Several aspects of managing the generational divide in learning were discussed, and some great ideas were exchanged. For one of the research, we identified experimentation as a key core competency for innovation and digital transformation. The case study we looked at was Pitney Bowes. It is fascinating that this 100-year-old company has transformed over multiple years. Pitney Bowes built its business by providing mail machines to companies. With the decline of traditional mail, it looked like in early 2000, that they may not make it. But then something significant happened. They started trying several digital things without worrying about failure in the early 2000s. A few engineers attempted to build what was then called Click Stamps Online. This was a big failure but gave them the foundation to print shipping labels for eBay. From there, they started commanding huge market share for shipping label prints, and the rest, as they say, is history. Today Pitney Bowes continues to dominate in the shipping software and solution and various APIs associated with that. This is just a fascinating story, all enabled by a few people and a supportive culture. It is stories like this, emphasize the need for making experimentation and communicating that failure from experimentation is acceptable is very critical.
Bringing learning as a key competency requires out of the box thinking. Nobel Prize winner Richard Thaler states that in a system, we have to observe points of failure and address it creatively. A group of researchers from the University of Chicago conducted an interesting experiment. They selected a group of teachers and evaluated if teacher performance is better with financial incentives tied to the learning of students. The researchers designed an innovative bonus system. Rather than giving the bonus post students doing well, they provided this bonus ahead. A 5000 dollar bonus was given ahead and said that bonuses would be taken away if the students do not do well. The experiment was successful, but the teachers who lost the bonus were extremely unhappy. But it taught an interesting lesson. If the failure point of this model is known, maybe we can take better steps to address it (like proper communication or not deduct the full amount and additional mitigation steps). As you make the outcome more tangible and real to your team members, results start flowing in. In our opinion, HR should track these stories and also conduct some experiments along these lines to see how to create those transformational moments.
We are beginning to see Digital Transformation impacting many African Nations. In one of the previous emails, we spoke about Rwanda. But we also see good efforts by Ghana. Organizations like Northern Innovation Lab that encourages Digital Entrepreneurs are coming up in Ghana. We believe that all companies have to evaluate Africa as part of their location strategy. Places like Ghana, Kenya, Nigeria, Rwanda, Egypt are locations we should track. We have launched a significant study on Africa, and we will release this in mid-October (if you are interested, let us Source: Nothern Innovation Lab and Draup Interviews know, and we will send you the report). At the centre of the African tech, revolution is e-commerce and health care transformation. New funds are flowing in, and accelerators are coming in. Tech Talent is not abundant, but hunger to learn is really high. We are also tracking professors who are doing great work.
We are introducing a model called Skills Divergence Model. To explain this, let us look at this story. For a long time, Actuaries will not apply for a job that does have the word Actuary in the titles. This is because the jobs in the 80s and 90s largely had this word Actuary in the titles, and they are simply not used to any other titles. But the Tech companies changed that. An Uber driver (who is also an Engineer – let us call him Marvin) in Philadelphia was driving a passenger. In his app, Marvin gets an alert saying that he is braking excessively and that often results in poor customer rating. Marvin quickly course corrects and gets a great rating for that trip. But his Engineer background made him research on how Uber is doing this. Marvin study led him to Frank Chang’s Safety Analytics group at Uber. Chang started his life as an Actuary and slowly expanded his skills. Chang taught uber ratings on Zipcode are not valid as a driver in Philadelphia could be driving in NYC. He brought in variables such as Driving Style, Braking patterns, and several other interesting variables and leads a very large team of Actuaries and Data Scientists. So what just happened in Chang’s career? Chang did not change what he was doing as an actuary-merely expanded the points of application. This has a very significant application for all job roles. So our model will help you understand the Divergence in applications of your employability skills. Maybe we will discover something amazing in this process together.