Corporate Learning for Data Professionals: 4 things to consider to make the right decision
You got it! The board has made a decision and you now have the chance to train your data talents. Now what? How can you choose the best learning experience and assure your company the highest and fastest Return on Investment in Corporate Learning?
We’ve set up a framework of 4 pillars to help you make the right decision:
1.CONTENT
First thing you should consider is if the Program content is accurate, well-structured and curated by recognized experts in each subject. Technology has made it very easy for anybody to build, sell and distribute content online but not all the content is created equal …
2. LEARNING EXPERIENCE
Besides content, what makes learning effective and productive is the learning experience itself: the more interactive, the more effective. That’s why while choosing a learning service, you’ve got to make sure it is designed to remove engagement barriers and allows collaboration between peers and trainers.
3. MENTORSHIP
The only way to ensure the necessary student engagement and to promote a practical perspective to the learning process is to have experienced mentors bringing industry expertise into the classroom and solving real-world problems. Differently from MOOCs (Massive Open Online Courses) suffering from very low completion rates, 3% in some cases*, training with knowledgeable mentors has the huge advantage of delivering much higher engagement and completion rates.
4. LEARNING ANALYTICS
And what about performance monitoring? Surely this is something less common and more difficult to find among corporate training services, but yet definitely represents a huge differentiator: a training program with a strong Learning Analytics Platform allows you to get insights on students’ learning progress, tailored training approach, as well as periodical updates and interactions on performances.
We hope this framework will help you make an informed decision, and choose the most effective learning experience for your data talents.
Let us know what you think, leave your comment now!