Learning and Development Analytics: For Beginners to Experts

learning and development data and analytics

Data is to a technology-driven society what dust is to the air. It’s ubiquitous. It’s huge beyond comprehension–and constantly changing. 

In order to manage the overwhelming amount of data generated every minute, the science of data analytics was born. In general, data analytics is the qualitative and quantitative process of clarifying data sets in order to discover the insights hidden in their patterns, relationships and trends. 

Although finance and accounting are typically the focus of intense data analysis that generates KPIs like ROI, IRR, and EBITDA, using data analytics in learning and development is no longer the exception. Thanks to software tools that gather, sort, and analyze data quickly and efficiently, increasing numbers of L&D professionals are using learning and development data analytics to assist them in planning, completing, and evaluating their entire training program. 

How does analytics help improve learning and development?

Over a decade ago, The International Conference of Learning Analytics and Knowledge defined learning analytics as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” 

This broad definition: 

1) Covers every aspect of learning and development and; 

2) Emphasizes using the data to understand and continually improve organizational training. 

In this context, then, data analytics for L&D: 

Provides a tool to quantify the value and effectiveness of L&D activities

Virtually every aspect of learning and development creates data or relies upon existing data. So, mastering data analytics as it applies to L&D overall just makes sense. 

Take ROI, for example. That performance indicator has long been one of the keys to determining how a company is faring. It’s also a critical factor in budgeting. Other things equal, the division, sector, or team that performs the best gets a bigger slice of the budgetary pie. 

In addition to providing insights about the overall L&D activities, data analytics helps identify which aspects of L&D are the most effective, which need bolstering, and which should be scrapped. 

By carefully analyzing training data, training professionals can answer questions about the number and type of intake requests, the number of training projects at any point in time, and the budgetary details of each one.  Armed with operational insights like this, L&D leaders know how to optimize their mix of services and provide what suits their learners and their organization best. 

Prepares the L&D team to enhance their performance 

A survey by the Learning and Performance Institute revealed that learning and development professionals admit to having low confidence and little expertise with data analytics. At the same time, the “Great Resignation” prompted by the pandemic has resulted in a surge in the need for reskilling or upskilling, even among L&D professionals. 

Proficiency with L&D data analytics helps learning professionals understand their roles better while providing insights in how they can optimize their training services to others.

What are the areas where analytics can be applied in training and development? 

Think through the range of activities involved in training and development:

  • Gathering and addressing training requests
  • Assessing the gaps in knowledge and/or skills
  • Planning and developing courses and activities that match learner needs and corporate goals
  • Tracking learner progress before, during and after training.
  • Gleaning insights from post-training interviews, evaluations, or observation of learners

All of these activities generate many points of data, even in a small company. All of those bits of data mean something on their own. In the aggregate, they tell a story of: 

  • What happened (descriptive analytics), 
  • Why it happened (diagnostic analytics),
  • What’s likely to happen (predictive analytics) and; 
  • What should be done (prescriptive analytics). 

Now ask yourself if any aspect of learning and development wouldn’t benefit if some person or some software program carefully analyzed the applicable data. So, then, the answer to this question is obvious: data analytics could be applied to every aspect of training and development. 

What types of data can L&D leverage for their training analytics?

Today, much of the vast amount of data that’s generated can be stored and then accessed relatively easily when needed, thanks to advances in software, hardware, and cloud technology. 

So, a key question for L&D to answer is, “What types of data are most useful?” 

Starting from the broad, overall perspective and narrowing to specific data related to L&D training initiatives, training personnel will benefit by using all of these types of data:

Business data

Business data relates learning and development functions to the guiding objectives of the company. For instance, L&D team members can analyze business data to discover: 

  • Whether sales training resulted in a corresponding increase in sales overall.
  • If sales people who received training performed better after the training that they did before it. 
  • How much the training and development activities contributed to the company’s net profit, ROI, or other KPIs. 
  • If learning is impacting productivity overall or at the project or team level.

