Challenge :     The leadership team of the firm I work for had agreed on the priorities and strategy for leveraging   technology to realise business goals . As the strategy was a multi - year journey and involved   orchestrating multiple levers simultaneously , they wanted to ( 1 ) put in place a scorecard to ensure we   are closely monitor and demonstrate the execution of strategy as intended and ( 2 ) facilitate a   consistent understanding of success ( up to 4 levels from the top ) .     As a starting point we had the following sources of information – A rich description of strategy and list   of primary metrics to be used ; Audit reports providing insights into the expectation of stakeholders as   regulators ; Previously used dashboards for similar purposes     Selection     After having looked at previous dashboards and speaking to various user groups- I observed    gaps and tensions between how user groups / stakeholders reasoned about the strategy and   associated metrics in their own contexts .   Some examples include – high level synthesis vs. ability to   drill down ; snapshot of current performance vs. forward looking predictions ; One metric at a time vs.   evaluate the interaction of multiple metrics at the same time . It was clear that refreshing previous   dashboard templates with data for new metrics was not an option .     I knew a dashboard was required but not sure about the form and interface necessary to strike the   right balance between the interpretation and expectation of user groups regarding the strategy . This   involved first making their mental models as explicit as possible ( see week 1 interview)and using them   as building blocks to develop a common mental model that everyone can use to manage the execution   of strategy . Hence , I chose visualisation / visual thinking as a tool .     Application :      I created the following material to be used during 2 - 3 workshops ( we called them road shows ) per   heterogenous group of stakeholders : 

 §   Dummy visual charts ( e.g. , line , bar pie etc ) for the metrics using dummy data . 

 §   Metrics journey for key metrics based on interviews with user group , i.e. , the different paths   of reasoning a user group is likely to take starting with a metric 

 §   Expected hypotheses between metrics derived from strategy . E.g- “ Overtime as the % of   mature engineers increases , we should also expect an increase in release frequency ” 

 During the online workshops , initially a walkthrough of charts and metrics journey was done , and the   forum was left open for reactions or suggestions on metric journeys . The inputs from various   workshops were synthesised in recurring interaction patterns depending on the type ( e.g. , % vs.   actuals ) or view ( e.g. , time series vs. distribution ) of metric . Similarly , the statements of expected   correlations were ratified , and the users were requested to suggest / draw how they would visualise   them . This resulted in a revised set of metrics journeys and a set of new correlation views which was   never used before in the organisation in this context(insight ) .       Approach :   Considering the stakeholder group and problem at hand , I think visual thinking was best   suited . Although I did manage this using online workshops , In future I would enough through   to using the right collaboration tools- screen shares with excel / ppt has their limits . 


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 :     The leadership team of the firm I work for had agreed on the priorities and strategy for leveraging   technology to realise business goals . As the strategy was a multi - year journey and involved   orchestrating multiple levers simultaneously , they wanted to ( 1 ) put in place a scorecard to ensure we   are closely monitor and demonstrate the execution of strategy as intended and ( 2 ) facilitate a   consistent understanding of success ( up to 4 levels from the top ) .     As a starting point we had the following sources of information – A rich description of strategy and list   of primary metrics to be used ; Audit reports providing insights into the expectation of stakeholders as   regulators ; Previously used dashboards for similar purposes     Selection     After having looked at previous dashboards and speaking to various user groups- I observed    gaps and tensions between how user groups / stakeholders reasoned about the strategy and   associated metrics in their own contexts .   Some examples include – high level synthesis vs. ability to   drill down ; snapshot of current performance vs. forward looking predictions ; One metric at a time vs.   evaluate the interaction of multiple metrics at the same time . It was clear that refreshing previous   dashboard templates with data for new metrics was not an option .     I knew a dashboard was required but not sure about the form and interface necessary to strike the   right balance between the interpretation and expectation of user groups regarding the strategy . This   involved first making their mental models as explicit as possible ( see week 1 interview)and using them   as building blocks to develop a common mental model that everyone can use to manage the execution   of strategy . Hence , I chose visualisation / visual thinking as a tool .     Application :      I created the following material to be used during 2 - 3 workshops ( we called them road shows ) per   heterogenous group of stakeholders : 

 §   Dummy visual charts ( e.g. , line , bar pie etc ) for the metrics using dummy data . 

 §   Metrics journey for key metrics based on interviews with user group , i.e. , the different paths   of reasoning a user group is likely to take starting with a metric 

 §   Expected hypotheses between metrics derived from strategy . E.g- “ Overtime as the % of   mature engineers increases , we should also expect an increase in release frequency ” 

 During the online workshops , initially a walkthrough of charts and metrics journey was done , and the   forum was left open for reactions or suggestions on metric journeys . The inputs from various   workshops were synthesised in recurring interaction patterns depending on the type ( e.g. , % vs.   actuals ) or view ( e.g. , time series vs. distribution ) of metric . Similarly , the statements of expected   correlations were ratified , and the users were requested to suggest / draw how they would visualise   them . This resulted in a revised set of metrics journeys and a set of new correlation views which was   never used before in the organisation in this context(insight ) .       Approach :   Considering the stakeholder group and problem at hand , I think visual thinking was best   suited . Although I did manage this using online workshops , In future I would enough through   to using the right collaboration tools- screen shares with excel / ppt has their limits .
### Output: 