Our last blog post discussed how achieving a global view of your organization’s skills data is crucial in unlocking instant insights while being the bedrock for your future L&D Strategy. But to reap the long-term benefits, you need a reliable data foundation.
A comprehensive understanding of your skills data can be your secret weapon. It’s the fuel that powers your L&D strategy and holds tremendous potential for revealing instant, actionable insights. As a result, your L&D strategy can provide a sharper, more precise view of the skills across the organization.
But to truly unlock the full value of this data, you need to build on a solid, reliable foundation. Sadly, this is not a ‘once and done’ job. From standardized formatting, to ensuring skills assessments are accurate and up to date, there are a number of best practices you’ll need to get on top of.
Before we dig into the blog post, be sure to take a look at the introduction to our Skills Data and Analytics blog series. It offers a comprehensive overview and handy tips to benefit from this series.
The shrinking half-life of skills: Why L&D must keep up
With the rapid pace of industry innovation and evolving business needs, the value of job skills is diminishing at an unprecedented rate. According to the World Economic Forum, the half-life of a job skill is just about five years. A global view of skills health has never been more critical and is rapidly becoming a prominent CEO agenda item.
A comprehensive picture lets you gauge the skill health of individuals, teams, and the organization as a whole. You can identify the most essential skills, and you can track the proficiency and volume of these skills. By doing so, you can encourage and proliferate the skills needed to drive success.
But remember, to improve skill health, you need to foster a culture where employees are encouraged to self-rate their skills, submit to peer and manager reviews, and undergo training and on-the-job assessments. Employees can also see their own skills develop over time, which helps them better understand their existing skills and identify areas for improvement. As a result, they become more invested in their development.
A global view also helps you calibrate skills data. You can identify discrepancies and take corrective action by comparing self-rated skills to peer ratings or assessments. You can also determine your workforce’s skill volume and proficiency and track progress toward your goals.
Talent mobility is another area where skills data can be really useful. Using skills data, you can move existing employees to new roles or departments based on their skill sets. This approach eliminates the need to hire new people and enables you to leverage the skills of your existing workforce more effectively.
In other words, the insights you gain serve as the bedrock for effective learning and development strategies, a key factor in highlighting skills gaps, and the ability to reskill and upskill employees. You need to be able to see what skill sets are missing or underdeveloped if you want to stay competitive.
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Organizing and prioritizing skills data: How to focus on what matters most
Organizing and prioritizing skills data might seem daunting at first, but you can make the process manageable by breaking it into the following steps. Remember to start simple and add more layers as you progress.
- Set the foundation. Ensure everyone is on the same page by creating a skills library and establishing a common language for discussing skills within the organization.
- Define your objectives. Now it’s time to explain the “why”—the fundamental business reason for measuring job skills. For example, the main driver might be growth, market expansion, or new product or service offerings.
- Map skills to roles. Given the sheer volume of skills data, it’s essential to prioritize. So, once your business objectives are in place, you should start mapping skills to the needed job roles. This means identifying the skills tied to the most critical business goals and focusing on these first.
You may find this blog from our sister company Bridge insightful: How to Build a Job Architecture With AI-Assisted Skills Matching
What’s the best way to use and maintain all my skills data?
Maintaining organizational skills data can be complex, especially when managing a diverse learning ecosystem. So, after gathering all the necessary information from all your data sources, it is essential to have a solid plan to ensure the data remains accurate and current.
One of the best practices for maintaining your data is establishing a centralized database or system (e.g., learning record store) where you can store and access all the information. This database should also allow easy updates, additions, and modifications as needed.
When adding new learning systems, training content, or assessments, it is crucial to have a process in place for integrating them into the existing learning ecosystem. This process could involve:
- mapping the new data fields to the existing ones,
- ensuring data is stored in a standardized format, and
- conducting regular audits to identify any inconsistencies or gaps in the data.
You should also communicate any changes or updates to the relevant stakeholders so they can stay informed and make necessary adjustments.
Regular data maintenance is also crucial. This process involves regular checks and updates to ensure accurate, relevant information. For instance, you might:
- verify employee profiles’ accuracy,
- update skill levels based on training completion or performance evaluations, and
- identify any data gaps that need to be addressed.
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How often should I reassess employees' skill levels? What are some best practices?
Assessing employees’ skill levels is essential to an organization’s success. You can identify gaps in knowledge or areas for improvement, ensuring that everyone has the necessary skills to excel in their roles. But how often should you reassess skill levels?
The answer to this question may vary depending on several factors, such as the nature of the job and the pace at which new skills are required. However, a general rule of thumb is to conduct skill assessments annually or biannually. This frequency gives you enough time to observe and evaluate any changes in your employees’ skill sets while also allowing them enough time to develop and demonstrate new abilities.
Some best practices can help ensure a smooth, effective process when reassessing employees' skill levels. First, establish clear expectations and criteria for skill assessment. By clearly defining each role’s skills requirements and the expected proficiency levels, everyone has a clear roadmap for skill development. This clarity also helps you assess progress accurately against predetermined benchmarks.
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Next, consider using a combination of self-assessment and manager evaluation for skill assessments. Self-assessment enables employees to reflect on their strengths and weaknesses and take ownership of their skills development.
On the other hand, manager evaluation provides an objective perspective and allows for a more comprehensive assessment of an employee’s skills. Combining both methods gives you a more holistic view of your employees’ skill levels.
Another best practice is to provide ongoing feedback and support throughout the year. Skill development is not a one-time event but rather a continuous process. You can help employees stay on track with their skill development goals by offering regular feedback and coaching sessions. This approach also allows for timely interventions if you identify any skill gaps during the year.
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Up Next: How to assess skills & competencies for organizational success
Our next post in this series explains why regular skill assessments are vital to an organization’s success and should be conducted at least annually.
By following best practices such as establishing clear expectations, using a combination of self-assessment and manager evaluation, providing ongoing feedback, and ensuring transparency in the process, you can ensure employees have the necessary skills to excel in their roles.
About the author
Brandon has spent the past 4 years demonstrating the accessibility and power of learning analytics. He works with organizations that are in the beginning stages of their learning analytics journeys—bringing their L&D data stories to life while identifying areas for growth to help ensure future success.
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