Learning operations data

Operational data tells the story of the daily workings of the L&D team. Gathered and leveraged correctly, it can provide invaluable insights into the productivity of your department. 

These insights can enable L&D to examine how well resources are being allocated and used. Critically, they can also provide back-up for learning leaders who are in the position of requesting more budget to keep up with training demand—or even defending their existing budget.

Some examples of learning operations data points that can be highly valuable are:

  • Which types of training get requested most often? Least often?
  • What percentage of training requests gets approved?
  • How long does the approval take?
  • Which departments request training most often?
  • Are the trainers sufficiently trained before they train others?
  • Is L&D operating within budget?
  • Are training programs prepared on schedule? 

HR data

HR data is often more readily available to learning and development than data from other areas of the business. In the overall story of talent development and performance improvement at the employee level, it can provide extra context on the impact of training initiatives.

  • What are the hiring and retention rates across the company?
  • Is there a positive correlation between training and retention rate?
  • What is the level of employee engagement?
  • Have hiring and retention rates been affected by the Great Resignation?
  • What learning gaps are most common?
  • What skill gaps are most common? 

Learning data

Instructional designers, learning experience designers, and any team members involved in the creation of training materials need regular feedback. Without it, they will struggle to measure how successful their output is at changing the behaviour, knowledge, and skill level of employees.

  • What’s the training intake rate?
  • What’s the training completion rate, especially for self-guided programs?
  • What percentage of learners pass the training?
  • What remedial training is offered to those who don’t pass?
  • How much time is being spent on training?
  • How is learning retention measured?
  • What types of learning yield the highest knowledge retention rates?
  • What types of learning receive the highest approval marks from learners?
  • What type of correlation exists between the training that learners appreciate the most and the retention rate of that training?
  • What types of learning delivery do learners prefer–video, podcast, classroom setting?
  • How are ongoing skill assessments performed?

How do you implement analytics in training and development?

How do you move from realizing the benefits of using analytics in training and development  to actually using them? 

How to gather and analyze learning and development data

Generating data is easy; gathering it well requires planning and follow-through. You could meticulously record all the training details–everything from training requests to after-training evaluations–and use spreadsheets to tally the results. Then you could find an L&D team member or an IT specialist to analyze the results and help you find the useful patterns that reveal:

  • What happened
  • Why it happened
  • What’s likely to happen in the future
  • What you should do to 
    • Capitalize on the strengths the data reveals 
    • Bolster the weaknesses  

Does this sound tedious? It is–particularly if your company is large and multi-faceted, and has a well-established, diverse training program that quickly generates thousands of data points. 

This approach is also prone to errors or omissions that result in incomplete data such as someone forgetting to record a training request, failing to follow-up with post-training observations, or incorrectly recording the costs associated with a training initiative. 

As a result, the conclusions derived from that learning and development data are suspect and quite possibly inaccurate. 

While initiatives such as xAPI have made great strides in collecting data on learning experiences, training analytics need more context and wider scope. Without it, your L&D data can only paint a small part of a very large picture.

How to use data for L&D

How you use the data depends on the problems you are attempting to solve or the goals and objectives you are trying to meet. 

For example, your medium-sized company might invest in an integrated L&D platform that allows you to track intake and guides the process of course development and design. The software may even include the details of who attended which training, when they received the training, and why they opted for it or were recommended for it. 

In that case, you have lots of data. Even if your platform has the capacity to crunch hundreds of metrics that cover virtually every aspect of your L&D activities, you don’t have the time or resources to understand and utilize all that information. You must determine the most important metrics at this point in time and learn all you can from what the data suggests. 

Further, assume that you are the director of the L&D department and you just received last month’s training details. Consider the differences in how you would use that data to determine:

  • Which L&D team member to promote to a team leader.
  • What training initiative resulted in the greatest amount of long-term change in work habits.
  • When to schedule a full-day training session for new hires.
  • How to modify the next quarter’s training initiatives to reflect unusually high employee turnover.

