In a previous post, we posed the question, “Is Big Data a Game-Changer for L&D?”
The answer is, we know it is! Big data cannot only align learning to job performance, but can inform strategic business decisions that have a huge impact on organizational success.
In this post, we’ll take a closer look at the power of “big learning data” using a few sales and safety examples. But, keep in mind that big data analysis for learning works across all industries and jobs. We’ll also refer to Axonify’s specific capabilities because it’s the only learning solution that provides organizations with a way to capture learning and performance data, and tie it to business results.
The power of harvesting big data for L&D
Have you ever wondered if your corporate learning initiatives were actually working? With big data, you can answer this question. By tracking employee knowledge and behaviour targets and metrics, you can correlate how well employees absorb information and apply learning to the job. Then, you can extrapolate if there are gaps in knowledge that are impacting their ability to perform specific job behaviors. You can also review this data at the most granular level to analyze an individual employee’s performance; or aggregate it across divisions, regions, or even across a global organization consisting of thousands of employees, and millions of learning events. Combining this data with job performance metrics—such as the number of injuries on the job or sales performance data—allows you to link learning initiatives directly to the bottom line. Sound complicated? It’s not—as long as you have the technology to help capture and analyze this data.
Leveraging big data at the individual employee level
What an employee knows directly impacts what he or she does on the job (we call that behavior). This level of knowledge influences employee performance and decision-making. To ensure learning can impact desired job outcomes, Axonify helps L&D professionals and business leaders to:
- Assess knowledge levels employees have achieved through learning.
- Identify how employees apply knowledge to the job by observing job behaviors and entering this information into the platform.
- Compare this data to job performance outcomes—such as the number of safety incidents, or percent achievement of a sales quota.
Let’s start with a safety example to illustrate how this works. If a fork lift operator gets into an accident after receiving training, you can correlate the data you have around his/her subject matter knowledge against very specific behaviors, such as “Driving the forklift in reverse going downhill” (the correct action that should have been taken to avoid the accident). If the knowledge level doesn’t meet a defined threshold, and you observe the employee performing this action incorrectly, once you input this observation into Axonify, the employee will receive more training. Once the knowledge level improves, you can then re-evaluate the behavior to identify whether it has been modified by the knowledge. This ensures you are delivering the right learning to drive the correct actions on the job.
Here’s another example: Perhaps a sales representative is not meeting sales targets for a particular product. In this case, you can test knowledge levels around product and sales skills, and document specific behaviors that define whether the employee is demonstrating the actions required to be a high-performing representative. If knowledge increases and observed behaviors begin meeting targets, then performance improvements will undoubtedly follow. If the majority of sales representatives fall in line with targets and their sales are improving, then you know from the data that there might be another reason a certain rep is under-performing—which will allow you to provide additional coaching or mentoring.
Analyzing big data for larger employee populations
The same learning data that is used in Axonify to analyze individual employee knowledge and behavior, can be aggregated to provide a broader look at your organization. Let’s go back to the sales target example. If you learned that sales reps in one region were outselling reps in other regions on a particular product line, you could evaluate whether there are differences in product and selling knowledge between the performers and non-performers in each region. Are there differences in product knowledge on a region to region basis? Are there differences in sales skills knowledge? If yes, you could provide additional learning to the non-performers until their knowledge levels improved to the target level, then analyze whether sales also improved.
When you’re talking about workplace safety, there are often differences in OSHA recordables from one facility to the next. Using Axonify knowledge and behavior data, you could evaluate why that’s happening. If all employees are receiving the same learning, are there differences in knowledge levels? Do lower knowledge levels also correlate to lower levels of proper behaviors, and an increase in incidents? Are the lower knowledge and behavior levels across the board, or are the numbers skewed by a small group of employees, or a single location? If you focus on improving knowledge levels, do you see a corresponding increase in proper behaviors and reduction in incidents? Whether it’s for less than 50 or for thousands of employees, you can capture this data and perform the analysis that helps pinpoint the issue.
Using big data analysis to deliver better learning content
As well as analyzing what employees know and do, you can also identify whether learning content is appropriate. In some cases, the data may show that employee knowledge is high, but the behaviors being exhibited are still incorrect. If this trend holds true from the individual employee through to larger groups of employees, it suggests that employees are not able to translate knowledge to the job, or that the learning content isn’t emphasizing the information employees need to take the right actions. It gives you the tools to take a closer look at whether learning content is related directly to the behavior that employees should take as a result of that learning, which is the very essence of applying learning to the job.
Maximizing the power of big data for predictive analytics
Besides using big learning data to analyze and improve learning for both individual employees as well as larger groups, you can mine it to identify and prevent potential problems before they occur, as well as leverage it to aid in proactive decision-making.
In the case of workplace safety, you can analyze the data for trends at the individual, group or even regional level. If you see a trend that highlights employee knowledge is not at a desired level and behaviors are not optimal, this is a clear indicator that there is a higher potential for injury. That means you can work on mitigating this risk before it becomes a huge problem by pinpointing the knowledge employees need to correct their behaviours and reduce the likelihood of an accident.
Big data can also help with business planning and decision-making. Here are just two ideas:
- In the case of a new product launch, you can use historical learning data to identify how long it takes for salespeople to get up to speed on product knowledge, and how long it takes to for their knowledge to translate into actual sales.
- With information about how long it takes for employees to reach optimum knowledge and accident-free operation, you can better plan for new hires, or even new plant operations.
From the most granular level to organization-wide analysis, big data for employee learning and knowledge is now here, and ready to play a big role in your business success. For more information: