Let the data work for you
The arguments above are clear: Learning & Development need to become a strategic partner to the C-suite and the business strategy. Moreover, L&D requires a personalized approach with a focus on the learner experience and including content in the flow-of-work. Unless each corporate L&D team significantly grows in size, these requirements for future proofing the L&D function are only possible by leveraging one often overlooked source of organizational knowledge readily available: data. If applied and analysed correctly, data can be a game changer within the functioning of corporate L&D.
Firstly, every well-developed L&D strategy has key performance indicators to evaluate the progress relative to the predefined L&D target goals. Often companies already use status-based metrics such as completion rates and attendance statistics to make their KPIs measurable. This is a good way to analyse the delivery progress within the L&D silo. Nevertheless, to rightfully step in as a strategic partner, KPIs need to evolve to indicators showing how L&D strategy contributes to the business performance. For this, a complementary use of longitudinal, outcome-based metrics, such as analysing the impact on team sales numbers and employee performance, is crucial.
Secondly, the role of data within learner experience is crucial. Most companies already made a considerable part of the investment: deploying an AI driven content library, often connected to or integrated into their Learner Experience Platform (LXP). But why does your costly, supposedly end-user friendly, AI driven system still does not offer relevant content for your learners? Although AI-based technology is evolving rapidly, algorithms are still evolving and, therefore, AI is only as good as the data it uses. For this reason, the focus should not only be on choosing which L&D technology has the most features, but also on how to populate it with the relevant data. To give a very concrete example: having an LXP that can give content recommendations based on (future) job profiles but not feeding the system with characteristics about the actual job profiles of your employees is an often seen missed opportunity. This is only one example out of many on how data can support your employees in what to learn at which moment, while bridging the gap between your employees' skills and the knowledge the organization needs.