Reflection – Learning Launch for Data Product Design Thinking 

 Challenge : 

 I am a member of a team that provides business intelligence and analytical solutions to the advisory   services team of a financial services firm . The advisory team published a list of recommended preferred   securities every week that is accessible to almost 4,000 financial advisors . 

 The challenges faced by the advisory team was that their members had to spend considerable amount   of time every week in collating , sorting and formatting data from Bloomberg before publishing the   recommended preferred securities list . Also , it was cumbersome for financial advisors to view and filter   preferred securities from the basic published Excel file as per particular criteria such as coupon rate ,   yield to worst , tax considerations , ratings and outlook categories . 

 Selection : 

 Learning launch tool enables engaging potential customers in the creation , design and testing of new   ideas as soon as possible ; and learning as you go , iterating constantly for improvement . 

 Our team used the learning launch tool as it is the ideal technique to ideate , create prototypes and test   various solutions in several cycles that will be created for the advisory team as well as the financial   advisors . This seems to be the best approach in applying design thinking for data products . 

 Application : 

 For the first learning launch , we underwent two rounds of discussions with the advisory team . This   helped our team to be aware of the weekly updating process in detail . We also created a survey and   distributed it to a sample of 200 financial advisors .   The survey helped our team in understanding in   detail the pain points faced by the financial advisors in accessing the published files . 

 For the second learning launch , our focus was to understand and collate the concern areas faced by the   members of the advisory group individually in their weekly tasks . We also distributed a second round of   survey to a sample of financial advisors , this time to gather various preferences / wish list in terms of user   experience . 

 Insights : 

 By applying the learning launch tool , we identified the need to create an advanced search tool solution   for the financial advisors . This search tool will enable the financial advisors to view preferred securities   information in detail as per their selection criteria . The criteria can be rating or outlook categories , bond   prices or tax considerations . The insight will help our team in developing the advanced search tool   prototype . This incorporation of insight will help in future launch designs in order to improve the user   interface and user experience . There is a scope of saving 60,000 hours annually by implementing an   advanced search tool solution . 

 The application of learning launch tool has also been beneficial in understanding the issues faced by the   advisory team in their weekly process of handling preferred securities data from Bloomberg . We have   identified the need to create an interactive uploader tool that will help in re - engineering the complete   manual processes of the team and save considerable time every week . The business process re- engineering will potentially save 250 - 300 hours every year for the advisory team . 

 Approach : 

 Learning launch seems to be the most appropriate tool for the mentioned project challenge . After   enough research has been completed through the learning launch , we can assure ourselves of the sound   fundamentals of the new idea and move to staging a roll - out of the idea . 

 Our team will assess the insights for the two - fold solution from our learning launches and gather   feedback to further the solutions in terms of user experience , user interface and other benefits . The   learning launch tool can also be further implemented to create a similar solution for the Equity research   team as well . Also , learning launch approach can be used for some of our future Big Data solutions too . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Reflection – Learning Launch for Data Product Design Thinking 

 Challenge : 

 I am a member of a team that provides business intelligence and analytical solutions to the advisory   services team of a financial services firm . The advisory team published a list of recommended preferred   securities every week that is accessible to almost 4,000 financial advisors . 

 The challenges faced by the advisory team was that their members had to spend considerable amount   of time every week in collating , sorting and formatting data from Bloomberg before publishing the   recommended preferred securities list . Also , it was cumbersome for financial advisors to view and filter   preferred securities from the basic published Excel file as per particular criteria such as coupon rate ,   yield to worst , tax considerations , ratings and outlook categories . 

 Selection : 

 Learning launch tool enables engaging potential customers in the creation , design and testing of new   ideas as soon as possible ; and learning as you go , iterating constantly for improvement . 

 Our team used the learning launch tool as it is the ideal technique to ideate , create prototypes and test   various solutions in several cycles that will be created for the advisory team as well as the financial   advisors . This seems to be the best approach in applying design thinking for data products . 

 Application : 

 For the first learning launch , we underwent two rounds of discussions with the advisory team . This   helped our team to be aware of the weekly updating process in detail . We also created a survey and   distributed it to a sample of 200 financial advisors .   The survey helped our team in understanding in   detail the pain points faced by the financial advisors in accessing the published files . 

 For the second learning launch , our focus was to understand and collate the concern areas faced by the   members of the advisory group individually in their weekly tasks . We also distributed a second round of   survey to a sample of financial advisors , this time to gather various preferences / wish list in terms of user   experience . 

 Insights : 

 By applying the learning launch tool , we identified the need to create an advanced search tool solution   for the financial advisors . This search tool will enable the financial advisors to view preferred securities   information in detail as per their selection criteria . The criteria can be rating or outlook categories , bond   prices or tax considerations . The insight will help our team in developing the advanced search tool   prototype . This incorporation of insight will help in future launch designs in order to improve the user   interface and user experience . There is a scope of saving 60,000 hours annually by implementing an   advanced search tool solution . 

 The application of learning launch tool has also been beneficial in understanding the issues faced by the   advisory team in their weekly process of handling preferred securities data from Bloomberg . We have   identified the need to create an interactive uploader tool that will help in re - engineering the complete   manual processes of the team and save considerable time every week . The business process re- engineering will potentially save 250 - 300 hours every year for the advisory team . 

 Approach : 

 Learning launch seems to be the most appropriate tool for the mentioned project challenge . After   enough research has been completed through the learning launch , we can assure ourselves of the sound   fundamentals of the new idea and move to staging a roll - out of the idea . 

 Our team will assess the insights for the two - fold solution from our learning launches and gather   feedback to further the solutions in terms of user experience , user interface and other benefits . The   learning launch tool can also be further implemented to create a similar solution for the Equity research   team as well . Also , learning launch approach can be used for some of our future Big Data solutions too .
### Output: 