Big Data Can Create Big Learning
Using data to inform learning strategy promotes agility in addressing business needs, feeds employees desire for feedback and coaching, and creates leadership pipelines.
Learning leaders tend to look at skills requirements from either a business needs perspective or in response to demand generated from line managers. Yet in the age of big data, this approach does not go far enough. Big data is transforming businesses and helping them become more responsive to customer needs, and it can have an equally high effect on corporate learning and development.
Chief learning officers already know coaching and feedback is important, and that motivation is necessary to engage learners. This is particularly the case as organizations increasingly look to e-learning to deliver cost-effective and flexible training. According to Speexx research, some 83 percent of corporate learners still appreciate coaching and feedback provided by a trainer or manager, while 84 percent of corporate learners consider a kick-off session, perhaps with a line manager introducing the e-learning training course, to be useful (Editor’s note: The author works for Speexx). The human factor remains key, and learners continue to benefit from ongoing support. Add big data into the mix, and CLOs have a much better idea where and when to provide feedback and coaching and what precisely will motivate learners.
Data also shapes more agile learning strategies. In the past, learning was pushed out to the workforce — the process was very much one-way. Now, there are several actions enterprises can take to incorporate big data benefits into global workforce development plans.
First, maximize talent platforms to provide more actionable learner intelligence. Collecting multiple types of learner data and feeding them meaningfully into a central talent or learning management system has been a challenge in the past. Now, learning content providers are stepping up to develop custom APIs that will interface with learning systems in a way that goes beyond the standard interface view of learner data. Standards for e-learning system interoperability — such as SCORM, or Sharable Content Object Reference Model — did not meet the needs of the big data environment. However, in its new incarnation as the Tin Can API SCORM, CLOs can collect data from a variety of employee learning experiences, both online and offline, in a consistent format that can be used for big data analysis.
Most organizations can then evolve their current talent platform to integrate big data to identify skill gaps across the organization before providing prescriptive learning for individuals. This way, organizations can shape a career path for learners more accurately based on their results in different training sessions and assessments. Big data can identify weak points for development by looking not only at raw test scores but also at the rate that the learner undertook and completed a course and which bits they struggled with.
The opportunity for uncontrolled importing and exporting of personal data in learning and development or talent management platforms does have implications for data protection compliance and conformance with Health Insurance Portability and Accountability Act rules, but the market is maturing fast. Most cloud-based, global learning and development software providers can help organizations navigate rules and regulations in the U.S. and internationally.
Big data also can support global organizations as they seek to roll out consistent learning worldwide. Multinationals need communication skills to enable employees to communicate across borders with each other, with partners and with customers. Employees generally welcome the opportunity to develop these skills using an e-learning platform — 89 percent of corporate learners appreciate the flexibility of online language training, and 80 percent of corporate learners report an overall positive outcome from their online language training — and big data can underpin that. A key component of effective global communications is a need for a common glossary. Big data helps to identify patterns that will support the standardization of terminology, which is crucial for large multinational manufacturers or sectors where a common terminology is key to safety or compliance.
Finally, learner data can inform succession planning. A large company can use data from regular placement tests across its workforce to create a pool of high performers it can then feed into an internal mobility program. Then, assessment data on technical and soft skills could be combined to create a talent pool for leadership. This means if CLOs are able to incorporate some of these tactics to integrate big data into their learning strategy they will enable workforce learning that is far more flexible and responsive than has been possible before.