Reflection – Learning Launch 

 Submitted by : Steven Richards 

 Date : November 25 2018 

 Challenge and Selection :   My first experience of using tools related to Design Thinking was the   Learning Launch – or Experimentation .   I ’ve been working with a number of global consultants from   all over the world in seeking hypothetical , disruptive innovations that World Wild Foundation might   potentially consider commercializing and scaling as part of their existing Consumer Health portfolio .    This was an entirely hypothetical example for them to consider – and may never see the light of day   – but we need to show them how the innovation process works and what it might produce .   As part   of that innovation phase – a critical part of it involved collecting external data from pharma   executives , technologists and actual consumers to validate if any of the rather zany products or   services we were creating might actually have merit in considering .   Thus a Learning Launch was   undertaken to develop a hypothesis for each of the proposed products ( 10 in all ) , and we quickly   realized that , as many of our ideas utilized the same technologies ( block chain , VR , big data , nano- technology , wearables , etc . ) we might be able to consolidate 10 product Learning Launches and   reduce that number to about 4 , as we had very limited time and needed to move fast .   Learning   Launch provided us the critical platform to take our rather wild ideas and see if there were any   issues , worries , challenges or out - right fails associated with chasing these further down the   innovation path . 

 Application :   We started by looking at the core or ‘ crux ’ hypotheses we needed to test with external   sources – and identified 4 key hypotheses to address .   In each of these , we need then to determine   what were the most effective means of gathering the most illustrative data that would either   validate our concepts – or prove them to be ineffective , too expansive , technologically impossible or   otherwise of little concern to the consumer and/or problematic with our client company .   We   ultimately landed on using on - line surveys using Survey Monkey , where we sought demographic   data to address ethnographics , then dug into the challenges / problems the respondents experienced ,   and further drilled down ( based on answers to key questions ) , as to what their experience was with   each of these elements in terms of available relief or cure .   This was used to qualify respondents to   be able to participate in the next phase of questioning .    We then introduced a prototype concept to   them , simply explaining the basics and what use cases we planned to implement . This then framed   up the follow on questions that queried whether or not they would feel comfortable engaging with   the technologies we proposed , the techniques and methods involved , and whether or not they   would consider using these concepts were they readily available tomorrow .   We were seeking , as Heiko Meier described in week 4 ’s Learning Launch presentation , the truth about whether or not our ideas   were even worth considering – or would the fall at the first hurdle .   We further sought open - ended   responses as to why they might use or not use our disruptive innovations – and then poured through   the results in earnest .   We launched these extensive surveys via various social media tools to gain   access to as large a sample size as we could – to maintain a level of statistical significance . 

 We then divided the responses into appropriate groups covering each of the disruptive innovations   we were seeking insight about – then into ethnographic categories , to see if patterns would emerge   on that demographic breakout . 

 In some cases – where responses appeared to lead towards deeper , more insightful learning – we   reached out to individuals for one - to - one interviews ( with their permission , of course ) , and dug   further into the details of why they responded the way they did , and why they would either choose   to engage or pass on the concepts we created .   We were deeply aware of confirmation bias , and   seeking to really understand why the sceptics were feeling the way in which they did . 

 We then met as a team with a specific focus – which was only to validate or invalidate the concepts   – based solely on the data we received back , and sought to ignore any personal attachment to the   concepts we ’d created initially . 

