From Data to Action: How to Access & Analyze Organizational Skills Data

The skills shortage clock is ticking

We’re facing a massive global skills shortage. A daunting prediction from Korn Ferry’s research suggests a seismic talent deficit to the tune of 85.2 million workers by 2030. That’s a staggering number, and the implications are stark—companies that fail to address this impending crisis could struggle to compete, innovate, or even maintain operations.

So, what’s the solution? It’s time for companies to upskill and reskill their workforce proactively. This isn’t just about surviving; it’s about thriving in a world of constantly evolving business conditions.

However, before you begin this journey, you must first have a thorough understanding of your current skills data. But before you start implementing training programs and workshops, there’s one critical step to take—you need to have all your skills data in hand. Gathering this data is your starting point.

This process involves identifying the current skills within your organization, determining where the gaps lie, and understanding how they align with your future business objectives.

Think of it as building a house. You wouldn’t start without a blueprint, right? Similarly, don’t dive into upskilling and reskilling without first understanding what skills are already in place and what new ones need to be developed.

In other words, to tackle this impending global skills shortage, we need to start by understanding what we have and lack. And from there, we can build a strategic roadmap toward upskilling and reskilling our workforce for the future.

Navigating your learning ecosystem: How to discover skills data

Skills data encapsulates a wealth of information about someone’s professional abilities, accomplishments, certifications, and work history. We can gain valuable insights into these capabilities and competencies by analyzing this data. But first, you need to know where all that data lives and how to collect it.

And just like learning isn’t limited to one place, skills data isn’t confined to a single location. You can find skills data in systems and platforms that store information on people's professional skills, competencies, certifications, and work experience.

Common examples include learning management systems (LMS), human resource information systems (HRIS), and learning experience platforms (LXP). Some organizations may also use:

• badging software,
• coaching platforms,
• observation checklists,
• talent management systems, and
• internal databases and spreadsheets.

How to aggregate and augment skills data from multiple systems

So, as you might imagine, identifying and collecting all that data across your learning ecosystem and beyond might seem overwhelming. So we’ve broken down a typical data collection process into these steps:

1. Extract data from multiple sources. Many L&D platforms—such as an LMS or LXP—have built-in reporting features, which you can use to extract data related to course completion, skills acquisition, and individual employee progress. The way in which skills data is extracted will vary according to each data source. Watershed connections to data sources typically take place via standardized APIs (e.g., xAPI), custom APIs, or via CSVs.
2. Transform data into a consistent format (e.g., xAPI). Data collected from multiple sources will typically have different fields and information. For example, some platforms can report on how much time someone spent completing an activity, but others do not. Some platforms provide more comprehensive metadata about courses, such as the instructor, while others do not. And because of these differences, you may need to convert, augment, or cleanse your data so it's all in a consistent and reportable format.
3. Load it into a central database or data warehouse (e.g., learning record store). From here, use a learning analytics platform and reporting tools to analyze data, identify trends, and make informed decisions about your organization's professional development and talent management needs.

Getting the balance right: How to weight your skills data

When it comes to utilizing skills data from different learning platforms for a particular topic (e.g., Python), it’s important to consider several factors.

First, assess the credibility and reputation of each platform. Second, evaluate the amount and relevance of content. Quality learning programs and pathways should cover foundational knowledge and delve into more advanced topics.

Remember, the “best” data sources depend on your learning objectives and the skills you want employees to acquire or improve. That’s why reviewing the training content, checking user reviews, and trying out a few sample courses are beneficial when determining which platform's skills data to use.

Aligning skills, learning, and HRIS data for enhanced performance

Once you’ve aggregated your data into a central location, it’s time to align it with learning and HRIS data so you can start building a more competent, efficient workforce.

For instance, you can identify skills gaps, drive targeted learning interventions, and monitor the effectiveness of these interventions. Here are a few ways you can better align your data:

Grouping and segmenting learners

One of the ways you can align skills data with learning data and HRIS data is by grouping and segmenting learners. The HRIS contains valuable information about learners—such as job titles, demographics, and employment history.

Integrating this data with learning data lets you categorize learners based on their roles, skill levels, or other relevant criteria, creating a more targeted and effective learning program.

This process also enables you to filter by organizational hierarchy, which is key for getting meaningful insights. Learning leaders can view reports globally or by region, department, or job role.

Line managers can have restricted access to their team and direct reports only. This means everyone gets to view the data that is meaningful to them while ensuring data is handled ethically and meets regulatory and security requirements.

Contextual learning

Context-based learning is another way to align these three data types. Understanding employees' concerns and training needs can help you design a more relevant upskilling program.

Skills data can provide insights into the areas where employees need enhancement, and HRIS data can provide the context of their current roles and responsibilities. This way, you can tailor the learning program to bridge the gap between existing and required skills.

Performance objectives

You can align your data by setting clear performance objectives for the learning program. You can derive these objectives from your business goals and HRIS data (e.g., employees' performance history).

By mapping these objectives with the skills data, you can measure the learning program's efficiency, effectiveness, and impact, ensuring it aligns with the business priorities.

Supercharge organizational skills with data

Skills data provides valuable insights, which you can use to develop your team effectively and achieve tangible outcomes—leading to improved performance, higher job satisfaction, and lower turnover.

• Identify skill gaps. By analyzing skills data, you can identify the gaps between current employee skills and those required for their roles. Use these insights to design targeted training programs to bridge these gaps.
• Personalize learning programs. Skills data can help you create personalized learning programs. By understanding each employee's strengths and areas for improvement, you can assign specific courses or training to help enhance their skills.
• Monitor performance. You can use skills data to monitor employees' progress post-training. Use this information to assess a learning program’s effectiveness and make necessary adjustments.
• Strengthen career development planning. By understanding an employee's skills and those needed for advancement, you can guide learners on the right path for career growth.
• Enhance succession planning. Use skills data to identify potential leaders within a team for effective succession planning. You can then invest in training and development for these high-potential individuals to prepare them for future leadership roles.

Up Next: How to get a global view of skills data for upskilling and reskilling

Skills data is a powerful resource that, when used effectively, can transform your organization’s L&D outcomes. You can create a more competent and efficient workforce by accessing, analyzing, and applying this data strategically.

And once you have all that data in place, it’s time to step back and look at what skills exist and which ones are missing. Join us for the next blog post in our series as we discuss how you can use your skills data to develop plans to reskill, upskill, and hire employees strategically.