Elearning! Magazine: Building Smarter Companies via Learning & Workplace Technologies.
Issue link: http://elmezine.epubxp.com/i/471607
22 February / March 2014 Elearning! consumerization actually funded by the enterprise. UL had two business lines for 50 years; as of this year, it has fve business lines. It was this kind of approach that helped the company jump-start new ideas. MATCH.COM is an online dating site. Surprisingly, 17 percent of all marriages are results of online dating sites. Te assumption is that if you can fnd your life partner with Match.com, why can't a company use the same algorithms to fnd the next great candidate or the next great employee? Even more than that, enterprises are using Match.com-style algorithms for internal purposes. Employees who are very knowledgeable but on the cusp of retirement are mentoring employees who want that same career path in virtual e-spaces instead of being in the same ofce. Te HR Department matches up these players, based on personnel profles. Tese types of algorithms also come in handy for individual projects. An example could be selecting an LMS. Tere are within a 50,000-employee organization maybe fve or ten people who might have that expertise. But when the responsible manager has to select an LMS for the frst time, having access to pertinent employee profles — with experiences and ratings — can identify who he or she can reach out to, in order to assist in that project. Some forward- thinkers in the learning profession even envision that, some day, employees, contractors and board members will help vote for the next CEO. WHERE'S IT ALL HEADING? All these new consumerizing technologies are really applying pressures on learning professionals to keep up. Organizations will have to fgure out where they are and where and what they want to be. Because all the behaviors and practices in the consumer space are now driving new learning technologies. Te algorithms that drive Netfix are the same kind of algorithms that can drive career- development components of a learning or talent system. Peer & Group Ratings *138 million visitors monthly *More than 61 million reviews Consumer uses: >> Restaurant ratings >> Restaurant locations >> Hotel ratings >> Customer reviews >> Star system Enterprise uses: >> Crowd-source feedback >> Performance reviews >> Performance improvements >> Star system Content Delivery *44 million users *More than 20,000 titles Consumer uses: >> Movie viewing >> Genres >> Peer and social recommendations >> System recommendations Enterprise uses: >> Career development >> Personalized learning content >> Peer and social recommendations >> Career roadmaps >> MOOC and content mapping to development goals Funding & Rewards *$1.3 billion in revenue *700,000 projects funded Consumer uses: >> Crowd-source funding >> Project launch support >> Partner awards Enterprise uses: >> Skill-building via time investments >> Gamifed and mobilized alerts for project recruitment Talent Recruitment *17% of all marriages from online dating *30% met on Match.com Consumer uses: >> Find your partner >> Single social parties >> Algorithm-based pairings >> Personalized dashboards with profles Enterprise uses: >> Mentor-protégé assignments >> Using experts within your company >> Vote for your next CEO