 Insight and Approach :   The insights we gained by reviewing the data from the surveys and interviews   was rather insightful – and challenged all of us to reconsider several aspects of our disruptive   innovations .   We found the people had very specific notions and pre - conceptions about the pharma   industry , misuse of personal data , the use of wearable data gathering devices and multiple   perspectives ( POVs ) represented from each of the constituent groups ( internal pharma execs ,   consumers , technologists ) .   What we quickly realized was that all parties felt that the innovation   activity that led to our particular concepts were laudable and worth exploring ultimately as ideas for   the future .   But in most cases – only the least invasive concepts were ultimately rewarded with an   appropriate level of validation – mostly in the realm of using wearable devices such as smart   watches or using VR tools ( but not implants , smart tattoos or nanotech ingestible technology ) .    Other ideas that involved genomics , open source data - base queries using their personal data ( even   anonymously using block chain management ) and creating a highly personalized ‘ Health Cockpit ’   were met with high levels of scepticism – based on the multitude of data breaches , misuse by profit   driven insurance companies , and poor data management in previous efforts to create data oceans   by the likes of Google working with the National Health Service in the UK – as well as the typical   commercial selling of personal data to marketing companies .   Invasive technologies were also met   with a level of concern – mostly around the unknown effects these devices might have on the   human body – not to mention what is done with the data they create and who owns it ultimately . 

 This was massively mind - expanding and brought to bear many aspects and knock - on effects of   intense media coverage , negative perceptions of capitalistic organizations , misuse of personal   information , the seemingly tone - deaf and uncaring approach that major technology companies have   architected their early experiments – as well as how they ’ve dealt with security breaches –   ultimately leading to an overall mistrust of new technologies potentially producing the same   negative outcomes for our respondents .   Had we simply went along with our own biases , where we   felt new technologies offered powerful diagnostic insights , leading to new treatment regimes , new   drug creation or use of alternative means to reduce anxiety , ease pain and suffering and reduce   dependence on drugs – we might have imagined humanity would receive us with open arms and   consume what we created with great enthusiasm .   We would have used traditional business   modelling , creating presumptive market penetration and use acceptance data – likely using   previously existing research that was confirming of our innovative use of technology – and never   would have seen the brick wall sitting in front of us as we sped along that the road ahead was wide 

 open .   This exercise in seeking ‘ the truth ’ and removing confirmation bias challenges , while then   focusing only on whether or not the raw data received either validated or invalidated our original   hypotheses save us time , money , and wasted innovation and manufacturing cycles , not to mention   failed marketing and sales activity and reputational damage we could have imposed on an unwitting   client who otherwise might have been completely sold on our ideas . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: Steven Richards
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: Reflection – Learning Launch 

 Submitted by : Steven Richards 

 Date : November 25 2018 

 Challenge and Selection :   My first experience of using tools related to Design Thinking was the   Learning Launch – or Experimentation .   I ’ve been working with a number of global consultants from   all over the world in seeking hypothetical , disruptive innovations that World Wild Foundation might   potentially consider commercializing and scaling as part of their existing Consumer Health portfolio .    This was an entirely hypothetical example for them to consider – and may never see the light of day   – but we need to show them how the innovation process works and what it might produce .   As part   of that innovation phase – a critical part of it involved collecting external data from pharma   executives , technologists and actual consumers to validate if any of the rather zany products or   services we were creating might actually have merit in considering .   Thus a Learning Launch was   undertaken to develop a hypothesis for each of the proposed products ( 10 in all ) , and we quickly   realized that , as many of our ideas utilized the same technologies ( block chain , VR , big data , nano- technology , wearables , etc . ) we might be able to consolidate 10 product Learning Launches and   reduce that number to about 4 , as we had very limited time and needed to move fast .   Learning   Launch provided us the critical platform to take our rather wild ideas and see if there were any   issues , worries , challenges or out - right fails associated with chasing these further down the   innovation path . 

 Application :   We started by looking at the core or ‘ crux ’ hypotheses we needed to test with external   sources – and identified 4 key hypotheses to address .   In each of these , we need then to determine   what were the most effective means of gathering the most illustrative data that would either   validate our concepts – or prove them to be ineffective , too expansive , technologically impossible or   otherwise of little concern to the consumer and/or problematic with our client company .   We   ultimately landed on using on - line surveys using Survey Monkey , where we sought demographic   data to address ethnographics , then dug into the challenges / problems the respondents experienced ,   and further drilled down ( based on answers to key questions ) , as to what their experience was with   each of these elements in terms of available relief or cure .   This was used to qualify respondents to   be able to participate in the next phase of questioning .    We then introduced a prototype concept to   them , simply explaining the basics and what use cases we planned to implement . This then framed   up the follow on questions that queried whether or not they would feel comfortable engaging with   the technologies we proposed , the techniques and methods involved , and whether or not they   would consider using these concepts were they readily available tomorrow .   We were seeking , as Heiko Meier described in week 4 ’s Learning Launch presentation , the truth about whether or not our ideas   were even worth considering – or would the fall at the first hurdle .   We further sought open - ended   responses as to why they might use or not use our disruptive innovations – and then poured through   the results in earnest .   We launched these extensive surveys via various social media tools to gain   access to as large a sample size as we could – to maintain a level of statistical significance . 

