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  • David Goldstone

Liberate your L&D data the Tableau way

Since the summer DG Data Services have had the pleasure of being partnered with Manchester based eLearning Services Consultancy Jampan, and offered a solution to Quilter, a Financial Services organisation. Below is a case study on how we and Tableau helped solve their problem. My Thanks go to Martin Couzins Director, itsdevelopmental Ltd and Ennis Reid at Quilter to help write up this case-study.


Summary:

L&D teams create a lot of data but creating valuable insights that lead to positive business impact can be a challenge. A new service from Jam Pan helps organisations liberate data and use it to drive business value. 


The challenge

“L&D must make better use of data.” This message will be familiar to all corporate learning professionals. However, with a variety of disparate data sources and IT systems, learning professionals have found it hard to create one place that collates all data, let alone provides actionable insights.

One Jam Pan client, financial services organisation Quilter, approached us with this dilemma. The L&D team was sitting on a vast amount of data from a variety of different sources and it wanted to start to use that data to deliver better business outcomes.


What we did

By partnering with DG Data Services Ltd, we created a central data source from a range of disparate IT systems. There are nine sources of data that flow into one central location. From here, data dashboards are created based on business need. These dashboards provide a unique view of the data based on what the L&D team or business would like to know.  


Drivers for the project

For Quilter, there were two drivers for this initiative. The first was using the data they generated to help shift the L&D team from a reactive to a proactive service. This meant using current and past data as a predictor of future performance.

Data provides a historical perspective on performance. It highlights where performance dips and why. Using these perspectives would help Quilter identify performance problems and act on them.

The second driver was 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 Quilter L&D team can then create and share these stories of success.

“We can 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 and therefore, I'm hoping we can drive a better emotional connection to learning for individuals and the organization.”


Uncovering success stories using data

Quilter uses data to identify where learning is having a positive business impact. The L&D team cross-references learning data points with engagement scores and learning hours within each team. They then drill down into the data to see what learning has led to increased engagement.

The L&D team looked at engagement levels across teams and found that some learning events had been particularly effective at driving up engagement. For example, there was a team within the contact centre that had participated in an insights session and backed that up with a volunteering day. As a result, the team’s engagement scores had improved particularly around indicators such as peer relationships and organisational fit.


Predictive analytics

Tracking data over time has enabled Quilter 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 further, 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 and taken up opportunities to move around the organization.

These insights told the L&D team that they had to get better at sharing stories around 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 Quilter to develop individual development plans for each of those managers.

Without visualizing the data in Tableau in a way that showed that learning hours were dropping the Quilter L&D team would possibly not have identified this problem, and certainly not quickly enough to remedy it.

The dashboards also enable Quilter to look for predictive factors around performance. For example, if training spend drops in a certain area, does the data predict that there will be an increase in turnover or a drop in engagement? If it does, then the L&D team can help the business address those factors before they happen.


Impact

The L&D team is not far off being able to show the return on investment of training initiatives. Already he is able to troubleshoot performance problems and deliver relevant solutions that are having significant business impact based on performance needs that have been identified through analyzing the data and that show the impact of learning on business performance.

Through visualization in Tableau, they are now also looking to correlate data to show the impact of L&D interventions. Attending training might show a positive impact on turnover, sickness and engagement, for example. This is the type of insight the team is looking to share with the business.


Our approach

We use a mix of analysis consultancy and design to create data dashboards that help visualize touch points, learning, training spend, levy utilization, engagement and people stats against business metrics.


How do we do this?

Our process is simple . . .


Step 1: Requirements gathering

We look at what you are trying to achieve and the data you have available to you currently. We look at the quality of the data, how it can be accessed and how frequently it is collected.



Step 2 Dashboard prototyping

After checking an example dataset to see if it will produce the insight you are looking for, we provide a prototype of your dashboards.


Step 3: Development and support

Regular meeting and progress updates enable you to see the development of your dashboards and to make alterations as required.

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