Mind Mapping – Idea Generation Process 

 Challenge & Selection 

 The tool I used was the Idea Generation process along with the Mind Mapping Tool to   understand how to grow our business .   This process fit nicely into an ongoing evaluation of our   products and services .   We are a technology services ( consulting ) and software company   focused on the banking markets ( trading ) and other market participants .   We sell a specialized   type of database used to support the most complex analytical requirements in the trading   industry and sell consulting services to build and maintain the software associated with the   database .       The Database ships with a proprietary development language that was very powerful but   difficult to learn and more difficult to staff .   There is a small die - hard community ( devotees &   builders ) that work with the technology daily .   These technology enthusiasts can be fickle and   difficult to work with ; this is in contrast to the database query language SQL which is used by   probably millions of people daily . Consequently , most major banks have built their own   proprietary ecosystems around our database with the difficultly residing in maintaining a team   with very niche , very expensive and specialized skills .       The message more and more had become from our clients that they loved the results that our   technology produced but they deeply disliked the making of the results , i.e. having to maintain   an extremely specialized expensive team of resources , among other things . Our challenge was   to keep these clients happy while trying to drive adoption more broadly in the trading   community and into new business verticals .     Application     After meeting with many of our clients over a couple of months we gathered feedback and tried   to put messages , themes and ideas into different buckets to observe the feedback .   The first   message we received loud and clear was the need for some kind of interoperability with   Python .   Although our proprietary language was dramatically more powerful , recent grads and   a large globally community had essentially said Python was the de - facto analytical language .    The reality was we could somehow try to convince a global community to learn our language or   we could build interoperability with Python instantly making our product accessible to millions   of people in many industries .   This may seem like a no brainer , but it is complicated . Reaching a   global community would take some doing , but it was an achievable goal .     The second theme that arose was the “ care and feeding ” of these implementations .   Banks   were tired of building and maintain technology ecosystems ; they wanted something off the   shelf .   We had developed some internal development frameworks and written some engines   ( software code ) that could be packaged together and productized .   With a little effort we could   productize these internal frameworks and have clients either start their relationships with our   technology or our larger clients could migrate to these frameworks . 

 These ideas we needed to flesh out from ideas to possible offerings .   For the easy fix ( which not   what we were long for ) to the paradigm shift in how we make money .   To answer some of   these questions we asked a small subsection of our own community to consultants who were   expert in our own technology and could also provide insight into what they saw day to day at   the clients ( it ’s amazing what a client will tell you in the conference room and what an onsite   consultant can observe ) .     We also approached our Core technical team to evaluate the feasibility of interoperability   would be and to capture pluses and minuses to make this technology interoperability happen .     Insight     These were some really big ideas at the firm .   For years technologists on Wall Street would   learn this exotic development language and deliver amazing insights into the markets .   There   was a high degree of “ pride of ownership ” and for years our firm had been content with making   money selling consulting .   If we could enable essentially anyone with a passing knowledge of   Python to yield similar results as our die - hard specialists , what would happen to the   community ?     We wrestled with this question . What made our technology special was its unique power .   If   interoperability with Python resulted in an offering that was diminished , then our technology   would no longer be special and therefore there would be no reason to use our technology .      The second idea , to offer a Product that was powered by our database and analogous to what   our community built and supported ( essentially competing with their livelihood ) was another   risk- but a risk we needed to take .       Approach     In the future , I would have brought in clients to our offices to both hear what was both good   and bad about our technology but I would also have them brainstorm tools and ideas to see   what insights they would bring .   This done well could yield interesting results , done poorly and   it could devolve into a rudderless affair . 


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: Mind Mapping – Idea Generation Process 

 Challenge & Selection 

 The tool I used was the Idea Generation process along with the Mind Mapping Tool to   understand how to grow our business .   This process fit nicely into an ongoing evaluation of our   products and services .   We are a technology services ( consulting ) and software company   focused on the banking markets ( trading ) and other market participants .   We sell a specialized   type of database used to support the most complex analytical requirements in the trading   industry and sell consulting services to build and maintain the software associated with the   database .       The Database ships with a proprietary development language that was very powerful but   difficult to learn and more difficult to staff .   There is a small die - hard community ( devotees &   builders ) that work with the technology daily .   These technology enthusiasts can be fickle and   difficult to work with ; this is in contrast to the database query language SQL which is used by   probably millions of people daily . Consequently , most major banks have built their own   proprietary ecosystems around our database with the difficultly residing in maintaining a team   with very niche , very expensive and specialized skills .       The message more and more had become from our clients that they loved the results that our   technology produced but they deeply disliked the making of the results , i.e. having to maintain   an extremely specialized expensive team of resources , among other things . Our challenge was   to keep these clients happy while trying to drive adoption more broadly in the trading   community and into new business verticals .     Application     After meeting with many of our clients over a couple of months we gathered feedback and tried   to put messages , themes and ideas into different buckets to observe the feedback .   The first   message we received loud and clear was the need for some kind of interoperability with   Python .   Although our proprietary language was dramatically more powerful , recent grads and   a large globally community had essentially said Python was the de - facto analytical language .    The reality was we could somehow try to convince a global community to learn our language or   we could build interoperability with Python instantly making our product accessible to millions   of people in many industries .   This may seem like a no brainer , but it is complicated . Reaching a   global community would take some doing , but it was an achievable goal .     The second theme that arose was the “ care and feeding ” of these implementations .   Banks   were tired of building and maintain technology ecosystems ; they wanted something off the   shelf .   We had developed some internal development frameworks and written some engines   ( software code ) that could be packaged together and productized .   With a little effort we could   productize these internal frameworks and have clients either start their relationships with our   technology or our larger clients could migrate to these frameworks . 

 These ideas we needed to flesh out from ideas to possible offerings .   For the easy fix ( which not   what we were long for ) to the paradigm shift in how we make money .   To answer some of   these questions we asked a small subsection of our own community to consultants who were   expert in our own technology and could also provide insight into what they saw day to day at   the clients ( it ’s amazing what a client will tell you in the conference room and what an onsite   consultant can observe ) .     We also approached our Core technical team to evaluate the feasibility of interoperability   would be and to capture pluses and minuses to make this technology interoperability happen .     Insight     These were some really big ideas at the firm .   For years technologists on Wall Street would   learn this exotic development language and deliver amazing insights into the markets .   There   was a high degree of “ pride of ownership ” and for years our firm had been content with making   money selling consulting .   If we could enable essentially anyone with a passing knowledge of   Python to yield similar results as our die - hard specialists , what would happen to the   community ?     We wrestled with this question . What made our technology special was its unique power .   If   interoperability with Python resulted in an offering that was diminished , then our technology   would no longer be special and therefore there would be no reason to use our technology .      The second idea , to offer a Product that was powered by our database and analogous to what   our community built and supported ( essentially competing with their livelihood ) was another   risk- but a risk we needed to take .       Approach     In the future , I would have brought in clients to our offices to both hear what was both good   and bad about our technology but I would also have them brainstorm tools and ideas to see   what insights they would bring .   This done well could yield interesting results , done poorly and   it could devolve into a rudderless affair .
### Output: 