A Music industry learning launch 

 Challenge 

 I am an aspiring music producer and a software engineer that provides software - based services to   companies in the music industry . 

 While attending several music industry networking events I noted record label artist & repertoire   managers ( A&R ) have several common challenges . One of those challenges is appropriately handling   the influx of artists in seek of their big breakthrough . Listening to an artist ’s music sent via email is a   time demanding chore involving lots of manual operations . However , it is almost mandatory to listen   to the submissions as nobody wants to miss out on the next big thing in music . I noted this challenge   and set out to improve their workflow with a software solution . 

 Selection 

 Six record label A&R managers I know personally showed interest in the idea I pitched . If the idea   would work , they were willing to pay for the service . Prototypes were made to validate insights and   assumptions on the desired workflow for the software . There were several assumptions that needed   to be addressed with real - life data which can be acquired through a learning launch : 

 -   Artists will accept the software over sending regular email ; 

 -   The software will reduce the amount of manual operations required for a record label to   communicate with an artist ; 

 -   The automation of the communication process between the artist and record label shall   result in time savings for the record label . 

 Application 

 The first learning launch was set up to validate all three aforementioned assumptions . The software   was tested on a single record label website for two weeks . There was only one A&R manager to test   the service in order to reduce software development cost for the learning launch . 

 Insights from the first learning launch uncovered new ideas and assumptions about the true   customer need . A second and third learning launch took place in order to validate new assumptions .   The second learning launch took place with two record labels . The third learning launch was held   with all 6 record labels participating . 

 Additionally , for all learning launches qualitative feedback and quantitative feedback was collected in   the form of feedback from users ( A&R managers ) , end - users ( artists ) as well as software usage   metrics . 

 Insight 

 After each learning launch data was analyzed . The first learning launch got artists really excited ! With   the introduction of the new software the record label offered a guaranteed response . The record   label was indifferent to the result . Overall the A&R noted a slight decline in manual operations and in   time spent on communicating with artists . Software metrics showed no significant change compared   to the prior situation without the software . The A&R ’s feedback surfaced the need for an additional   feature to further simplify communication with the artist . This feedback warranted a second learning   launch . 

 The second learning launch took place at two record label websites . Artists using the service from the   website of the second record label where indifferent to the introduction of the software , seemingly 

 because there was no guaranteed response being offered . The additional feature which was   implemented changed software metrics drastically . The time A&R ’s spent on communicating with   artists dropped by 60 % . Both A&R ’s were very much impressed . A needle mover . 

 For the third learning launch several features were added . Most importantly – the feature to provide   a guaranteed response to every artist , improving overall satisfaction of the artists and pleasing all 6   A&R managers . 

 Approach 

 The way the A&R manager advertised my service drastically altered the perceived value for the end- customer ( artists ) . A clear and concise working agreement could have prevented dirty data during   the first learning launch . Running the first learning launch with 2 companies could have uncovered   this data discrepancy earlier in the process . Alternatively , if the record label would not have   advertised with a guaranteed response , getting a guaranteed response likely would not have existed   as a feature today . 

 Real - life data is critical in a design - thinking approach . Design thinking lends itself to software   development in that it allows you to quickly iterate over ideas and validating assumptions . The   iterative approach containing three consecutive learning launches allowed for quick alterations ,   validating newly identified customer needs and assumptions . 


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: A Music industry learning launch 

 Challenge 

 I am an aspiring music producer and a software engineer that provides software - based services to   companies in the music industry . 

 While attending several music industry networking events I noted record label artist & repertoire   managers ( A&R ) have several common challenges . One of those challenges is appropriately handling   the influx of artists in seek of their big breakthrough . Listening to an artist ’s music sent via email is a   time demanding chore involving lots of manual operations . However , it is almost mandatory to listen   to the submissions as nobody wants to miss out on the next big thing in music . I noted this challenge   and set out to improve their workflow with a software solution . 

 Selection 

 Six record label A&R managers I know personally showed interest in the idea I pitched . If the idea   would work , they were willing to pay for the service . Prototypes were made to validate insights and   assumptions on the desired workflow for the software . There were several assumptions that needed   to be addressed with real - life data which can be acquired through a learning launch : 

 -   Artists will accept the software over sending regular email ; 

 -   The software will reduce the amount of manual operations required for a record label to   communicate with an artist ; 

 -   The automation of the communication process between the artist and record label shall   result in time savings for the record label . 

 Application 

 The first learning launch was set up to validate all three aforementioned assumptions . The software   was tested on a single record label website for two weeks . There was only one A&R manager to test   the service in order to reduce software development cost for the learning launch . 

 Insights from the first learning launch uncovered new ideas and assumptions about the true   customer need . A second and third learning launch took place in order to validate new assumptions .   The second learning launch took place with two record labels . The third learning launch was held   with all 6 record labels participating . 

 Additionally , for all learning launches qualitative feedback and quantitative feedback was collected in   the form of feedback from users ( A&R managers ) , end - users ( artists ) as well as software usage   metrics . 

 Insight 

 After each learning launch data was analyzed . The first learning launch got artists really excited ! With   the introduction of the new software the record label offered a guaranteed response . The record   label was indifferent to the result . Overall the A&R noted a slight decline in manual operations and in   time spent on communicating with artists . Software metrics showed no significant change compared   to the prior situation without the software . The A&R ’s feedback surfaced the need for an additional   feature to further simplify communication with the artist . This feedback warranted a second learning   launch . 

 The second learning launch took place at two record label websites . Artists using the service from the   website of the second record label where indifferent to the introduction of the software , seemingly 

 because there was no guaranteed response being offered . The additional feature which was   implemented changed software metrics drastically . The time A&R ’s spent on communicating with   artists dropped by 60 % . Both A&R ’s were very much impressed . A needle mover . 

 For the third learning launch several features were added . Most importantly – the feature to provide   a guaranteed response to every artist , improving overall satisfaction of the artists and pleasing all 6   A&R managers . 

 Approach 

 The way the A&R manager advertised my service drastically altered the perceived value for the end- customer ( artists ) . A clear and concise working agreement could have prevented dirty data during   the first learning launch . Running the first learning launch with 2 companies could have uncovered   this data discrepancy earlier in the process . Alternatively , if the record label would not have   advertised with a guaranteed response , getting a guaranteed response likely would not have existed   as a feature today . 

 Real - life data is critical in a design - thinking approach . Design thinking lends itself to software   development in that it allows you to quickly iterate over ideas and validating assumptions . The   iterative approach containing three consecutive learning launches allowed for quick alterations ,   validating newly identified customer needs and assumptions .
### Output: 