Learning and Development
Datafication: Using Data and Analytics to Drive Performance in L&D
Today we live in an era where rapid changes in technology are creating frequent skill gaps. Keeping up with all the changes has become quite challenging for those in academia as well as in business. In this scenario, the Learning and Development function (L&D) in organisations has become crucial, given the fact that upskilling and internal mobility have become the norm. However, a Gartner research report found a disconnect between the success metrics of L&D programs and their impact on business.
As the landscape of workplace training evolves to meet this need, Gartner recommends using data-driven insights to transform L&D. Academic institutions and L&D functions that shape the workforce of tomorrow must adopt a data-centric approach to drive learning programs today.
By embracing datafication, L&D can unlock timely and relevant learning opportunities, enabling better learner performances. Such a learning culture will also ensure effective learning experiences through enhanced employee engagement, which in turn leads to greater retention, a key challenge in the current talent-driven business world. However, if academic institutions can adopt this approach to learning, it would ensure that students learn right and adapt to the continuous learning culture that prepares them for a successful career, thereby addressing the skill gap issue efficiently. Let us see what datafication of L&D means and how it helps businesses.
Decoding datafication in L&D
Datafication involves leveraging data from various systems, such as HR and learning management solutions (LMS), to analyse learning behaviours and skill sets, allowing for better alignment with the talent needs of the market. Collecting and analysing data and extracting insights helps organisations and academic institutions craft an intelligent L&D strategy for their learners. Powered by data analytics, the L&D function can drive personalised learning journeys that ensure better learning outcomes. It helps bridge the gap between L&D success metrics and the impact of learning on business.
Leveraging data is one of the key recommendations in a Deloitte report titled, Short on skills? Rethink your learning approach. The report suggests adopting an AI-based learning system to align the employee learning journeys to market dynamics. This approach aids in creating an enterprise learning management system that assures a future-ready workforce. Data acts like a northstar, guiding the learners and the enablers to align with the market needs and goals.
Relevant L&D data types
- Quantitative data
- Qualitative data
- Behavioural data
Some of the quantitative data that can be tracked and analysed are:
Course completion rate: It indicates the effectiveness and engagement of the course content.
Assessment scores: They help measure the learner's comprehension and retention of the course.
Time spent: Offers insight into learners' interest and the effectiveness of the content through module-wise learner engagement analysis.
Attendance record: Helps track engagement and commitment of learners to provide learning behaviour insights.
Learner feedback: Open-ended surveys and interviews help the stakeholders understand learner satisfaction, improvement areas, engagement levels and quality of the learning material.
Usage patterns:Tracks the frequency and methods through which learners interact with the various learning resources. Progress tracking is one way to understand this.
Interactions: Learner engagement analysis of interactions like clicks, views, navigation, etc., help identify popular or underutilised learning content.
Combining qualitative and quantitative data offers comprehensive insights, and when cross-referenced with behavioural data, it improves learning outcomes through carefully crafted learning management solutions.
Data analytics and their applications in L&D
- Descriptive analytics
- Diagnostics analytics
- Predictive analytics
- Prescriptive analytics
This analytics mode helps identify patterns using L&D data such as course completion rates, assessment scores, usage patterns and interactions. It provides a descriptive analysis of the historical data of past training programs to understand their effectiveness while answering the question, "What happened?", and aids in identifying areas of improvement.
This analytics mode provides inputs to the question, "Why did it happen?" It throws light on the reasons that lead to the outcomes. For example, why do a few modules have lesser engagement or completion rates? It helps spot and address specific issues with timely interventions.
In an outcome-driven world, it also helps to understand “What can happen in the future?” Predictive analytics provides insights into potential completion/drop-off rates, course demand, problematic modules, etc. These insights help finetune and personalise the training content, as well as the mode and support provided to the learners to ensure desired outcomes.
Prescriptive analytics is like an add-on to predictive analytics. If we know what is likely to happen, it also helps to know “What should we do” to ensure the desired outcomes. And that is what prescriptive analytics helps with. It can help chart highly personalised learning programs for learners to meet their unique needs. Creating adaptive learning platforms that dynamically adjust the learning content and mode based on learner performances is a great way of implementing L&D analytics.
Datafication in L&D: Gazing into the crystal ball
Recognising the transformative potential of data analytics in L&D helps organisations become agile and competitive by cultivating a culture of continuous learning empowered by personalised journeys. It helps academic institutions nurture students that are future-ready, and who have understood how to learn the correct way to keep learning throughout their lives! Embracing datafication in L&D is undoubtedly a paradigm shift to the traditional learning approach but it is one that helps navigate the complex modern academic and talent challenges effectively.
How can Infosys BPM help?
Infosys BPM'sLearner Segmentation and Recommendation Services help academic institutions make informed decisions with our AI/ML-based integrated recommendation system. It helps predict learner segmentation with personalised course and service recommendations, enabling academic institutions to enhance the outcomes and effectiveness of the learning programs.
