Learning Launch 

 Challenge     I work for a company which provides business information via a Web platform .   Currently , almost all customers , who are information consumers , access this Web platform   ( Software as a Service , SaaS ) to perform queries ( based on the input keyword(s ) ) and get   results about their industry . Those results are rendered as text data in different sections in a   Web page including facets / filters to potentially narrow the results set to more specific ones .   The challenge lies in the fact that customers are reporting that it may be cumbersome and   time - consuming to read all the sections and that it 's not always straightforward to derive   knowledge from all the results . How to help customers be more efficient in digesting the   returned data ?   In addition , my company provides a set of Application Programming Interfaces ( APIs ) to   " consume " the information elsewhere . Indeed , sometimes customers need to combine their   internal data with the ones from my company and , in such use cases , APIs are a convenient   way to achieve this goal .   My assumption is that we can solve some of raised pain points by providing pre - computed or   pre - digested information through visual dashboard in addition to text sections using our own   set of APIs along with some graphical libraries .     Selection     I decide to use the learning launch process to test my idea . Why using the learning launch ?   Because , in my opinion , it is the best - suited design thinking tool to test such an idea . Indeed ,   it allows to implement , quickly and at low cost ( a couple of days of a developer and a Web   designer time , definitely less than a week ) , a Minimum Viable Product ( MVP ) , in our case a   Web dashboard , to check whether or not displaying the information visually would be   beneficial to existing customers and , why not , attract prospects .     Application / Insight     First , to bypass my confirmation bias , I conducted a survey among our existing customer 's   base ( both Small and Medium Enterprises ( SMEs ) and large companies ) who reported the   pain points mentioned above . With members of different departments , we also visited some   customers to understand their use of the current Web interface and how they derive insights   from the data returned . Observation is really the best way to learn how to improve such user   interface ! The goal was to get different points of view or perspectives of the customer 's   journey . We were also looking for confirming ( or disconfirming ) facts from customers to   validate ( or not ) my assumption that a dashboard would ease the process of getting insights   from data and therefore match some of the customer 's needs such as take better - informed   data - driven decisions quicker . The collected data demonstrated that the two categories of   customers were looking for such kind of visual insights . This approach has been useful to look   for the truth ( the customer 's voice / validation ) . I 'm now confident that it 's worth testing my   assumption and not only because I 'm convince that it 's a good idea ;) 

 The adoption of the MVP has been immediate ! Being able to test an idea with this learning   launch process is really invaluable . Having the customer 's positive feedback in such process   was promising but not enough . It was n't " tangible " . The quick and " cheap " MVP really helped   customers envision what they would get in the official product ( the one with text sections   only ) . It also generated feedback to improve the MVP which is the best way to really target   customer 's needs .      Approach     Lessons learned from this exercise include the necessity of putting the customers at the   center of all decisions . Ideas must be validated by customers to avoid redo " mistakes " and   ensure we provide as soon as possible the best user interface , in our example , but more   broadly , the best service possible . The service that will match what customers really need and   not the one what we think customers need . Having the chance to visit customers and see   them interacting with the product is the best way to learn how to best match their   expectations . Then , being able to quickly test and iterate thanks to their feedback is a   complementary required step to success . 


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: Learning Launch 

 Challenge     I work for a company which provides business information via a Web platform .   Currently , almost all customers , who are information consumers , access this Web platform   ( Software as a Service , SaaS ) to perform queries ( based on the input keyword(s ) ) and get   results about their industry . Those results are rendered as text data in different sections in a   Web page including facets / filters to potentially narrow the results set to more specific ones .   The challenge lies in the fact that customers are reporting that it may be cumbersome and   time - consuming to read all the sections and that it 's not always straightforward to derive   knowledge from all the results . How to help customers be more efficient in digesting the   returned data ?   In addition , my company provides a set of Application Programming Interfaces ( APIs ) to   " consume " the information elsewhere . Indeed , sometimes customers need to combine their   internal data with the ones from my company and , in such use cases , APIs are a convenient   way to achieve this goal .   My assumption is that we can solve some of raised pain points by providing pre - computed or   pre - digested information through visual dashboard in addition to text sections using our own   set of APIs along with some graphical libraries .     Selection     I decide to use the learning launch process to test my idea . Why using the learning launch ?   Because , in my opinion , it is the best - suited design thinking tool to test such an idea . Indeed ,   it allows to implement , quickly and at low cost ( a couple of days of a developer and a Web   designer time , definitely less than a week ) , a Minimum Viable Product ( MVP ) , in our case a   Web dashboard , to check whether or not displaying the information visually would be   beneficial to existing customers and , why not , attract prospects .     Application / Insight     First , to bypass my confirmation bias , I conducted a survey among our existing customer 's   base ( both Small and Medium Enterprises ( SMEs ) and large companies ) who reported the   pain points mentioned above . With members of different departments , we also visited some   customers to understand their use of the current Web interface and how they derive insights   from the data returned . Observation is really the best way to learn how to improve such user   interface ! The goal was to get different points of view or perspectives of the customer 's   journey . We were also looking for confirming ( or disconfirming ) facts from customers to   validate ( or not ) my assumption that a dashboard would ease the process of getting insights   from data and therefore match some of the customer 's needs such as take better - informed   data - driven decisions quicker . The collected data demonstrated that the two categories of   customers were looking for such kind of visual insights . This approach has been useful to look   for the truth ( the customer 's voice / validation ) . I 'm now confident that it 's worth testing my   assumption and not only because I 'm convince that it 's a good idea ;) 

 The adoption of the MVP has been immediate ! Being able to test an idea with this learning   launch process is really invaluable . Having the customer 's positive feedback in such process   was promising but not enough . It was n't " tangible " . The quick and " cheap " MVP really helped   customers envision what they would get in the official product ( the one with text sections   only ) . It also generated feedback to improve the MVP which is the best way to really target   customer 's needs .      Approach     Lessons learned from this exercise include the necessity of putting the customers at the   center of all decisions . Ideas must be validated by customers to avoid redo " mistakes " and   ensure we provide as soon as possible the best user interface , in our example , but more   broadly , the best service possible . The service that will match what customers really need and   not the one what we think customers need . Having the chance to visit customers and see   them interacting with the product is the best way to learn how to best match their   expectations . Then , being able to quickly test and iterate thanks to their feedback is a   complementary required step to success .
### Output: 