How to use the data most effectively depends upon what you need to answer. That, in turn, depends upon the goals and objectives of your team, department, and company. 

Useful techniques for learning and development data analysis

Rapid and large increases in technology make data analytics a fluid field. However, a few guidelines remain consistent.

Quantitative data lends itself to analysis

The general rule for data analysis in L&D–or anywhere–is that quantitative data is easier to analyze than is qualitative data. 

So, the post-training questionnaire that asks learners to rate the content and the instructor using a 1-7 scale is easier to analyze than one that uses words such as excellent, good, and fair. (What one learner views as “good” content may be only “fair” to another learner.) 

The open-ended question that allows learners to write in their opinions is the hardest to analyze. Dynamic LMS platforms or learning software programs that include data mining and keyword recognition capabilities are able to detect anomalies, identify trends, and generate meaning even from qualitative data. However, quantitative data lends itself to easier analysis.

Gathering data at different points in time provides a more accurate assessment

Learning is a process more than a point-in-time event. True “Eureka!” moments are rare. 

Gathering training data before, immediately after, and weeks or months after learners receive training fills out their learning picture. Repeated data gathering also helps you gauge the overall effectiveness of the program in terms of learning, budget constraints, and time commitments. 

Should you hire data analysts as part of the L&D team?

Would you hire the average short-order cook to cater a key meeting to which you’ve invited 100 major stakeholders? If you’re serious about learning as much as the data can tell you about the past, present, and future performance of your company, why wouldn’t you hire a data analyst? 

Folks in this field generally go beyond simply understanding what the data says. They’re passionate about figuring out as much about the “why” and “how” and “when” as they can.

Training Industry notes, “There’s technique, technology and, now, talent for L&D analytics.” Your company would reap benefits by acquiring some of the talent, whether it comes through external hiring or developing these capabilities among existing team members. 

Best tools for learning and development data analytics

Are you ready to invest in learning analytics that will propel your training programs to the next level? These solutions will help.


Cognota lets you discover operational trends and visualize what those trends mean. By managing training intake, planning, team capacity, storyboarding, and collaboration in one platform, your learning operations data produces insights that enable you to assess:

  • How your team is responding to training demand
  • Visibility into the volume and status of training requests
  • Where training requests are coming from in the organization
  • Where training requests are coming from in the organization
  • Resources that are over or under utilized
  • Where efficiencies can be created in your processes
  • How quickly your team is completing tasks and projects 

You’ll have everything  you need to generate and share KPIs that support the value of your L&D program. See for yourself with a free trial or speak with the sales team to schedule a demo.

iSpring Learn

The iSpring Learn software reporting function provides real-time, integrated data analysis regarding courses and learners. Discover what’s working well and what isn’t. Monitor an individual’s progress and relate it to performance.  Evaluate your team’s performance, too. 

Absorb LMS

The Reporting and Analytics functionality of Absorb LMS provides you with the information you need to understand what the data is saying and make decisions that line up with that knowledge. The Absorb LMS reporting and analytics dashboards provide insights that direct your learning initiatives and help you validate your programs with key indicators. 

Learning and development data analytics: key takeaways 

  • Data–Big Data–is everywhere, and it’s here to stay, thanks to technology that recognizes, gathers, and responds to it. 
  • Learning and development data analytics holds the key to unlock what all that L&D data means and understand how it can be used.
  • L&D leaders who want to thrive going forward need to understand and harness data analytics.
  • Robust L&D platforms enable comprehensive, integrated data collection and analysis that help L&D align its activities with overall business objectives and the needs of its learners.

Cognota lets you harness learning and development data analytics so that you can glean from the data everything you need to understand where you’ve been and recognize the best path forward. See for yourself with a free trial or speak with the sales team to schedule a demo.

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Learning and Development Analytics: For Beginners to Experts