In a modern business environment that is dominated by processes, technology, and information, data is an incredibly useful tool in determining a variety of business impacts. Organizations create and collect data across all areas of business, and the use of data analytics is a driving force in determining ROI and other high-impact business metrics. When it comes to learning and development, successful understanding and implementation of data analytics is a key factor in ensuring programs are effective and efficient.
Generally, data analysis is the process of cleaning, transforming, and modeling data to discover useful information for business needs. Data analytics in learning and development can be defined as the process of gathering and analyzing data on a variety of different learner metrics. These metrics include but are not limited to employee performance, application of skills, process recall, and more. Data analysis can be useful in two different stages of creating a learning and development program: pre-rollout and post-rollout.
Pre-rollout data analysis is all about gathering information on your learners and their needs. Most organizations implement learning and development programs to address skills gaps and bolster employee knowledge. A proper analysis of current employee proficiency can help your organization deliver the perfect solution. Time and again, we’ve focused on the importance of conducting a training needs analysis before building any learning program, and data analytics is a critical part of that process.
Here’s example of what pre-rollout data analysis may look like in learning in development:
An organization notices that certain employees in their field support services do not have enough time to complete certain aspects of their roles. They want to implement a learning program that helps their employees better use technologies to complete tasks, but they’re not sure exactly where most of the employees’ time is being spent. As a part of their training needs assessment, the organization gathers statistics on particular tasks and how long their employees spend on those tasks. After reviewing this data and comparing it to time on task estimates, they find that their employees need training on two or three specific processes to improve efficiency. This is a great use of pre-rollout data analytics.
Post-rollout data analytics is arguably more complex than pre-rollout, but it can help your organization avoid wasting time on ineffective learning programs. Here are a few examples of what this could look like:
- Analyzing data collected on how employees’ efficiency and performance in their role has changed after completing training.
- Creating a graph that reflects individual performance within specific roles to gain a better understanding of which employees need support and which are performing well (tip: many companies leverage the knowledge and insight of their high-performing employees when creating L&D programs).
- Deep dives into learning content engagement—that is, how are your learners interacting with training materials? Are they skipping through videos or watching them all the way through? Engagement is an important aspect of successful learning programs, so don’t overlook this aspect.
Keep in mind that learning and development data can be both quantitative and qualitative. Quantitative data deals with things you can count, put in order, and categorize. Qualitative data focuses more on experiences and feelings; the ways in which your employees have perceived learning programs, for example.
Here are some examples of quantitative data in an L&D context:
- Course completion rates
- Hours of training completed by individual employees, teams, and departments
- Revenue statistics before and after training
- Top-performing employees training exposure and completion vs. low-performing employees training exposure and completion
Some examples of qualitative data in L&D are:
- Whether or not your employees found the training useful and applicable to their roles
- How employees perceived the training as fitting into their job, i.e., whether or not employees found training to be a good use of time and resources
As work continues to shift to more digital platforms and processes, data is more accessible than ever. This is a massive benefit to organizations, as it allows them to make better decisions on where to spend their money and time. In the current learning and development landscape, there are a few ways in which your organization can gather data to be used in the training decision making process.
For quantitative data:
If your organization has an LMS, there are a variety of different ways in which you can gather data. Most LMS software comes standard with analytics functionality which allows for the user to automatically generate reports and data on training programs. If you generally develop training on a standalone basis, you can use a custom post-rollout survey to obtain quantitative data.
For qualitative data:
The best way to get a true understanding of employee experiences and perceptions is to provide spaces for them to be heard. If you value the opinion of individual employees and want to analyze their experiences, consider conducting post-rollout interviews, or send questionnaires to employees that target their experiences.
Today, the most important business decisions are made on the recommendation of detailed data analysis findings. Learning and development programs can be a significant financial investment for organizations and creating a system in which data drives decisions can be one of the best ways to ensure you are getting the most out of your investment. When building your next learning and development program, consider creating a solution that allows you to meticulously analyze learning outcomes—this will not only improve current employee success, but build a strong platform for learning in the future.
