Most L&D practitioners know that they could be making better use of data. As a function we generate a lot of data from all the interventions and activities we track but a lot of us don’t use that data effectively.
But it is the data that can cause the problem. How are you supposed to track it, especially when it is being generated by disparate systems? That was our challenge and one we have overcome using a data visualization tool called Tableau.
By pulling numerous sources of data into one central location we have been able to create dashboards that provide a unique view of the data based on what the L&D team or business would like to know.
For example, if a manager has attended training we can see if it has a positive impact on turnover, sickness and engagement. If it has, we would share this with the business as a positive outcome of learning.
We also look for predictive factors; for example, if training spend drops in a certain area, does our data predict that there will be an increase in turnover or a drop in engagement? If it does, then we can help the business address those factors before they happen.
So why are we putting all this effort into making sense of our data? There are two reasons. First, we want to use data to help shift the L&D team from a reactive to a proactive service. This means using current and past data as a predictor of future performance.
The second driver is using data to uncover learning success stories at an individual, team or functional level. Where learning has resulted in a positive business impact, the data can help identify what interventions worked. The L&D team can then create and share these stories of success.
The data enables us to start to talk about the difference that learning has made. It's then no longer about the tool or about the intervention, it's about the impact. I'm hoping this can drive a better emotional connection to learning for individuals and the organization.
Tracking data over time has enabled us to identify drops in learning activity that might have a knock-on effect on the business. For example, the L&D team noticed a drop in learning hours within the operations teams. Digging deeper, the data showed an increase in employee relations cases and a decrease in employee satisfaction. This insight led the L&D team to use the data to establish the cause.
Feedback from employees showed that management style was an issue. Employees in these teams also felt that they had not grown professionally or had been able to take up opportunities to move around the organization.
These insights told the L&D team that they had to get better at sharing stories about the opportunities to move around the business.
They also showed that 10 managers had low engagement scores and that these managers’ actions impacted on up to 800 people. This insight enabled us to develop individual development plans for each of those managers.
Without visualizing the data in a way that showed that learning hours were dropping, we would possibly not have identified this problem, and certainly not quickly enough to remedy it.
Using data in this way is helping us troubleshoot performance problems and deliver relevant solutions.
These insights show the power of the data that we, as L&D professionals, have at our disposal. If you are not already using data to help improve performance, then I’m hoping our journey will encourage you to do so.