Challenge 

 My company sells subscriptions of specialized medical content to health institutions globally .   Many   of the buyers / administrators at the institutions complained that their users were not always aware of   the content they were subscribing to , so it often went unused .   This made it difficult to justify renewal   of the subscription .   The problem was also reflected in our analytics which showed heavy usage by a   very small percentage of an institution ’s members and very little usage with the broader population . 

 We knew one option was to make the content discoverable on search engines like Google or other   platforms medical searchers used such as PubMed .   However , the technology of our current   platform prevented us from opening our site to Google ’s crawlers to enable indexing of the content .    Initial estimates indicated it would be too costly to build an entirely new platform to meet this need . 

 Selection 

 We chose to use a learning launch to test .   Given the known popularity of Google amongst our   customers , we wanted to test for a solution that was within our capabilities and budget .   Since we   were uncertain Google would crawl us , and more importantly rank us well , we designed a simple ,   iterative way to test our concepts while gathering data from real world users .   This would enable us   to determine our assumptions were correct ( or not ) and build a business case to secure additional   investment for the project . 

 Application 

 We decided to start as small as possible for our test .   Our hypothesis was : If we make a small set of   our content available on a platform that google could crawl and index , we could get one or more   users to click on it in search results .   The first iteration of this test was mostly manual with a very   simple interface .   We were n’t worrying about engagement or scale yet .   We just wanted to know if   we could get that one click .   The first iteration succeeded .   The next step was to make more content   available with a slightly more engaging interface .   We incrementally added more content and   features to the site as each step was successful . 

 As we began to see the success of the project , we devised a plan to scale the amount of content   and user load that would be put on the system .   We were able to make a case for this investment   since our ongoing learning had shown positive results . 

 Insight 

 The most important insight gained from this Build - Measure - Lean cycle was the cultural shift it   brought to the organization .   There was a realization that we need not make big , risky decisions , but   instead could step into a question methodically and scientifically .   The organization also realized the   importance of ensuring proper measurement tools were in place through initial launches .   In the   past , we had deferred tracking or analytics tools to subsequent releases , thinking it was more   important to get features in the market .   We realized it was more important to define the RIGHT   features rather then quickly release suboptimal features . 

 Approach 

 While the initiative was generally successful , there are a few things I would change next time .    Primarily , I would involve more people in the process , across different functions and including   executives .   As we sought to scale out as we learned , we found some in the organization challenged   our approach and findings .   They felt like they were being blindsided .   Another thing I would do 

 differently is to ensure that the organization knew that our initial launches were not inherently   scalable .   There was a misconception amongst some executives that we could simply “ flip a switch ”   to scale this out once we saw positive results from our initial tests .   This made our business case for   more investment more challenge , but not insurmountable . 


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 

 My company sells subscriptions of specialized medical content to health institutions globally .   Many   of the buyers / administrators at the institutions complained that their users were not always aware of   the content they were subscribing to , so it often went unused .   This made it difficult to justify renewal   of the subscription .   The problem was also reflected in our analytics which showed heavy usage by a   very small percentage of an institution ’s members and very little usage with the broader population . 

 We knew one option was to make the content discoverable on search engines like Google or other   platforms medical searchers used such as PubMed .   However , the technology of our current   platform prevented us from opening our site to Google ’s crawlers to enable indexing of the content .    Initial estimates indicated it would be too costly to build an entirely new platform to meet this need . 

 Selection 

 We chose to use a learning launch to test .   Given the known popularity of Google amongst our   customers , we wanted to test for a solution that was within our capabilities and budget .   Since we   were uncertain Google would crawl us , and more importantly rank us well , we designed a simple ,   iterative way to test our concepts while gathering data from real world users .   This would enable us   to determine our assumptions were correct ( or not ) and build a business case to secure additional   investment for the project . 

 Application 

 We decided to start as small as possible for our test .   Our hypothesis was : If we make a small set of   our content available on a platform that google could crawl and index , we could get one or more   users to click on it in search results .   The first iteration of this test was mostly manual with a very   simple interface .   We were n’t worrying about engagement or scale yet .   We just wanted to know if   we could get that one click .   The first iteration succeeded .   The next step was to make more content   available with a slightly more engaging interface .   We incrementally added more content and   features to the site as each step was successful . 

 As we began to see the success of the project , we devised a plan to scale the amount of content   and user load that would be put on the system .   We were able to make a case for this investment   since our ongoing learning had shown positive results . 

 Insight 

 The most important insight gained from this Build - Measure - Lean cycle was the cultural shift it   brought to the organization .   There was a realization that we need not make big , risky decisions , but   instead could step into a question methodically and scientifically .   The organization also realized the   importance of ensuring proper measurement tools were in place through initial launches .   In the   past , we had deferred tracking or analytics tools to subsequent releases , thinking it was more   important to get features in the market .   We realized it was more important to define the RIGHT   features rather then quickly release suboptimal features . 

 Approach 

 While the initiative was generally successful , there are a few things I would change next time .    Primarily , I would involve more people in the process , across different functions and including   executives .   As we sought to scale out as we learned , we found some in the organization challenged   our approach and findings .   They felt like they were being blindsided .   Another thing I would do 

 differently is to ensure that the organization knew that our initial launches were not inherently   scalable .   There was a misconception amongst some executives that we could simply “ flip a switch ”   to scale this out once we saw positive results from our initial tests .   This made our business case for   more investment more challenge , but not insurmountable .
### Output: 