Learning & Development Manager High Speed Training
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Why we need a data-led approach to L&D effectiveness

During this challenging time for business, when budgets are likely to be tight, it’s more essential than ever before that L&D is able to effectively measure the impact of its initiatives and secure buy-in from the top. A data-led approach is key to success.

25th Nov 2020
Learning & Development Manager High Speed Training
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Investment in L&D initiatives can play a crucial role in an organisation’s ability to succeed for many reasons. Today’s employees are actively seeking roles that will give them the opportunity to learn and build on new skills, so employers need to show willingness to invest in their staff in order to attract the best talent. It is also beneficial for employers to invest in their current workforce through L&D initiatives, because when they are properly aligned with company goals, they can have a substantial impact on helping organisations to meet their long and short-term objectives in all sorts of areas – financial, employee retention and customer satisfaction, to name a few.  

L&D professionals are increasingly seeing the value of using methods of quantitative and qualitative data collection that look at the impact on the learner, customers and the business. 

Without any data or evidence, however, businesses can struggle to measure the impact that L&D has had, making it much harder to prove the return on investment and gain the necessary buy-in for planned initiatives or future training. Additionally, failing to collect the necessary information to measure the impact of an L&D programme can mean that the L&D team is unable to identify where initiatives may not be providing the desired outcome. This can result in wasted time and money, and quickly losing buy-in from across the organisation. 

It’s essential for L&D initiatives to be closely aligned with the organisation’s key objectives. Collecting and evaluating data provides key information that can help L&D to identify training needs and skills gaps, develop well-designed initiatives to close them, measure ROI and determine if they have been successful in supporting business goals.

Beyond the ‘happy sheets’

When discussing data in L&D, many conversations hinge on the use of attendance and satisfaction score sheets (happy sheets). While these should not be discounted as ways of measuring the success of L&D, there’s an opportunity to go beyond these metrics. L&D professionals are increasingly seeing the value of using other methods of quantitative and qualitative data collection that look at the impact on the learner, customers and the business. Effective ways to help inform L&D can include looking at:

  • Employee retention metrics: these help to determine if an onboarding programme is helping staff feel competent in their duties.
  • Productivity metrics: measuring changes to levels of productivity from before and after an intervention can flag areas of concern early on.
  • Incident records: for example, for health and safety (H&S) matters, running H&S training and measuring the number of related incidents before and after the course can provide a clear indication as to whether it was deemed useful.
  • Customer satisfaction scores (for example a CSAT score): comparing a log of the tone of customer service enquiries and an overall CSAT score from before and after staff training can show a direct and positive effect.
  • Observation data: this data collection method can show if there is a noticeable behavioural shift amongst employees after completing training.
  • Learner satisfaction data: utilising qualitative data via interviews, surveys or Q&As about the learning experience to identify where there is room for improvement.
  • Before and after assessment scores: putting an assessment in place prior to training and then repeating the same test post event enables the two scores to be compared. This can be used to determine what key areas of knowledge have been taken away from the course and where there are still areas to focus on.

Implementing a combination of assessment techniques will be imperative for organisations of all sizes to improve the way they operate and perform more successfully compared with their competitors.

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Instilling a positive learning culture

There are certain issues that may hinder the effective use of data and metrics in L&D, however. When teams work in so-called ‘data silos’ and are reluctant to share data, or simply do not have effective data sharing methods in place across the organisation, it can be extremely destructive to the personal development of the wider workforce. Additionally, the organisation's learning culture can have a significant impact on data collection and sharing, and the reach of L&D initiatives. If senior members of staff have a negative outlook on L&D then this can negatively influence an organisation’s learning culture, which is dangerous territory and can result in poor L&D buy-in and data sharing from the top down.

One way L&D professionals can overcome this particular issue is by using data to demonstrate the impact of their interventions. This will help highlight the positive impacts an intervention can have, helping the L&D team gain increased buy-in from decision makers. Data collection and analysis will also provide valuable insights into any successes and weaknesses of an activity, allowing L&D professionals to showcase positive impacts and better inform any future interventions.

Having a positive learning culture across an organisation, starting from the top down, is essential to ensuring that employees approach learning opportunities with a positive mentality.

Involving key stakeholders in the L&D development process from the start is an effective way to gain buy-in and will result in more senior members of staff being willing to invest time and money in L&D. This will then, in turn, trickle down to the rest of the whole organisation, helping to position training as much more than just a box-ticking exercise.

Interested in this topic? Read Learn, data, action: how to make your learning data actionable.

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