 We then divided the responses into appropriate groups covering each of the disruptive innovations   we were seeking insight about – then into ethnographic categories , to see if patterns would emerge   on that demographic breakout . 

 In some cases – where responses appeared to lead towards deeper , more insightful learning – we   reached out to individuals for one - to - one interviews ( with their permission , of course ) , and dug   further into the details of why they responded the way they did , and why they would either choose   to engage or pass on the concepts we created .   We were deeply aware of confirmation bias , and   seeking to really understand why the sceptics were feeling the way in which they did . 

 We then met as a team with a specific focus – which was only to validate or invalidate the concepts   – based solely on the data we received back , and sought to ignore any personal attachment to the   concepts we ’d created initially . 

 Insight and Approach :   The insights we gained by reviewing the data from the surveys and interviews   was rather insightful – and challenged all of us to reconsider several aspects of our disruptive   innovations .   We found the people had very specific notions and pre - conceptions about the pharma   industry , misuse of personal data , the use of wearable data gathering devices and multiple   perspectives ( POVs ) represented from each of the constituent groups ( internal pharma execs ,   consumers , technologists ) .   What we quickly realized was that all parties felt that the innovation   activity that led to our particular concepts were laudable and worth exploring ultimately as ideas for   the future .   But in most cases – only the least invasive concepts were ultimately rewarded with an   appropriate level of validation – mostly in the realm of using wearable devices such as smart   watches or using VR tools ( but not implants , smart tattoos or nanotech ingestible technology ) .    Other ideas that involved genomics , open source data - base queries using their personal data ( even   anonymously using block chain management ) and creating a highly personalized ‘ Health Cockpit ’   were met with high levels of scepticism – based on the multitude of data breaches , misuse by profit   driven insurance companies , and poor data management in previous efforts to create data oceans   by the likes of Google working with the National Health Service in the UK – as well as the typical   commercial selling of personal data to marketing companies .   Invasive technologies were also met   with a level of concern – mostly around the unknown effects these devices might have on the   human body – not to mention what is done with the data they create and who owns it ultimately . 

 This was massively mind - expanding and brought to bear many aspects and knock - on effects of   intense media coverage , negative perceptions of capitalistic organizations , misuse of personal   information , the seemingly tone - deaf and uncaring approach that major technology companies have   architected their early experiments – as well as how they ’ve dealt with security breaches –   ultimately leading to an overall mistrust of new technologies potentially producing the same   negative outcomes for our respondents .   Had we simply went along with our own biases , where we   felt new technologies offered powerful diagnostic insights , leading to new treatment regimes , new   drug creation or use of alternative means to reduce anxiety , ease pain and suffering and reduce   dependence on drugs – we might have imagined humanity would receive us with open arms and   consume what we created with great enthusiasm .   We would have used traditional business   modelling , creating presumptive market penetration and use acceptance data – likely using   previously existing research that was confirming of our innovative use of technology – and never   would have seen the brick wall sitting in front of us as we sped along that the road ahead was wide 

 open .   This exercise in seeking ‘ the truth ’ and removing confirmation bias challenges , while then   focusing only on whether or not the raw data received either validated or invalidated our original   hypotheses save us time , money , and wasted innovation and manufacturing cycles , not to mention   failed marketing and sales activity and reputational damage we could have imposed on an unwitting   client who otherwise might have been completely sold on our ideas .
### Output: 