Learning trends

How big data is changing corporate learning for the better

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Big Data

With the rise of Web 2.0, social media, smartphones, cameras, and the Internet of Things, the amount of data generated by the Internet has exploded. Experts estimate that 2.5 exabytes of data, the equivalent of 250,000 Libraries of Congress, were generated every day in 2016. And that number is growing exponentially, doubling nearly every two years.

In the HR field, HR data analytics should move from a niche group in HR to a business function in 2017. HR leaders’ interest in data, and their desire to correlate it to business performance is increasing significantly. Deloitte discovered that 39% of companies they surveyed this year want to correlate people data with business performance – this is 63% more than it was last year. Going even further, 9% want to use it to predict business performance – this is more than 125% over last year(1).

Simultaneously, the amount of data available is on the rise. Pulse surveys and feedback apps, along with on-the-spot assessments and digital learning, offer companies fast results, creating even more new data. As Deloitte’s Josh Bersin puts it, “Every program designed, every incentive roll out, every organizational challenge should be informed by data.”

So what does this mean for corporate learning? Just as people data is helping businesses get the most out of HR, it will also lead to big, positive changes in the learning field itself. People data will help build more relevant training initiatives, by allowing L&D managers to correlate learning and business performance, and ultimately to predict it. It will also allow for more effective learning experiences by suggesting the most relevant courses to learners, and taking them to a new level of customization. Ultimately, this data will be the stepping stone to building learning communities and networks, and making experts more visible.

The Brand New World of Data

The use of data can help implement the “Netflix method” into learning – this means offering learners the most relevant content based on what they have already viewed. Combined with the “Amazon method” (suggesting courses based on what other viewers of the same content have also viewed), learners will dive into an immersive, personalized, relevant learning experience. They will be able to navigate within their learning environment in the same way they do when shopping or watching movies online.

Not all learners learn in the same way – sensibility to visual or auditory stimuli, duration of courses and interactivity can have a huge impact on the ways learners absorb new knowledge and are able to apply it in their daily jobs. Through cross-analysis of data based on results from assessments and learning patterns, it will be possible to identify the learning style of every individual, and to offer them the learning experience best suited to their needs. By adapting to individual learning styles, and thus further personalizing the learning experience, L&D departments will see completion, engagement and retention surge. The next step will be to bring together people who share the same learning patterns, going beyond job title, department and region. These new kinds of learning communities will open the door to a new form of collaboration that will go beyond the traditional perception of teams and cohorts.

The last, and maybe most critical impact that data can have on learning has to do with predicting the level of skills acquired by learners and, in turn, predicting performance. Predictive analysis combines statistics, machine learning and modelling techniques to create a model able to make estimations about the future. Using predictive analysis in decision making processes can allow an organization to base decisions on data and not on intuition. By cross-analyzing learning patterns with survey data, organizations can predict which skills a learner has mastered based on how they behave on the platform. This provides crucial information on how to adapt development plans, but also on who might be ready for their next challenge in a new position. Finally, by cross-analyzing learning data with data from Human Resource Information Systems (HRIS), feedback apps, pulse surveys, company dashboards and CRMs, organizations can predict individual and team performance.

The use of big data opens up a world of possibilities for L&D departments, but it’s also a substantial challenge. Current tools do not necessarily provide relevant data to L&D managers, and the L&D staff isn’t always trained to analyze data properly. Organizations can also prove to be resistant when it comes to generalizing data-based decision-making. It’s up to leaders to embrace this challenge to stay competitive, and to find a way to use the information available for the better.