Corporate training is undergoing a profound transformation. It is no longer merely a means to update skills, but a true strategic driver of organizational growth. The ability to read and interpret data represents the turning point between those who “do training” and those who build learning ecosystems capable of evolving over time.
Having worked in digital learning for a long time, I find this shift particularly fascinating: technology, finally and concretely, offers us the opportunity to bring depth back to learning processes. Data, when interpreted with humanistic sensitivity, tells stories of people, of paths, of change. This, in my view, is where the real contemporary challenge lies: combining the intelligence of systems with the intelligence of relationships, transforming training into a space where analysis and empathy coexist and enhance each other.
Learning analytics are emerging as the new compass to guide change. From simple monitoring tools, they have evolved into an interpretive language that connects learning to business outcomes. According to Deloitte’s Global Human Capital Trends (2024), over 70% of organizations recognize the value of learning data for talent development, yet only a minority have a mature strategy to fully leverage it. What is needed today is not the accumulation of numbers but the development of a true data culture: a grammar of learning that enables us to read the weak signals of change and translate them into concrete decisions.
Oltre il completamento del corso: verso analytics più sofisticati
For years, corporate training has been measured through quantitative indicators: hours delivered, completion rates, platform access, tests passed. Useful but limited metrics, as they fail to describe how—and not just if—people learn.
Le piattaforme di nuova generazione integrano oggi sistemi di tracciamento avanzati che rilevano interazioni, engagement, tempi di attenzione e correlazioni tra attività formative e performance lavorative. I dati confluiscono in dashboard dinamiche e strumenti di Business Intelligence che permettono ai team L&D di osservare i processi in tempo reale e di intervenire in modo mirato.
According to the Brandon Hall Group, companies using advanced analytics in training programs see a 24% increase in talent retention and a 32% improvement in average productivity. Training, therefore, is no longer a cost center but a growth lever: a laboratory where data not only describes the past but anticipates the future.
From behavioral metrics to predictive models
The new ecosystem of learning analytics embraces a broader and deeper range of metrics. Behavioral analyses track interactions, navigation paths, and collaborative dynamics in social learning hubs, providing a precise map of participation. Cognitive metrics, on the other hand, observe time spent on reflection, revision, and peer feedback, offering a qualitative measure of learning.
At the same time, performance metrics relate training outcomes to operational results, while predictive analyses—based on artificial intelligence algorithms—identify early signs of dropout, declining engagement, or reskilling needs.
The goal is no longer to measure for control, but to measure for understanding. As the Harvard Business Review notes, the true value of learning analytics lies in their ability to connect learning and organizational impact, turning numbers into insights that improve overall performance.
L’emergere del multimodal learning analytics
The most innovative frontier is multimodal learning analytics (MMLA), which combines digital and biometric data to provide a deeper view of the learning experience. Leading companies are experimenting with biosensors, eye-tracking, and facial expression analysis to detect attention, stress, and engagement during training sessions—especially in virtual and augmented reality environments and simulations.
The goal is not invasive but cognitive: to understand when and how people learn best, in order to design personalized and sustainable learning experiences. However, this evolution brings with it major ethical and regulatory issues: transparency in the use of biometric data, privacy protection, and algorithmic accountability.
As the European Commission’s Digital Education Report (2023) reminds us, “learning data belongs to people, not platforms.” Building trust means combining innovation with responsibility, technology with humanity.
ROI, impact, and the strategic value of training
La domanda centrale rimane: qual è il ritorno dell’investimento formativo? Misurare il ROI dell’e-learning è complesso ma oggi possibile grazie a modelli integrati che combinano la logica di Kirkpatrick (reazione, apprendimento, comportamento, risultati) con indicatori di business come produttività, qualità del lavoro o tempi di onboarding.
A McKinsey (2023) study shows that organizations that systematically measure the impact of training are five times more likely to outperform their industry average in terms of performance.
What is the practical implication of this focus on data? For those like me who have studied the humanities, it is gratifying to see the evolution of the Learning & Development function from cost center to strategic business driver. L&D professionals are becoming data-driven learning architects, able to read indicators, interpret trends, and guide business decisions. The LinkedIn Learning Report (2024) confirms that companies integrating analytical dashboards into HR-L&D processes reduce response times to new skill needs by 20%, improving alignment between training and strategic objectives.
KPI, governance, and the risk of Data overload
Extensive use of data also opens new challenges. Defining KPIs, as we have experienced firsthand, is an act of strategic balance: too many indicators create confusion; too few reduce analytical depth. A clear learning data strategy is needed—one that answers three essential questions: which decisions do we want data to support? which behaviors do we aim to influence? what value do we intend to create for the organization and its people?
Data governance is another crucial issue. Beyond regulatory compliance, it is necessary to develop a culture of transparency and security, so that data becomes a shared asset rather than a risk. The most insidious danger is data overload: an excess of information that paralyzes decisions. The most mature organizations prioritize quality over quantity, focusing on a few meaningful indicators capable of generating useful knowledge.
Leggere l’apprendimento per leggere il futuro
Learning analytics today embody the new intelligence of training. They are no longer simple measurement tools, but true architectures of meaning, capable of transforming experience into knowledge and knowledge into strategy. The training of the future will certainly be adaptive, predictive, and generative: an ecosystem that learns as people learn, fueling a continuous cycle of innovation and improvement.
Aware of this direction, at Viasky we are enhancing LMS-derived reporting with Business Intelligence systems capable of integrating data from other corporate IT solutions, thus providing a more complete and meaningful strategic view of training impact. It is an evolution that shifts analysis from the descriptive to the decision-making level, transforming data into levers for organizational growth.
Ultimately, numbers do not replace human judgment—they amplify it. They offer solid ground on which to base more informed choices, more targeted strategies, and truly effective development paths. And in an era when human capital is the main source of competitive advantage, being able to read learning literally means being able to read the future.
I believe the heart of the challenge lies precisely here: remembering that behind every data point there is a person, an experience, a story that deserves to be understood beyond the figures. Quantitative analysis gains value only when it intertwines with the ability to interpret the meanings, emotions, and motivations that drive human learning. Data then becomes not just a measurement tool but a language of listening—a bridge between what we know and what we can still learn as a community of thought and practice.
