1 . Challenge 

 Data visualization has changed our society considerably . From the most simple projected line across a football field through to complex graphs outlining market fluctuations , they are changing the way that our society is approaching and understanding data . However , despite the huge impact visualizations have had , they still face considerable challenges in the future . We take a look at the 5 most pressing . 

 Virtual reality is going to have a huge impact on the potential for data visualizations , allowing people to interact with data in the third dimension for the first time . Imagine being able to pick a data set and move it around on any axis to compare it to another , it is n’t too far away . According to SAS we can process only 1 kilobit of information per second on a flat screen , which can be increased significantly if it ’s analyzed in a 3D VR world . In fact , we have already seen Goodyear collaborate with Dr Robert Maples to use VR data visualization to improve their Formula 1 tyre performance . 

 However , the challenge with this comes with trying to get it in the hands of businesses who would benefit from the technology . Virtual reality is something that is currently seen as predominantly for entertainment so trying to get a senior leader in a fortune 500 company to wear one to look at sales data would certainly be a struggle . At present there are some moves to try and make VR headsets more compact , but this is going to take several years and data visualization needs to stay front and centre until then . 

 2 . Selection 

 Augmented reality may well be the single biggest change that we are going to see regarding the use of data visualizations . To some extent we have seen some of it already , with HUDs like the now defunct Google Glass , overlaying data onto what you can see in front of you . Bizarrely , one of the key reasons for the sudden concentration on AR is the huge success of Pokemon Go , which not only showed the capabilities of AR , but also introduced it to a wide and diverse audience . 

 The challenge that data visualization is going to have is that those creating them need to make sure they are doing so in an understandable and non - obtrusive way . It creates a new dynamic , where the data overlaid needs to be clear , concise and not distracting . It ’s a fine line to balance on and a real challenge for those who are used to creating traditional visualizations . 

 3 . Application 

 VR and AR are likely to be interesting technologies in the future , but for the time being , we are still going to be consuming the majority of our data through traditional 2D screens . As the number of data visualizations increases in almost every area , the chances of yours standing out decreases too as you ’re trying to get to the top of a larger and larger pile . 

 It means that whilst these other technologies are developing , people working in data visualization need to try and find a way of making their visualizations stand out from the crowd , without making it overly complex . This could mean more vivid colors , increased interactivity or simply using the most interesting data , but finding the correct way is certainly a hurdle to overcome in the next few years . As data has spread throughout society one of the elements that has become evident is that there is a huge variation in the levels of understanding . This could even be in a high powered business setting , where people who are used to seeing basic excel graphs do not understand anything more complex . The idea of interactivity within visualized data is not something they would ever feel necessary . However , there are others who would benefit from more complex visualizations , where they can see as much as possible in as smaller space as they can , through interactive design or just more complex features . It is , therefore , difficult for those designing visualizations to match up to the wide - ranging understanding of data and data visualizations . It could be that multiple visualizations are created for different levels of data literacy , but that ’s simply wasting resources and is hardly a practical solution . 

 4 . Insight and Approach 

 we move toward more interactive and complex trends for data visualizations , we are going to be seeing an increased need for technical skills to first understand and translate the data then create visualizations around the results . We already have a shortage of data scientists and people who can feed the right data to the right people , so this is going to be a key challenge for the creation of decent data visualizations that can pinpoint important data . Although this is likely to increase in the future with an increasing number of universities offering data science courses , this is unlikely to see data scientists becoming prevalent for several years . As discussed previously with VR and AR , working on new technologies is not easy , especially for those with little experience of similar areas . At the same time , these technologies are not developing for data visualization alone , with those with the training and qualifications having the option of working on other popular mediums , like gaming or movies , which may also have higher salaries given the focus of the technologies on these markets . Therefore , trying to find somebody with the technical expertise who has n’t already joined these other industries is going to be a huge hurdle to overcome . 


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: 1 . Challenge 

 Data visualization has changed our society considerably . From the most simple projected line across a football field through to complex graphs outlining market fluctuations , they are changing the way that our society is approaching and understanding data . However , despite the huge impact visualizations have had , they still face considerable challenges in the future . We take a look at the 5 most pressing . 

 Virtual reality is going to have a huge impact on the potential for data visualizations , allowing people to interact with data in the third dimension for the first time . Imagine being able to pick a data set and move it around on any axis to compare it to another , it is n’t too far away . According to SAS we can process only 1 kilobit of information per second on a flat screen , which can be increased significantly if it ’s analyzed in a 3D VR world . In fact , we have already seen Goodyear collaborate with Dr Robert Maples to use VR data visualization to improve their Formula 1 tyre performance . 

 However , the challenge with this comes with trying to get it in the hands of businesses who would benefit from the technology . Virtual reality is something that is currently seen as predominantly for entertainment so trying to get a senior leader in a fortune 500 company to wear one to look at sales data would certainly be a struggle . At present there are some moves to try and make VR headsets more compact , but this is going to take several years and data visualization needs to stay front and centre until then . 

 2 . Selection 

 Augmented reality may well be the single biggest change that we are going to see regarding the use of data visualizations . To some extent we have seen some of it already , with HUDs like the now defunct Google Glass , overlaying data onto what you can see in front of you . Bizarrely , one of the key reasons for the sudden concentration on AR is the huge success of Pokemon Go , which not only showed the capabilities of AR , but also introduced it to a wide and diverse audience . 

 The challenge that data visualization is going to have is that those creating them need to make sure they are doing so in an understandable and non - obtrusive way . It creates a new dynamic , where the data overlaid needs to be clear , concise and not distracting . It ’s a fine line to balance on and a real challenge for those who are used to creating traditional visualizations . 

 3 . Application 

 VR and AR are likely to be interesting technologies in the future , but for the time being , we are still going to be consuming the majority of our data through traditional 2D screens . As the number of data visualizations increases in almost every area , the chances of yours standing out decreases too as you ’re trying to get to the top of a larger and larger pile . 

 It means that whilst these other technologies are developing , people working in data visualization need to try and find a way of making their visualizations stand out from the crowd , without making it overly complex . This could mean more vivid colors , increased interactivity or simply using the most interesting data , but finding the correct way is certainly a hurdle to overcome in the next few years . As data has spread throughout society one of the elements that has become evident is that there is a huge variation in the levels of understanding . This could even be in a high powered business setting , where people who are used to seeing basic excel graphs do not understand anything more complex . The idea of interactivity within visualized data is not something they would ever feel necessary . However , there are others who would benefit from more complex visualizations , where they can see as much as possible in as smaller space as they can , through interactive design or just more complex features . It is , therefore , difficult for those designing visualizations to match up to the wide - ranging understanding of data and data visualizations . It could be that multiple visualizations are created for different levels of data literacy , but that ’s simply wasting resources and is hardly a practical solution . 

 4 . Insight and Approach 

 we move toward more interactive and complex trends for data visualizations , we are going to be seeing an increased need for technical skills to first understand and translate the data then create visualizations around the results . We already have a shortage of data scientists and people who can feed the right data to the right people , so this is going to be a key challenge for the creation of decent data visualizations that can pinpoint important data . Although this is likely to increase in the future with an increasing number of universities offering data science courses , this is unlikely to see data scientists becoming prevalent for several years . As discussed previously with VR and AR , working on new technologies is not easy , especially for those with little experience of similar areas . At the same time , these technologies are not developing for data visualization alone , with those with the training and qualifications having the option of working on other popular mediums , like gaming or movies , which may also have higher salaries given the focus of the technologies on these markets . Therefore , trying to find somebody with the technical expertise who has n’t already joined these other industries is going to be a huge hurdle to overcome .
### Output: 