Challenge   I am leading a product development team of a company specialising in building IT systems   for government entities . The acquisition of consumer data is fundamental to the system , in   order to build downstream application to support business functions of our customers .   Although the data is acquired through third party providers , using agreed methods , one of   our key customers has highlighted a concern about the poor quality of the data received   from the third parties and lack of systems tools to efficiently identify third parties which are   not complying with the data requirements .      Following an initial engagement with the customer to understand   their problems , we have   done some brainstorming internally to build a prototype for a data quality tool to assist   customers with identification of compliance issues and subsequent engagement with third   parties to address those issues in an efficient manner . The challenge is to build a solution   which is fit for purpose and can be deployed quickly for customer feedback . The portfolio   team has agreed to fund a 3 - month project with the relevant resources . Our sales team will   help us to organise testing of the first prototype with a key customer organisation . The sales   team will also help to us to demonstrate prototype to two other customers to build a   product roadmap for other customers .     Selection     We have decided to use the learning launch tool . This will help us build an effective tool that   our customer can use to monitor any data quality compliance issues . We have done some   initial definition of the problem with a customer and worked with our own subject matter   experts to come up with a prototype . The selection of the learning launch tool will help us   to quickly get feedback from wider customer stakeholders and run hands - on experiments   with the customers to test or revisit our assumptions we have made and to refine the   prototype in iterations .     Application   As part of our first launch , we focused on demonstrating a web browser based user data   quality prototype that provides user dashboards to highlight any errors found within the   consumer data or incomplete data received through 3rd party providers . The dashboard also   provides a set of preconfigured views for users to get insight about the type of issues found   and enables them to drill into the actual data if required . The customer users had an   opportunity to navigate through the prototype interface and provided their feedback about   the features . This was enormous insight for us to know that the users found the interface   very intuitive to use with no user training and they could see the value the data quality tool   brings to help them with the compliance process . As the key assumptions have been tested   to be true , we had a go ahead to move to   the next iteration of the learning launch . As an   output of the first launch , we prioritised features for the next iteration based on the value   they bring to the users .      The second launch was to introduce a notification service into the prototype to send   automatic email alerts to the 3rd party providers for any errors found in the data without 

 requiring a user to initiate an action . Moreover , we built a few additional views to meet user   needs . This learning launch tools helped us to confirm a new product concept with an active   customer to ensure it fulfils their needs without having to wait for the full product to be   ready before the customer can see it . The learning launch proved to be a powerful tool to   align our product development with market needs and making sure an interest from the   customer users .      After securing a commitment from the customer to buy this new data quality tool , we were   able to demonstrate the prototype to two other customers to get their buy - in , which   provided a strong boost for the go - to - market plan based on robust commercial model .      Insight   We applied the learning launch to design and test our products . Through customer   engagement and learning through customer feedback , we were able to build a product to   meet customer needs . It felt reassuring to test our ideas and assumptions with the   customer . It is through this process we were able to get some insight about some other   features users would like to see in the overall system which will be really useful for the   future growth of our relationship with the customer . As part of the project closure exercise ,   we documented some lessons learnt through this session which will be useful for future   application of the learning launch tool .      Personally speaking , I have learnt a new way to approach problem solving   and be open to   apply a growth mindset to come up with a solution for a problem .      Approach   The learning launch is the right tool for our business as it offers both flexibility and agility to   test new concepts with our customers before they are turned into   commercial offerings .   We are going to review the lessons learnt from two launches , in order to make sure any   future launches benefit from this experience . Broadly speaking , this insight will also improve   how other design thinking tools can be combined with the learning launch tool to build a   right - sized framework for solving business problems . 


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: Challenge   I am leading a product development team of a company specialising in building IT systems   for government entities . The acquisition of consumer data is fundamental to the system , in   order to build downstream application to support business functions of our customers .   Although the data is acquired through third party providers , using agreed methods , one of   our key customers has highlighted a concern about the poor quality of the data received   from the third parties and lack of systems tools to efficiently identify third parties which are   not complying with the data requirements .      Following an initial engagement with the customer to understand   their problems , we have   done some brainstorming internally to build a prototype for a data quality tool to assist   customers with identification of compliance issues and subsequent engagement with third   parties to address those issues in an efficient manner . The challenge is to build a solution   which is fit for purpose and can be deployed quickly for customer feedback . The portfolio   team has agreed to fund a 3 - month project with the relevant resources . Our sales team will   help us to organise testing of the first prototype with a key customer organisation . The sales   team will also help to us to demonstrate prototype to two other customers to build a   product roadmap for other customers .     Selection     We have decided to use the learning launch tool . This will help us build an effective tool that   our customer can use to monitor any data quality compliance issues . We have done some   initial definition of the problem with a customer and worked with our own subject matter   experts to come up with a prototype . The selection of the learning launch tool will help us   to quickly get feedback from wider customer stakeholders and run hands - on experiments   with the customers to test or revisit our assumptions we have made and to refine the   prototype in iterations .     Application   As part of our first launch , we focused on demonstrating a web browser based user data   quality prototype that provides user dashboards to highlight any errors found within the   consumer data or incomplete data received through 3rd party providers . The dashboard also   provides a set of preconfigured views for users to get insight about the type of issues found   and enables them to drill into the actual data if required . The customer users had an   opportunity to navigate through the prototype interface and provided their feedback about   the features . This was enormous insight for us to know that the users found the interface   very intuitive to use with no user training and they could see the value the data quality tool   brings to help them with the compliance process . As the key assumptions have been tested   to be true , we had a go ahead to move to   the next iteration of the learning launch . As an   output of the first launch , we prioritised features for the next iteration based on the value   they bring to the users .      The second launch was to introduce a notification service into the prototype to send   automatic email alerts to the 3rd party providers for any errors found in the data without 

 requiring a user to initiate an action . Moreover , we built a few additional views to meet user   needs . This learning launch tools helped us to confirm a new product concept with an active   customer to ensure it fulfils their needs without having to wait for the full product to be   ready before the customer can see it . The learning launch proved to be a powerful tool to   align our product development with market needs and making sure an interest from the   customer users .      After securing a commitment from the customer to buy this new data quality tool , we were   able to demonstrate the prototype to two other customers to get their buy - in , which   provided a strong boost for the go - to - market plan based on robust commercial model .      Insight   We applied the learning launch to design and test our products . Through customer   engagement and learning through customer feedback , we were able to build a product to   meet customer needs . It felt reassuring to test our ideas and assumptions with the   customer . It is through this process we were able to get some insight about some other   features users would like to see in the overall system which will be really useful for the   future growth of our relationship with the customer . As part of the project closure exercise ,   we documented some lessons learnt through this session which will be useful for future   application of the learning launch tool .      Personally speaking , I have learnt a new way to approach problem solving   and be open to   apply a growth mindset to come up with a solution for a problem .      Approach   The learning launch is the right tool for our business as it offers both flexibility and agility to   test new concepts with our customers before they are turned into   commercial offerings .   We are going to review the lessons learnt from two launches , in order to make sure any   future launches benefit from this experience . Broadly speaking , this insight will also improve   how other design thinking tools can be combined with the learning launch tool to build a   right - sized framework for solving business problems .
### Output: 