Learning launch 

 Challenge and selection 

 I manage a Malta government funded startup accelerator which runs special programmes that   provide support to small tech startup teams that want to prove a business concept through a low   fidelity prototype . When we first started we had next to no experience in doing this . We engaged in   plenty of research , talked to experts in the field , and to many tech founders and entrepreneurs learn   about the business of accelerating startups . We also visited several European accelerators and   incubators in Lisbon , Venice , Tartu , Vienna and Warsaw . 

 Even if we had our own vertical expertise in various technology fields , we had no past experience in   accelerating startups . We quickly realised that even if we were helping startups prove their business   concept and test their prototype , we had ourselves designed a concept which needed validation and   testing . Based on the research we had done , we decided that the learning launch was the best tool   we could use to test a number of assumptions . And that probably two or more learning launches   would be necessary . 

 Application   When innovating and navigating new waters , you are often not aware of what new knowledge lies   out there that needs discovering . So new complex questions that needed answering kept surfacing   as we ran one learning launch after another after another . This made openness and experimentation   the driving philosophy of the accelerator . 

 Initially we wanted to answer questions like , was the pre - seed grant sufficient funding to incentivise   a talented individual to quit his job and become a tech entrepreneur ? Would expert mentors be   willing to provide their input to the programme for free ? Eventually , was the grant and programme   content enough to draw non - indigenous startup to come to Malta , follow the programme and stay ?   Importantly , was the content of the programme relevant and rich enough to improve survivability of   the startups ? 

 We also had plenty of “ what ” and “ why ” questions that needed answering . What value did the   startup teams find in the programme ? Why did insufficient ideas come from indigenous teams ? And   why did some non - indigenous teams find it attractive to stay , and others not ? 

 Due to the unprecedented nature of the acceleration programme for Malta and the insufficient   expertise available , it became evident that only learning launches would give us concrete answers   and insights which would enable us to scale up our accelerator . 

 Insight & Approach   Launching a call for startups requires considerable planning and design to target startup teams with   the required talent , maturity , skill and technological competence . This enabled us to single out   beforehand certain assumptions we wanted to test , including the limitations and value of the   acceleration programme . 

 For example , after establishing a set amount for the pre - seed grant , based on market estimations   and a number of interviews , we learnt that indigenous startup teams were not sufficiently motivated   due to the high demand for tech talent coming from the ICT , media and online gaming industries .   Eventually , when we internationalised our calls , the number of applications received shot up by over 

 500 % , proving amply that Malta could become a startup hub in the region . Further learnings were   then made . Among them , we found that non - indigenous startups were attracted by the grant and   the content of the programme . But they were also more interested in the network of mentors and   the international context which was generally perceived as more promising from a fund raising   perspective , particularly for certain technologies and markets . 

 Reflecting on the work done , however , since we are a small team , most learnings were not usually   formalised other than through a post - mortem to discuss feedback from the startups . It is important   instead that assumptions that need to be tested must be stated , and metrics established to   determine verification of the assumption . 

 Nevertheless , the learning launch tool , adopted iteratively from one programme to the next , has   helped the team gain vital insights on what are the key ingredients that should be included in an   international , scaled up design of the accelerator programme . 


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 and selection 

 I manage a Malta government funded startup accelerator which runs special programmes that   provide support to small tech startup teams that want to prove a business concept through a low   fidelity prototype . When we first started we had next to no experience in doing this . We engaged in   plenty of research , talked to experts in the field , and to many tech founders and entrepreneurs learn   about the business of accelerating startups . We also visited several European accelerators and   incubators in Lisbon , Venice , Tartu , Vienna and Warsaw . 

 Even if we had our own vertical expertise in various technology fields , we had no past experience in   accelerating startups . We quickly realised that even if we were helping startups prove their business   concept and test their prototype , we had ourselves designed a concept which needed validation and   testing . Based on the research we had done , we decided that the learning launch was the best tool   we could use to test a number of assumptions . And that probably two or more learning launches   would be necessary . 

 Application   When innovating and navigating new waters , you are often not aware of what new knowledge lies   out there that needs discovering . So new complex questions that needed answering kept surfacing   as we ran one learning launch after another after another . This made openness and experimentation   the driving philosophy of the accelerator . 

 Initially we wanted to answer questions like , was the pre - seed grant sufficient funding to incentivise   a talented individual to quit his job and become a tech entrepreneur ? Would expert mentors be   willing to provide their input to the programme for free ? Eventually , was the grant and programme   content enough to draw non - indigenous startup to come to Malta , follow the programme and stay ?   Importantly , was the content of the programme relevant and rich enough to improve survivability of   the startups ? 

 We also had plenty of “ what ” and “ why ” questions that needed answering . What value did the   startup teams find in the programme ? Why did insufficient ideas come from indigenous teams ? And   why did some non - indigenous teams find it attractive to stay , and others not ? 

 Due to the unprecedented nature of the acceleration programme for Malta and the insufficient   expertise available , it became evident that only learning launches would give us concrete answers   and insights which would enable us to scale up our accelerator . 

 Insight & Approach   Launching a call for startups requires considerable planning and design to target startup teams with   the required talent , maturity , skill and technological competence . This enabled us to single out   beforehand certain assumptions we wanted to test , including the limitations and value of the   acceleration programme . 

 For example , after establishing a set amount for the pre - seed grant , based on market estimations   and a number of interviews , we learnt that indigenous startup teams were not sufficiently motivated   due to the high demand for tech talent coming from the ICT , media and online gaming industries .   Eventually , when we internationalised our calls , the number of applications received shot up by over 

 500 % , proving amply that Malta could become a startup hub in the region . Further learnings were   then made . Among them , we found that non - indigenous startups were attracted by the grant and   the content of the programme . But they were also more interested in the network of mentors and   the international context which was generally perceived as more promising from a fund raising   perspective , particularly for certain technologies and markets . 

 Reflecting on the work done , however , since we are a small team , most learnings were not usually   formalised other than through a post - mortem to discuss feedback from the startups . It is important   instead that assumptions that need to be tested must be stated , and metrics established to   determine verification of the assumption . 

 Nevertheless , the learning launch tool , adopted iteratively from one programme to the next , has   helped the team gain vital insights on what are the key ingredients that should be included in an   international , scaled up design of the accelerator programme .
### Output: 