Cristino Ramirez 

 29 May 2020 

 Challenge & Selection : 

 Our government customer was used to relying on proprietary data collection and analysis tools   specifically tailored for the government operating environment .   Over time , the government customer   realized that commercial solutions offerings existed that could provide highly accurate data analysis to   enhance their knowledge and awareness of their operating environment at a very affordable cost .    When compared to designing and acquiring their own proprietary system , the government customer   realized that our commercial solution could have the potential to enhance their capabilities , but were   interested in understanding how our data analysis system could be integrated into their normal daily   operations . 

 Selection : 

 I chose the “ Learning Launch ” tool for this customer , because it allowed our team to work very closely   and collaboratively with the customer in an environment that closely mimicked how the system would   be deployed if the project proved to be a viable prototype that should be scaled . 

 Application :    Since the customer was already a receiver of raw data from my company , we were able to   generate a high number of data sets over a 6 - week period to include disconfirming data in our test   batches .   The cost of the “ Learning Launch ” was largely limited to labor costs associated with two of our   analysts and software engineers that supported the active contract with the customer for data   collection .   The individuals chosen to support the “ Learning Launch ” were very disciplined in their   approach , but also had a history of listening very well to customer and tailoring the response to best   meet the customer ’s needs .   Since the nature of the project was leveraging real world data that   generated potentially hundreds of datasets daily , we were able to both develop fast feedback cycles and   present real world examples to our customer to garner their feedback . 

 We built the test around several key assumptions developed in consultation with the customer : ( 1 )   Accuracy of data analysis was the most important attribute of the data collected and analyzed ; ( 2 ) Speed   of data delivery was the second most important attributed ; ( 3 ) Number of samples daily was the third   most important attribute . 

 Insight :   The “ Learning Launch ” provided to be successful in both walking towards a scalable prototype   to meet the customer ’s needs , but also to aide our team in testing assumptions .   Both the customer and   our team were surprise that the order of priority for “ Learning Launch ” assumptions changed to : ( 1 )   Speed became the most important attribute for the customer ; ( 2 ) The customer identified the “ number   of samples daily ” as the second most important attribute ; ( 3 ) Accuracy , which was the attribute we   assumed was most important , actually became the least important attribute .   The customer realized   that if the data collection and analysis process could happen at the speed they desire with a higher   number of samples daily , they would get a higher level of accuracy in their data over time .   In the end ,   their most import outcome was to know very early that the system detected something .   If perceived as   a significant issue , the customer could choose to deploy additional resources to help resolve . 

 Approach :   The “ Learning Launch ” tool proved to be an excellent method to test assumptions with real   data in an environment that provided customer collaboration and controlled cost over the 6 - week test .    In the end , the scalable prototype met the needs of the customer who has significant “ buy in ” given they   helped shape the final product . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: Cristino Ramirez
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: Cristino Ramirez 

 29 May 2020 

 Challenge & Selection : 

 Our government customer was used to relying on proprietary data collection and analysis tools   specifically tailored for the government operating environment .   Over time , the government customer   realized that commercial solutions offerings existed that could provide highly accurate data analysis to   enhance their knowledge and awareness of their operating environment at a very affordable cost .    When compared to designing and acquiring their own proprietary system , the government customer   realized that our commercial solution could have the potential to enhance their capabilities , but were   interested in understanding how our data analysis system could be integrated into their normal daily   operations . 

 Selection : 

 I chose the “ Learning Launch ” tool for this customer , because it allowed our team to work very closely   and collaboratively with the customer in an environment that closely mimicked how the system would   be deployed if the project proved to be a viable prototype that should be scaled . 

 Application :    Since the customer was already a receiver of raw data from my company , we were able to   generate a high number of data sets over a 6 - week period to include disconfirming data in our test   batches .   The cost of the “ Learning Launch ” was largely limited to labor costs associated with two of our   analysts and software engineers that supported the active contract with the customer for data   collection .   The individuals chosen to support the “ Learning Launch ” were very disciplined in their   approach , but also had a history of listening very well to customer and tailoring the response to best   meet the customer ’s needs .   Since the nature of the project was leveraging real world data that   generated potentially hundreds of datasets daily , we were able to both develop fast feedback cycles and   present real world examples to our customer to garner their feedback . 

 We built the test around several key assumptions developed in consultation with the customer : ( 1 )   Accuracy of data analysis was the most important attribute of the data collected and analyzed ; ( 2 ) Speed   of data delivery was the second most important attributed ; ( 3 ) Number of samples daily was the third   most important attribute . 

 Insight :   The “ Learning Launch ” provided to be successful in both walking towards a scalable prototype   to meet the customer ’s needs , but also to aide our team in testing assumptions .   Both the customer and   our team were surprise that the order of priority for “ Learning Launch ” assumptions changed to : ( 1 )   Speed became the most important attribute for the customer ; ( 2 ) The customer identified the “ number   of samples daily ” as the second most important attribute ; ( 3 ) Accuracy , which was the attribute we   assumed was most important , actually became the least important attribute .   The customer realized   that if the data collection and analysis process could happen at the speed they desire with a higher   number of samples daily , they would get a higher level of accuracy in their data over time .   In the end ,   their most import outcome was to know very early that the system detected something .   If perceived as   a significant issue , the customer could choose to deploy additional resources to help resolve . 

 Approach :   The “ Learning Launch ” tool proved to be an excellent method to test assumptions with real   data in an environment that provided customer collaboration and controlled cost over the 6 - week test .    In the end , the scalable prototype met the needs of the customer who has significant “ buy in ” given they   helped shape the final product .
### Output: 