Who wants self-assembling curriculum, personalized for every employee in a company? What about a system that can tell leaders who and where their top talent is? Most leaders would be interested, but until now these things have been dreams. Not anymore. Today, these systems are not just possible but already available.
It’s about time, too. It has long been a vision for the learning industry to have large repositories of content that could be easily assembled on the fly and reused as needed to create great programs for learners. But for many organizations that goal has been hard to achieve. Some have succeeded, others have failed and many have given up.
Pushing out relevant content to users is also a challenge. Few learning leaders enjoy creating and updating competency models, and even fewer think their companies have perfect models for the jobs and roles within their organizations, if these models exist at all. Historically, competency models, job/role models, mentoring programs or any one of a number of expensive, time-consuming and politically complicated-to-implement peer models were the best way to push learning out to people in ways that were useful to their work. The models were hard to create, and even harder to maintain. Keeping the job/role model current as the business evolved or mapping new learning content or other relevant offerings to the existing competency model still can be painful these days because things are changing so rapidly.
Further, how does the learning leader identify experts, the rock stars who have great insight and knowledge to share? How does he or she pinpoint the most useful and valuable content?
If organizations are investing or have invested in expensive learning technology systems, they want to know they are pushing out the most up-to-date and relevant content available and that they can use these systems to easily identify top talent and resident experts as learning resources for the community at large to tap into.
Unfortunately, this is not always the way things go. Traditional models were slow to evolve and incorporate fantastic new content, much less have methods to identify resident rock stars and connect people to people in relevant ways.
It’s a New Day
Fortunately, things have changed. Thanks to big data and social learning — and its relevant crowd content creation and sharing — learning leaders can finally make the aforementioned vision a reality and solve some major pain points in the process.
Imagine real-time recommendations with the most knowledgeable and influential people and new content personalized not down to the competency or job, but right down to the individual.
It’s not a dream. Many of us have already experienced it. Many have used Netflix and gotten personalized film recommendations, or used Amazon.com and gotten purchasing recommendations based on what other buyers who bought the same item like. These recommendations are made possible by technologies that make it easy for companies to crunch massive amounts of data to identify patterns and turn those patterns into relevant recommendations for their users.
Big data powers social interactions, but leaders often take for granted the power behind informal interactions. Social learning can be as simple as an online discussion group or the community an instructor creates for a class. Or it can be a comprehensive companywide social enterprise deployment where the learning goes hand-in-hand with collaboration.
Regardless of size, enabling social learning systems provides a virtual goldmine of actionable insight. An organization that fails to tap into employees who contribute and submit ideas does not understand the potential social learning can offer.
Similarly, many human resources departments are stuck in an antiquated routine when it comes to recruiting and succession planning. They fail to recognize the role crowdsourcing and company-mined data and analytics can play in finding the right successor. This affects the learning leader who cannot adequately partner with HR peers to identify what talent holes need to be filled, which skills are required and what learning interventions may be necessary to develop them.
Some may wonder, if the idea of crowdsourcing is so promising, how can we turn personal, informal chatter into actionable data? This has been a struggle leaders have spent years trying to remedy, but with the modern office comes modern opportunities. Technological advances have changed the playing field, and the potential for real minute-by-minute interaction between employees — and between employees and their employers — is easier and better.
Guitar Center, a retailer with more than 260 stores across North America and 12,000 employees who are passionate about music, deployed software in January to boost informal learning. Now employees can interact and learn from each other more easily, which elevates engagement and improves job performance.
A post might read something like, “Hey, the rep from Fender was here and just told me a certain artist is using this guitar on his next tour.” With a companywide social learning network, now every rep can share that story with clients who show interest in Fender’s products in an effort to boost sales. It would be nearly impossible to include that kind of story in a formal learning curriculum.
How employees interact and connect with each other is now one of the cornerstones of corporate development and learning. While formal processes always will have their place, informal processes and interactions are becoming more important in developing curriculum and learning leaders’ efforts to facilitate career growth and learning.
Gathering data on these kinds of social interactions can be the learning executive’s greatest tool. Measuring influence, reputation and impact at work can identify performance levels, and by incorporating real-time feedback and social recognition, real-time performance reviews can have real impact. For example, Guitar Center’s social network tracks all contributions and measures which are the most read and liked. Not only does management have an unbiased metric on employee contribution, but also they can see which individuals are the hubs in the social learning network.
Peers also can recognize or badge each other and identify role models. Once a role model is identified, the system can look into the role model’s informal and formal learning activities and suggest classes, learning groups, etc., creating a curriculum to help an individual attain a particular skill set. This enables workers to set their own paths, realize their potential and become the rock stars they could be with the right development.
Companies are essentially using personalized social learning to do all the heavy lifting needed to weave together the fabric of collective insights. For example, in a social learning network like Guitar Center’s employees can input their informal thoughts and conversations on a day-to-day basis and share their feedback or insights on a topic. The system can note or flag relevant data points and connect the dots between the employer’s needs and the employee’s interests and capabilities.
Therefore, if a need arises in the organization, someone who may have been previously overlooked for a position can be top of mind thanks to agnostic data. This type of big data or machine learning allows the learning leader to evolve from an administrator role to one focused on actively unearthing potential and streamlining issues with tangible responses.
When companies gather data from these interactions, they also can measure influence, reputation and impact at work, and identify performance levels by incorporating real-time feedback and social recognition. Real-time performance reviews, rather than a formal annual or semiannual sit-down, are much more effective for the employee and employer. This type of system also allows individuals to recognize role models among their peers and helps departments to create social curricula based on attaining the same skill set as the role models.
As noted previously, machine learning has been used for years by companies like Amazon and Netflix to help their customers find products or movies that interest them. Human capital software vendors have taken the same concept and applied machine learning algorithms to career development. These systems can aid an employee’s career aspirations by recommending classes, content and potential mentors. They also aid talent processes such as succession planning by updating employee profiles and role model — and potential leader — identification.
Nothing New Under the Sun
Just as the advent of e-learning did not replace traditional face-to-face instruction, social and collaborative learning and top talent identification will not replace competency models and current succession planning practices, but they will augment them via self-directed learning and career development and online/automated, highly personalized mentors.
There’s something else to consider. The employee landscape continues to shift, and millennials will represent half of the workforce by 2020, according to the University of North Carolina’s Kenan-Flagler Business School. This generation has different workplace requirements than its predecessors. The learning industry needs to constantly innovate and be on the lookout for the next big thing, whether it be wearable technology, remote workplaces or BYOD policies.
As workforces continue to evolve and the nature of the workplace continues to change, attracting and keeping the best talent possible is even more important to a company’s survival. Now the learning industry has the technology to find out what employees are thinking and how the workplace affects what they do on a daily basis. The learning leaders who embrace this kind of innovation will create rock stars of high-performing employees and help their organizations to become global leaders.
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