Teaching data science to team for improving product quality  - Learning launch

Challenge:    I am a part of a wealth team in a fintech organization. Our team is supposed deliver a product  which will empower our customers to manage their personal finances better. Our product has  been in the market for a long time, but we realized that we are quickly losing our market share  due to better quality product coming up in the market.  For years we have been working on this  product but we realised that it will not be possible to improve the product further with traditional  technology. Our challenge is to enable our team with advanced data science based technique  so that we can improve our product to meet the customer satisfaction.     Selection:    To solve the challenge above, we are using learning launch as a tool of design thinking.  Learning launch was a perfect suit for us because we had an idea to train our team for data  science but it was something very new for us. We had assumptions that data science will solve  our problems but we were not sure. So as mentioned in the last module of the course, we  cannot rely on our own assumptions and we needed to test whether our idea will work or not.  For these kind of scenario, learning launch is perfect tool.    Application:    As, mentioned in the video of learning launch, the key part to validate any idea is to have data  supporting the idea. We needed to go to data science practitioners and figure out whether our  problem can be solved using data science techniques. We needed to find data which supported  our idea. Simply relying on our belief makes us vulnerable to confirmation bias. We contacted  different consultancy firms to find the evidence of our problem being solved by data science  techniques and indeed we found confirming data that our product can be improved with data  science techniques. We then hired corporate trainers and organized 4 week long training for our  team to enable them to implement the solution of our improved product.    We started encouraging team members to attend different data science related workshops and  conferences. We hired few data science experienced people in our team.    Insight & Approach:    By applying the learning launch tool, our team identified the weakness of our team and then  devised solution to strengthen the team. We made several assumptions and using learning  launch, we thoroughly identified those assumptions and validated them. We introduced training  our teams for data science and that made lot of difference not only to quality of our product, but  also to the overall growth and revenue of the company as well. The major insight that we

achieved from this experiment is that it is extremely necessary to keep the team updated with  the latest technologies coming to the market.    The alternate solution to our problem would have been to hire a consultancy firm to figure out  the problem with our team and have them fix it. But with learning launch, we were not only able  to identify the problem, but also effectively solve it.


Teaching data science to team for improving product quality - Learning launch Challenge : I am a part of a wealth team in a fintech organization . Our team is supposed deliver a product which will empower our customers to manage their personal finances better . Our product has been in the market for a long time , but we realized that we are quickly losing our market share due to better quality product coming up in the market . For years we have been working on this product but we realised that it will not be possible to improve the product further with traditional technology . Our challenge is to enable our team with advanced data science based technique so that we can improve our product to meet the customer satisfaction . Selection : To solve the challenge above , we are using learning launch as a tool of design thinking . Learning launch was a perfect suit for us because we had an idea to train our team for data science but it was something very new for us . We had assumptions that data science will solve our problems but we were not sure . So as mentioned in the last module of the course , we can not rely on our own assumptions and we needed to test whether our idea will work or not . For these kind of scenario , learning launch is perfect tool . Application : As , mentioned in the video of learning launch , the key part to validate any idea is to have data supporting the idea . We needed to go to data science practitioners and figure out whether our problem can be solved using data science techniques . We needed to find data which supported our idea . Simply relying on our belief makes us vulnerable to confirmation bias . We contacted different consultancy firms to find the evidence of our problem being solved by data science techniques and indeed we found confirming data that our product can be improved with data science techniques . We then hired corporate trainers and organized 4 week long training for our team to enable them to implement the solution of our improved product . We started encouraging team members to attend different data science related workshops and conferences . We hired few data science experienced people in our team . Insight & Approach : By applying the learning launch tool , our team identified the weakness of our team and then devised solution to strengthen the team . We made several assumptions and using learning launch , we thoroughly identified those assumptions and validated them . We introduced training our teams for data science and that made lot of difference not only to quality of our product , but also to the overall growth and revenue of the company as well . The major insight that we achieved from this experiment is that it is extremely necessary to keep the team updated with the latest technologies coming to the market . The alternate solution to our problem would have been to hire a consultancy firm to figure out the problem with our team and have them fix it . But with learning launch , we were not only able to identify the problem , but also effectively solve it .