The second semester offers students an interesting module which combines the utilization of data analysis tools with the practical measurement of an own-built data-collection prototype which is used in real life software development teams. In the module “Empirical Research Module and Effective Data Analytics” the students learn how they can build and use a Raspberry Pi single board computer and its sensors, to collect data from actual scrum teams to finally analyze the data to pull conclusions about influencing factors on the productivity. The Final Outcomes of the module were presented at IBM Watsons headquarters in Munich.
PROTOTYPING AND MEASURING
To learn more about the technical aspects of digitization, and how data can be used to improve business processes, the BE.Di students were challenged in a way that they had to come up with concepts how productivity of scrum teams could be measured. Once this was figured out, the students should develop and eventually build a prototype which would measure the data and write the code which would run the sensors and collect the data to further processing. During intense sessions, the students learned a lot about IT hard- and software as they were suddenly confronted with the challenge to develop an IT prototype, which most of them have never done before. To give this module more practicality and enforce the learnings, collaborations with actual IT companies were formed to measure their productivity of the scrum meetings and give them advice how they could improve it.
PRESENTATION @ IBM WATSON
The presentation of the final outcomes and findings of the module took place at IBM Watson to support the feedback of the Professors with business insights and the feedback of experts from the IT sector. IBM Watson is a newly founded subdivision headquartered in Munich, which is researching new IoT and AI concepts for different industries. The exceptionality of this department is that it gets to develop new concepts in a playful think-tank design approach with little pressure from hierarchies and financial success. Thus, this department should engage agile and innovative, unconventional development of new concepts which could be utilized by IBM as new products in the future.
As this Master’s program is all about developing new digital business models, ideas and products, IBM Watson is the perfect partner for gaining insight in the IT industry and getting feedback for the conducted data analysis of the students. The students were given the opportunity to present their findings of their conducted data collection and analysis at IBM Watsons department in Munich and were invited in their futuristic office.
A TOUR AT IBM WATSON
IBM Watson’s office is in a new futuristic skyscraper in the North of Munich. The department is packed with cool IoT devices and concepts, as well as many showrooms and meeting areas. The students were amazed by the open and stylish concept of the office and couldn’t dare to stop smiling. After a quick introduction and briefing, a representative of IBM Watson gave the students a tour and insight into IBM and their products it is developing at the moment, or which have already been finalized. Self-watering miniature gardens, a personal AI driving assistant for BMW, a smart fridge and smart wearables were just a couple of the IoT products. Another highlight of the tour was a small robot setup, which used a Raspberry Pi to sort out malfunctioning mirrors for cars in an assembly line. It was interesting to see for the students what else could be done with these miniature computers. As the students just used the Raspberry Pis for simple data collection, they were amazed that it could be even used for car manufacturing.
After the tour the students presented the outcomes of their studies to IBM and the Professors. The huge presence of at least 4-6 IBM employees was very honoring and motivated the students even further to make a great presentation of their work. As the course was split up in different teams, which would work on different parts of the scrum agile project management (e.g. Team Review, Pair Programming etc.), the separate teams presented their results of the module. It was highly interesting to see how differently the teams formulated their business problems and hypothesis, how they build the prototype and how they measured and analyzed the data. After the teams presented their findings and suggestions for improvement they answered the questions which were asked thoroughly by the IBM workers who also had lots of valuable input to give.