
- Do they have the intent to be a top performer? Do they show a passion for continuous improvement? Are they internally motivated to succeed?
- Have they demonstrated that they have the skills they need to excel in a new role?
- Have they achieved a level of mastery over their skills? What evidence do they have to prove this mastery?
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How Traditional Data Sources Fall Short
We don't typically assess employee potential in a vacuum. Instead, we have a particular context in mind. We want to know if an employee has the potential to excel in a specific role or career path.
If the role is very close to the employee's current job — say, the move from junior developer to senior developer — then backward-looking data sources like resumes and performance reviews may tell us most of what we need to know.
Even then, we're probably missing some vital information. "Junior developer" and "senior developer" are different roles because they require at least slightly different skill sets. Historical data about an employee's performance as a junior developer may not tell us whether they've mastered those skills specific to the senior developer role. In matters of workforce transformation, there's often an even more significant difference between an employee's current and new roles. In a previous article, we used the example of transforming software engineers into data scientists. The two jobs share some essential skills, but employees still need extensive training to develop the skills they're missing. In this case, records of an employee's performance as a software engineer simply couldn't paint the whole picture needed to determine their potential as a data scientist.
Past performance data can have other shortcomings, too. It doesn’t always account for any training a worker underwent in their last role or any new skills they picked up as a result. Additionally, the performance reviews from which past performance data is drawn are typically carried out by human assessors. Those assessors’ unconscious biases can influence how they evaluate employees, leading to less-than-totally-objective performance data.
Furthermore, relying solely on backward-looking data can encourage talent leaders to develop some damaging misconceptions about employee development. The first is that long tenure correlates with advanced skills. Sometimes that may be true, but people don't automatically learn new things or improve their skills simply by sitting in the same job for a long time. They need to put in an active effort.
The second, and even more dangerous, misconception is that a person's current job predicts the limits of their capabilities — that, for example, someone with a background in HR only has the potential to excel in other HR roles. Such a thought process is incompatible with successful workforce transformation efforts, which require creativity, flexibility, and the ability to see connections between seemingly disparate career paths.
So, if records of past performance present an incomplete picture of employee potential, let's look at how adding learning data can help organizations make more intelligent workforce planning and development choices.
How Role-Based Learning Data Helps Identify High-Potential Employees
Upskilling and reskilling are the heart of workforce transformation. They're also processes that traditional data sources may fail to capture meaningfully. That's why talent leaders should incorporate data generated by the learning process into their analysis when assessing employees and making strategic workforce transformation decisions.
By considering learning data and past performance data in tandem, organizations can more fully and accurately understand employees’ skill sets. For example, a resume can list a person's certifications, and learning data can paint a picture of how those certifications were earned. The process of learning can be just as valuable as the outcomes when weighing employee potential.
And while data from any learning program can be useful, data from role-based learning initiatives can be especially valuable in evaluating employees.
As we've covered before, role-based learning is an approach to employee development where learning paths are organized by role, and employees are connected to learning opportunities based on their current roles and the roles they aspire to.
Role-based learning paths are designed to equip employees with the skills they need to excel in specific roles. So, when assessing whether an employee has the potential to succeed in a particular position, we can use their activity in a corresponding role-based learning path to answer the key questions:
1. Does the employee have the intent to be a top performer? Do they show a passion for continuous improvement? Are they internally motivated to succeed?
There is no clearer illustration of a person's intent to excel in a role than participating in a learning path designed specifically for that role. The fact that an employee is spending time on training content signals their motivation.
Furthermore, we can look at how much time an employee has spent on learning — how many minutes of content they've consumed — to help quantify their passion.
2. Has the employee demonstrated that they have the skills they need to excel in a new role?
Role-based learning paths include practical, hands-on labs and exercises where learners apply their new skills to real-world scenarios. Such active learning is better for retention, and the activities offer critical insights into how employees wield the skills they'll need for their new roles.
3. Has the employee achieved a level of mastery over their skills? What evidence do they have to prove this mastery?
The mere act of completing a role-based learning path can serve as proof of mastery in itself, as doing so requires engaging with a substantial body of knowledge and passing the requisite assessments. But learners can also earn Digital Badges or parlay their new skills and knowledge into formal certifications from authoritative bodies, adding even further proof of mastery.
Ultimately, incorporating learning data into your process for evaluating employee potential offers two benefits. First, it provides a complete picture of the employee's upskilling or reskilling journey, which is critical in assessing their ability to take on a new role.
Second, learning data enables a more agile and effective workforce transformation strategy. Records of past performance, by their nature, cannot tell us how employees will rise to new challenges they've never seen before. Conversely, role-based learning explicitly helps employees cultivate the skills they need to take on new roles. Data from these learning programs helps talent leaders identify employees who have the highest potential to thrive in the workplace of the future.
Do You Have the Data You Need to Identify High-Potential Employees?
If you're upskilling and reskilling your workforce, the process you're following to provide those additional skills matters. Learning data should inform your workforce transformation efforts.
It's worth taking some time to reflect on the data your organization currently uses to evaluate employee potential. Look at what you're collecting and how you're collecting it. Does the data pool include data from learning programs? If not, it's time to add that data.
That said, learning data is only helpful if you can easily plug that data into your HRIS or another central hub, where you can use the data alongside more traditional sources of information to make the smartest decisions about workforce planning and development. Toward that, you'll need a learning platform with powerful reporting capabilities that integrates with your existing workforce data solution.