Magazine excerpt: Learning by numbers
Carolyn Blunt looks at how to use data to provide bigger impact on the business and colleagues.
From customer insights to back-office processes, organisations in every sector are currently investing in techniques and technology that help them learn from data. Yet when it comes to L&D itself, the same principles don’t always seem to apply.
Businesses keep running programmes, commissioning e-learning or sending their people on courses while making little or no effort to use big (or even little!) data to truly track performance outcomes. Beyond the obligatory post-course evaluations, there’s often minimal attempt to use data to evaluate the impact of training on individual or organisational performance.
While Learning Management Systems support the collation of some basic data to demonstrate who applied and what the feedback scores were – “look how much they all enjoyed it” – few organisations are able to assess the effectiveness of the L&D they offer in any meaningful way.
Equally, decisions about provision are typically made in two ways: the first is ‘sheep dip’ mandatory training for all, whether for compliance reasons, when new kit is available or there’s a strategy or brand change to communicate. The second is often based around individuals identifying their own training needs as part of an annual assessment.
Actual analysis of individual learning needs on a meaningful scale is now being done with technology. Our primary focus is in customer conversation management skills, something that can be recorded, analysed and flagged by a machine at scale and with speed.
The case for data-driven L&D
None of us wants to provide training simply for the sake of it: we want to know what training is genuinely needed and where we’re driving positive change. That in turn helps us to understand what aspects of the solutions really do work and how we can improve our interventions and materials to increase their impact.
Beyond the obligatory post-course evaluations, there’s often minimal attempt to use data to evaluate the impact of training on individual or organisational performance.
From the business perspective, the case for demonstrable results is equally persuasive: while many L&D budgets continue to be ring-fenced from the efficiency savings other functions have to make, there’s a growing pressure to reduce the amount of time spent on training and away from core activities.
More worryingly still, a recent Deloitte survey found that support from colleagues for traditional L&D is ebbing away. In the study, involving over 700 business and HR professionals, the Net Promoter Score for corporate L&D was a jarring -8.1
Faced with such challenges, the complacency that leads to the suggestion that “there is no such thing as big data in HR”2 is surely outdated. Instead, it’s time for L&D teams to rethink their approach to using data, and the technology that helps us collate and apply it.
There are three broad areas where data can – and should – be applied to learning:
- To help measure the impact of training on individual performance and on the organisation’s performance.
- To provide tangible evidence of training needs and inform course design or coaching sessions.
- To enable personalisation and a more flexible approach to L&D.
Embracing a data-driven culture
The three use cases I’ve examined here are intrinsically linked: using data to analyse needs feeds into more personalised learning which, almost inevitably, will have a bigger impact on each member of the team – and the business as a whole.
At the heart of all three is the sense of a data-driven culture; one which is already on the road to digital transformation and which has access to the technology or capability to gather insights from data to drive the positive change.
In a majority of organisations, there are pockets of such a culture: perhaps in the back office, perhaps in CRM. Few, as yet, are in HR or L&D; it’s time we changed that.
About the author
Carolyn Blunt FCIPD is MD of Ember Real Results. Next month, Carolyn will explore using big data in sales.
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