How to use data to optimise training strategies
It is possible to improve the impact of training by collecting and analysing data. But it is not always easy to collect them nor to know which ones or how to use them.
Nowadays, those who choose to work in education increasingly have to deal with data collection and analysis. Data analysis is one of those practices that is becoming indispensable to any type of organisation, from a company to an educational institution at any level, from a business strategy to an education strategy.
The collection and use of student data is crucial to answering the question: "Is the chosen training strategy worth the investment?".
Data analysis is bound to be part of every training professional's job, but it is not always easy to find the right information and interpret it correctly.
In this article we will discuss the most common challenges encountered in data collection in this field and look at some practices to overcome them.
Why data is important for training
Since employee training and retraining are fundamental and vital elements of any business strategy, the measurement of results related to these practices should be as well.
A training strategy is necessary to retrain employees, to keep them up-to-date and to remain competitive. It is used to teach topics of compliance with corporate values and to ensure compliance with legal and industry requirements. Many companies offer personal employee development programmes to ensure that employees are psychologically well off in the work environment. In some parts of the world such as Northern Europe and the U.S.A., companies are increasingly offering L&D (learn and develop) courses to keep employees motivated and thriving in the work environment.
These are all good reasons to implement a training strategy. However, simply launching a course, no matter how well organised and structured, does not guarantee results. To know whether the training strategy you are implementing is successful, you must be able to measure and analyse data.
You have to consider quantifiable metrics, so take into account things like training completion rates and performance. But you also have to consider qualitative data, such as feedback from participants and evaluation of the training and managers. It can be tricky to understand how to measure or interpret data correctly, and there are some common pitfalls to watch out for.
Below we look at the 4 most common problems in collecting and analysing data and strategies to overcome these obstacles.
1. Lack of goals and definition of success
It is difficult to analyse data when you do not know what you are looking for. Many organisations struggle to understand what to analyse and what data to collect. This is because they do not have clear objectives or a baseline defining what success is.
The solution here is to set clear objectives in the form of key performance indicators. Every training programme you put in place has a reason: make sure you establish key performance indicators (KPIs) related to those objectives, just as you would for any business initiative.
Clarify the results you want to achieve from the training and set targets that indicate success. For example, if your goal is to ensure that everyone is up-to-date on new compliance policies, set a KPI that indicates a 100 per cent training completion rate within three months.
If your goals are more ambitious, you could set a 'knowledge retention' target to ensure that people have actually internalised the new content. You could define this target as 'every employee will score 90% or higher in post-training assessments'.
2. Data Collection
It is not automatic to have the opportunity to collect relevant data when training starts. Moreover, it is often the case that a training programme is launched and completed only to realise that more information is needed to evaluate its success.
Suppose you have just completed a programme on corporate values and culture. You have data showing that all employees have taken the course. But as time passes, it is clear that not everyone has fully understood the training material. So the company wants to identify weak points in the course in order to strengthen it.
Questioning employees about their training experience is expensive and time-consuming. In addition, the more time that passes after the end of the courses, the less reliable the employees' recollection will be.
The solution in this case is to create a system in which you can, at any time you want, generate reports that take stock of the situation.
If you use a learning management system (LMS) to deliver training, you will be at an advantage because you can take advantage of the platform's functions. Depending on the system used, you can set up functions that track the key metrics you choose, and some systems also allow you to analyse the data. These systems allow you to generate a report at any time and see the status of things quickly.
Collecting data manually is more complex. One has to manually keep track of who registers, who completes each course, performance and a lot of other data. This must happen in parallel with regular evaluations and reports on student progress. Ask educators to carry out other quantitative evaluations to see if people retain what they learn, and other qualitative evaluations such as student satisfaction rates, whether they manage to put what they learn into practice in the workplace, or whether they find what they learn useful. This can be done through questionnaires offered at the end of each lesson and at strategic moments (such as at the beginning, in the middle, at the end and several months after the end of the course).
Once you have chosen and initiated some methods to collect data, constantly examine the results to assess progress and find areas for improvement as you go along. To put the information gathered into practice, use the qualitative data to create new content or improve existing content in line with student expectations.
3. Collecting qualitative data
Collecting quantitative data can be time-consuming but it is not a complex task. Qualitative data is more difficult to collect and analyse. Even those who rely on the most advanced technology such as a learning management system to deliver training cannot create a complete picture of how or if training helps employees in the workplace.
If a company sets the goal of training its sales department to increase sales by 15 per cent in one year, and after one year sales increase by 15 per cent, you know that the training has worked. But how is it possible to know in the meantime whether employees are really benefiting from the training?
The solution in this case is questionnaires. Interview or send questionnaires to employees on what they think about the training, whether they are benefiting from it, what they would improve on, ask if they have had a chance to use the new skills on the job. Ask managers if they see the new skills being used at work. Ask employees how they feel about the training and skills learned during periodic performance reviews.
A tip: since analysing qualitative data is complex, do not limit yourself to just asking open-ended questions but ask them to express feelings on a numerical scale. This will make it easier for you to make sense of and organise this kind of data.
There are several ways to collect post-training feedback: e-mail, online tools that allow you to create questionnaires and organise their sending, learning management systems, in-person interviews and many other ways.
4. Lack of a safe sharing environment
Appraisals and feedback sessions are only successful if employees willingly share their experiences, both positive and negative. Very often it is underestimated that some may be hesitant to share any concerns or confusions for fear that it might affect their performance appraisal or for fear that the information might get to superiors. Or they may feel they do not look like a team player if they get negative feedback.
The solution is to create a safe sharing environment. To do this, anticipate your concerns and, before sending out questionnaires or conducting interviews, talk openly about why you are collecting this data. Reiterate that the purpose is to provide employees with the skills and professional development they need to succeed. Then, clarify how the data you share will be used and how it will not be used. You may also consider keeping data anonymous so that employees feel freer.
Improving training with data
The importance of data nowadays is undeniable and starting to know how to use it can make the difference between a successful and a failing training plan. The challenges described above are common to every training plan, but it can be overwhelming to tackle them all at once. So, whether you are in the middle of a training plan or still studying your options, you can start addressing these challenges now, one at a time. Establishing clear goals in terms of success, evaluating which metrics to prioritise and creating a plan to monitor the information is the first step.
Knowing whether training is successful is crucial to achieving related business goals. If you can overcome the challenges inherent in collecting learning data, metrics will be a valuable tool.
Translated with www.DeepL.com/Translator
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