Applying data analysis for optimization of learning plans

Data analysis for learning plans: from numbers to concrete development

The HR department sits on a goldmine of data. Learning outcomes, training participation, competency assessments, feedback from evaluations. Yet many organizations continue to develop learning plans based on intuition and experience. While the numbers can tell a completely different story. Data analysis for learning plans isn’t about complicated dashboards or complex statistics. It’s about smartly deploying available information to make development trajectories more effective. To see what works, what doesn’t work, and where your investment yields the greatest return.

Why data is becoming more important for L&D now

The expectations of learning and development have changed. CFOs want to know what those training budgets deliver. Managers are looking for targeted interventions instead of one-size-fits-all programs. And employees expect development that aligns with their specific needs. Traditional learning plans are often developed from a supply perspective. An external party has an interesting program, so we’re going to roll it out. Or there’s a new policy, so everyone gets training. The question of whether this aligns with actual development needs often remains unanswered. Data analysis reverses this logic. You start with concrete questions: What challenges are employees facing? Which competencies make the difference in performance? Where do we see knowledge gaps that impact results? You find the answers in the data your organization already collects.

The four pillars of data analysis for learning

Effective data analysis for learning plans rests on four foundations that together provide a complete picture. The first pillar is descriptive analysis. This is the basic level: what’s happening now? How many employees have completed which trainings? What are the average test scores? How high is the completion rate of e-learning modules? These numbers give you an objective overview of the current situation. Explanatory analysis goes a step further and asks why. Why do some teams score better on certain competencies? Why is the dropout rate higher for certain trainings? By looking for patterns in your data, you discover underlying causes. Perhaps it turns out that trainings on Friday afternoons consistently get lower satisfaction scores, or that certain prior knowledge is crucial for the success of a follow-up course. The third pillar is predictive analysis. Here you use historical data to predict future outcomes. Which employees have the greatest risk of dropping out during an intensive development trajectory? Which combination of trainings leads to the best performance outcomes? These insights help you steer proactively. Prescriptive analysis is the most advanced level. This provides concrete recommendations: what should you do? Based on all available data, you get suggestions for optimal learning trajectories per employee or job group. Think of adaptive learning paths that automatically adjust based on progress and results.

From data to insight: the analysis plan

A structured approach prevents you from drowning in numbers without concrete next steps. A good data analysis plan for learning plans follows a clear route. Start by defining your question. What exactly do you want to know? “We want better trainings” is too vague. “We want to know which elements of our leadership program lead to measurable behavioral change within three months” is concrete and measurable. Next, identify which data sources you need. Think of LMS data on training participation and results, performance data from appraisal conversations, feedback from employee surveys, and possibly even business figures like customer satisfaction or productivity numbers. At Deepler, we regularly see that organizations are surprised by the wealth of usable data they already collect. The next step is actually collecting and cleaning data. This sounds boring, but is crucial. Incomplete records, different naming conventions between systems, or outdated information can completely distort your analysis. Invest the time here. Then comes the analysis itself. Depending on your question and available expertise, this can range from simple Excel analyses to more advanced statistical methods. The goal is always the same: discover patterns that lead to better decisions. Translate your findings into concrete insights that are relevant for decision-makers. A graph with correlations says little, but “employees who complete training X before training Y score 23% higher on the final test and apply the knowledge faster in practice” provides direction. Based on your insights, formulate concrete recommendations for optimizing learning plans. Be specific: which trainings need adjustment, which sequence is optimal, where is extra guidance needed? The final step is monitoring and adjustment. Implement your improvements and measure again. Data analysis for learning isn’t a one-time project but a continuous process of improvement.

Practical example: optimizing a learning trajectory with data

A medium-sized organization had an extensive onboarding program for new employees. The program cost a lot of time and money, but nobody knew exactly what it delivered. By combining different data sources, an illuminating picture emerged. LMS data showed that certain modules consistently had low scores. Performance data revealed that employees who had skipped these modules didn’t receive worse evaluations. Retention data showed that employees who did receive intensive guidance on specific components stayed significantly longer. The conclusion: three modules could be eliminated, two needed to be completely redesigned, and for one component personal coaching was essential. The result was a shorter, more effective program with 30% lower costs and higher satisfaction scores.

The role of the data optimization analyst

Some organizations choose to create a specialized role: the data optimization analyst for L&D. This professional combines knowledge of learning and development with analytical skills. The analyst collects and integrates data from different sources, performs analyses, and translates findings into concrete improvement proposals. But more importantly: this person functions as a bridge between the numbers and practice. They help HR professionals and managers make data-driven decisions without everyone having to take a statistics course. For many organizations, a full-time analyst isn’t feasible. Then this role can be combined with other HR analytics tasks, or you can work with external expertise that periodically contributes to optimization.

Common pitfalls and how to avoid them

The biggest pitfall is collecting data without a clear purpose. You build impressive dashboards that nobody uses because they don’t answer concrete questions. Always start with the question of what you want to know, not with what you can measure. A second risk is tunnel vision on quantitative data. Numbers tell an important part of the story, but not everything. Combine hard data with qualitative input from conversations and observations. A training can receive high satisfaction scores but still not achieve behavioral change. Many organizations underestimate the privacy aspects. When analyzing individual learning data, you must handle personal information carefully. Be transparent about what you measure and why, and ensure adequate security. Finally: analysis paralysis. You keep collecting and analyzing data without ever taking action. Perfect data doesn’t exist. At some point you need to make decisions with the information that’s available, implement, and then optimize further.

Technology as enabler, not as goal

You don’t need advanced AI systems to start with data analysis for learning plans. Begin with the tools you already have: your LMS, your HRIS, perhaps a spreadsheet. As you mature in data-driven work, you can invest in integrated systems that automatically connect learning data with business results. Platforms that build predictive models or generate personalized learning trajectories. But that investment is only valuable if you have the basics in order. At Deepler, we see that organizations that are successful in data-driven L&D don’t necessarily have the most advanced technology. They do have a culture where decisions are systematically tested against facts, where experimentation is encouraged, and where lessons quickly lead to adjustments.

From insight to impact

Data analysis for learning plans isn’t a goal in itself. It’s about better development of employees, higher performance, and ultimately better business results. The organizations that are successful in this treat their learning data with the same professionalism as their financial figures or customer data. They invest in the right competencies, create clear processes, and remain critical about what works and what doesn’t. The first step is often the most important: choose one concrete learning trajectory or program that you have doubts about. Formulate a clear question. Collect the relevant data. Analyze what you see. And adjust based on your findings. That one optimization not only delivers a better program, but also valuable experience with data-driven work. Experience that you can expand to other parts of your L&D strategy. This way you build step by step an organization that learns from its data, and therefore learns more effectively.