Predictive modelling is set to revolutionise how organisations deliver L&D initiatives. HRD explores the latest developments and where the future might lead.
HR PROFESSIONALS are becoming more and more adept at utilising the insights gleaned from big data to bolster their decision-making capabilities. Talent management, recruitment and performance management are just three areas in which big data is being used, not just to underline past trends but to forecast ahead.
Now it’s time for L&D departments to get in on the act. Karlie Cremin, principal of Dynamic Leadership Programs Australia, says this is a relatively new development, especially in Australia. While some companies are embracing data insights, Cremin says there are unique challenges in the L&D arena, primarily because there is not as much pre-existing data on L&D activities.
“E-learning has to a certain extent started to change this; however, the correlations between that data and outcomes have not been completely established yet,” she says. “For this reason, it can be difficult for organisations to know where to start.”
A predictive tool
Perhaps the most exciting development regarding the use of big data to shape corporate L&D is predictive modelling. Predictive modelling is the process of taking a data set and utilising identified variables to model the probability of different outcomes. This allows users to draw correlations between variables and identify predictors within a data set. This then provides a platform from which to take strategic action.
There are several ways this can be used in the L&D space, as Cremin explains. “The easiest, and subsequently cheapest, application is using workforce data to predict skills need,” she says. “This then provides the roadmap for where you need to be investing to ensure that the organisation has access to the skills it needs, when it needs them.”
Beyond that, Cremin says the possibilities “are endless”. For example, predictive modelling can be used to tailor L&D programs to the individual user. It can also be used to forecast the effectiveness of a program, from which course conveners can make changes to ensure highest yield. HR practitioners can even utilise predictive modelling to identify high-performing candidates for early selection into leadership programs.
“These are just some of the uses we know of right now,” says Cremin. “I struggle to fathom the scope of predictive modelling in this space in the future. It will be huge.”
Adaptive, personalised career paths
Countless surveys and research papers have identified that employees value career paths and development plans – these are often cited as the most valuable retention tools for employers. Data insights are helping on this front, too, by providing HR with the ability to track data on an employee during their journey through their employment with the organisation. This can be used for predictive modelling around development plans and optimum times for investment, development and progression.
“What this means is that big data is already allowing development plans and the way individuals manage their careers to be highly customised to the individual, and highly adaptive to variables such as personality, stage of life and preferences in general,” says Cremin. “Big data is now giving us access to create an intersection between customising L&D activity for the needs of the individual, customising L&D activity for the organisation and predicting the point where both will have the highest utility. This is truly exciting.”
Even more revelatory is the potential for data insights to be used to hone training effectiveness. For example, these insights may reveal that learners tend to skip over or appear to be disengaged with one component of a course. Data captured by e-learning platforms can reveal huge amounts of information about a user’s interaction, learning preferences and often how that correlates to absorption of information and their use of critical thinking. This data can then very easily be used to piece together a course specific to the individual, which covers the same information as the course everyone else does. The difference, however, is that it’s customised to ensure the best learning experience for the individual.
“The implications of this, particularly for those who have not thrived in traditional learning environments, should really not be undervalued,” Cremin says. “This has the power to completely change how we view and approach education.”
Don’t forget the human element
Despite the exciting developments, Cremin is mindful of relying too heavily on the algorithms and machine learning that result from big data. This is especially the case if HR relies solely on these insights to make decisions about who is likely to benefit most from a training investment.
“I do worry about what we lose by taking direction from data, and overlooking the humanity in our workforce,” she says. “I would stress that data should always be used to inform, not to direct action. I don’t believe in completely writing off an individual because the data indicates that they are not going to be a top achiever. The most productive workforces are diverse workforces.”
She adds that while data can be used to predict which employees are likely to benefit the most from training – and machine learning and associated outputs are a highly efficient way to mine huge amounts of data – the outputs still need to be interpreted. “There will always be a requirement for us to look at the world in context and decide what all the information is telling us,” Cremin says.
Get on board now
There is also the critical question of return on investment for any learning initiative – but again, big data can potentially help.
Cremin has noticed that in many organisations in recent years there has been a reluctance to invest in items that are not strictly operational. This, of course, means that the burden for HR professionals to demonstrate ROI has increased significantly. The great thing about investing in big data is that it is reliably measurable.
“In short, it’s our belief that any organisation not investing in this now is going to be obsolete very shortly,” she says. “They will find it impossible to attract talent; they’ll find themselves without the skills to deliver core outputs, and will eventually become irrelevant to the marketplace. This is not discretionary spending for anyone with long-term goals.”
As for the future, Cremin says “the sky is the limit” for big data and predictive modelling in L&D. She urges HR to brace for an ever-increasing rate of change from this point onwards. Her personal hope is that leaders can utilise big data in a way that means their organisations are higher performing, and the individuals within them have a more customised career path that is meaningful for them.
“Of course, the ultimate would be that education becomes more and more accessible and adaptive to needs so that every single person has access to a quality education,” she says. “I’m really hopeful I will see that in my lifetime.”
GETTING CFO BUY-IN
Need to get an L&D initiative over the line with your CFO? Karlie Cremin suggests bringing their attention to three critical areas:
1 The cost of not taking action – highlight the ways in which the organisation is not getting a return on current activity and the opportunity cost of continuing on that way.
2 The value of the strategic currency of data – information is power. If you hold information on L&D that your competitors don’t, you stand to become an employer of choice with a workforce that becomes increasingly superior over time.
3 Data doesn’t occur in a silo – the data harvested and interrogated for L&D will have value for other areas as well. Through utilising a big data model, all areas of the business will thrive, and are better informed to make higher quality decisions.
Karlie Cremin is the Principal of DLPA Group of companies including Dynamic Leadership Programs Australia (DLPA) and Iedex
*This article was originally published in HRD Magazine 27th April, 2017
you to ask yourself – what would your business really be without your workforce? Maybe you should stop and thank them.