Design Thinking Final Assignment

Chad Knipschild

Challenge    My work includes leading a small team of global threat analysts. We produce risk analysis for  military applications and operations around the globe. Our processes are flexible, however with  many recent changes to technology, our training is outdated. Our training pipeline does not  adequately reflect the tools and skills used while on the job. This has become especially  apparent with new hires, since they do not have the experience of the other analysts who have  served in the positions for years. We as a team want to figure out the most adequate and  efficient training methods to adapt to the new tools and technologies, enhancing our ability to  quickly provide the most relevant threat analysis to our customers.    Selection    This problem, I thought would be best suited for a Learning Launch. We have a very tight  timeline, and unfortunately fly the plane while we build it. Thus, Learning Launch would allow  my team to quickly test a few hypotheses, gain customer feedback and decide for on a new  training pipeline quickly.    Application    I started the processes with feedback from the analysts and our customers. I simply asked, what  data sets do we need to be more familiar with? A slew of answers came back. Two experienced  analysts and I sat down to sift the lists. We brainstormed a few different methodologies to  address the data sets. Some we came up with included, external classroom training, allocated  time for mentoring assisted with on-the-job, and internal, once per week training on each topic.      We wanted feedback immediately, so we found a school to send one of the analysts to for one  of the topics the following week. Sent another to conduct analysis with no training to the  customer to learn on the job, and simultaneously internally training a final analyst on a topic  over the course of two-three hours. At the end of the week, we brought them all together and  had them have a discussion with the customer. It was evident that the analyst who went to the  class had the most in-depth knowledge. However, they had been gone from our shop unable to  do any administrative work for three days. Our internally trained analyst came very close to the  classroom trained analyst in terms of depth of knowledge and had been able to complete his  administrative functions while helping a separate customer on the side. The analyst who  trained on the job, had the best relationship with the customer on a personal level, however  still lacked in-depth knowledge of the data sets, and still was not able to get all of the  administrative pieces complete.

We concluded the most efficient means of training was internal sessions based off previous  experience. If there is a project in which we need to go very deep, we can opt to send someone  to a school to enhance their knowledge even further. On the job training      Insight & Approach    What I have learned in a scientific approach, conducted quickly and inexpensively to gather our  data to determine whether our training methodologies have enough merit to warrant detailed  investigation with the commitment of further time, people and resources. Upon completion of  our experiment, we have dedicated training time weekly, where an analyst presents a topic  each week, describing their experiences with data sets. New members if they have no  experience, will choose an interest of theirs and present.     Sprinting to a new training methodology using design thinking has been tremendously helpful.  Looking back, I wish we had invoked more customers to enhance our ability to simultaneously  prioritize which data sets need to be trained on vice just our methodology. However, that could  also be the next step in a hypothesis driven experiment or if given more time could be used  through storytelling methodology of the customer.


Design Thinking Final Assignment Chad Knipschild Challenge My work includes leading a small team of global threat analysts . We produce risk analysis for military applications and operations around the globe . Our processes are flexible , however with many recent changes to technology , our training is outdated . Our training pipeline does not adequately reflect the tools and skills used while on the job . This has become especially apparent with new hires , since they do not have the experience of the other analysts who have served in the positions for years . We as a team want to figure out the most adequate and efficient training methods to adapt to the new tools and technologies , enhancing our ability to quickly provide the most relevant threat analysis to our customers . Selection This problem , I thought would be best suited for a Learning Launch . We have a very tight timeline , and unfortunately fly the plane while we build it . Thus , Learning Launch would allow my team to quickly test a few hypotheses , gain customer feedback and decide for on a new training pipeline quickly . Application I started the processes with feedback from the analysts and our customers . I simply asked , what data sets do we need to be more familiar with ? A slew of answers came back . Two experienced analysts and I sat down to sift the lists . We brainstormed a few different methodologies to address the data sets . Some we came up with included , external classroom training , allocated time for mentoring assisted with on - the - job , and internal , once per week training on each topic . We wanted feedback immediately , so we found a school to send one of the analysts to for one of the topics the following week . Sent another to conduct analysis with no training to the customer to learn on the job , and simultaneously internally training a final analyst on a topic over the course of two - three hours . At the end of the week , we brought them all together and had them have a discussion with the customer . It was evident that the analyst who went to the class had the most in - depth knowledge . However , they had been gone from our shop unable to do any administrative work for three days . Our internally trained analyst came very close to the classroom trained analyst in terms of depth of knowledge and had been able to complete his administrative functions while helping a separate customer on the side . The analyst who trained on the job , had the best relationship with the customer on a personal level , however still lacked in - depth knowledge of the data sets , and still was not able to get all of the administrative pieces complete . We concluded the most efficient means of training was internal sessions based off previous experience . If there is a project in which we need to go very deep , we can opt to send someone to a school to enhance their knowledge even further . On the job training Insight & Approach What I have learned in a scientific approach , conducted quickly and inexpensively to gather our data to determine whether our training methodologies have enough merit to warrant detailed investigation with the commitment of further time , people and resources . Upon completion of our experiment , we have dedicated training time weekly , where an analyst presents a topic each week , describing their experiences with data sets . New members if they have no experience , will choose an interest of theirs and present . Sprinting to a new training methodology using design thinking has been tremendously helpful . Looking back , I wish we had invoked more customers to enhance our ability to simultaneously prioritize which data sets need to be trained on vice just our methodology . However , that could also be the next step in a hypothesis driven experiment or if given more time could be used through storytelling methodology of the customer .