David Schuff

The most important takeaway from the speakers was to allow the graphic to stand on its own and tell a story. The message you want to deliver should be immediately and clearly understood by the picture. Those that need to be explained have not done the job. Also, different types of graphics do better than others to tell specific stories – how do things compare versus what are the trends, etcetera. It is not just about drawing a picture to show the data, but to make it clearly show an actionable solution. My concern now is that I need to learn Tableau much better than I currently know it!

I think that the most important takeaway from the guest speakers’ presentation was that a good data visualization should be easy to understand for the general audience (with no special background, like statistics or economics), the color scheme should be consistent in all of the graphs used to make it easier for the audience to compare different sets of data, and most importantly the data visualization should be able to communicate a clear message and answer a particular question being addressed.

The guest speakers helped a lot in understanding how the visualizations are actually used in making business decisions. It was good to know the practical usage of what we learn in class. It was interesting to see different types of visualizations for a question and learn from the speakers which visualization is more effective for the question at hand. Even though it might seem obvious, it was good to see examples where a combination of visualizations are used to communicate a message.

I thought that the presentation was great! It was awesome to know just how many different applications visualization has. I think one of the most important take-aways were the ground rules for good visualizations (keeping the audience in mind, using the right type of graphic, use actionable data, etc.) It surprised me that certain data and information can be seen so much more clearly when viewed in the correct way, and that the best graphics are the ones that elicit even more questions about a data set.

The biggest takeaway from the presentation was the need to tailor presentations and graphs to your audience. Graphics should be made to highlight information your audience is most concerned with. To make that information standout is necessary to keep their attention and help them see data in a way that answers their questions quickly. The goal should be to make a graph reveal critical information which your audience can understand in about five seconds.

At the outset the presentation was great. high quality talk and visuals. I particularly liked the focus on the design of the visualization rather than just focus on the tools.

I also thought the presentation was very enlightening. As some of our other classmates have touched on, I felt that one of the most important takeaways was the idea of an infographic telling a clear narrative, that is easy to understand without need for explanation. If you have to explain the infographic, then chances are that there will be several people who do not understand, or do not understand in the time that the slide or graphic should take to cover.

Last week the Wall Street Journal published an article how the next big thing is a Masters in Data Analytics. The presentation definitely proved this through the discussions of how it could be used in different areas within an organization. Visualization and uses of big data help not only analyze the information from the past but also possibly predict or have somewhat of an idea of what the future holds as well.

The most important takeaway from the speakers’ presentation was that data analytics and visualizations should try to simplify information, not complicate it. Often times when we are trying to prove how much work we have done or when we are unable to discern the most important information, we often end up with a far more complex story than our clients want. Presenting a simple visualization that proves you have synthesized all the relevant information into an easily accessible format is often what is most desired by the client.

I completely agree with a lot of the above comments. The most important takeaway for me was the importance of simple visualizations. Data can be complex and complicated. Figuring out the right method for the story you are trying to tell is critical. The visualization should not add to the level of complication, but rather simplify it so that the most important key takeaway can be absorbed by the audience. Overall though I really enjoyed listening to both Rich and Todd talk about data visualization and how it is so applicable across industries today to be able to deliver insight, action, and impact.

I think that the most important takeaway from last weeks presentation was clearly the importance of being sure to allow the data to tell a story. While the presenter makes clear that it is important to use your data to back up your claims, it is even more important to create a coherent narrative that helps put your claims and the data in context. It is a two fold takeaway that reinforces itself. While it is clearly important to ensure that you are able to create a narrative that helps support the correct pathway and solve the problem for the client you are working with, it is also important to ensure that your conclusions are derived from the information that you are showing.

1. I believe the most important takeaway from the guest lecture presentation is how the same data can be presented in different ways to draw different inferences. Sometimes, we stick with one style of representing the data, without realizing other styles might be able to deliver the message more meaningfully.

I thought the presentations were extremely insightful and could be directly applied to the field in which we are interested in. I thought the biggest takeaway from my end was the ability for them to translate data in an easy to understand manner. They told us about the data set that they were using and the ways they could graphically depict the information to present it in a clear manner. They showed us multiple graphs and charts and explained each one thoroughly and showed the key differences each graph was highlighting. I kind of wish the presenters could have stayed longer and taken a little extra time on each graph and broken down one of the graphs to show exactly how the graph was thought up and then created. Overall, I thought the guest speakers and presentation was a great way to show an applicable use of infographics and data visualization.

‘If you’ve never seen what a Chief Data Scientist looks like.. Well now you do.’
–> The guest speakers provided me with a standard to strive for. The two Deloitte representatives were smart, interesting, captivating, and down to earth. I cared about their presentation and the content they provided, but I was more focused on what words they used, how they played with the data, why they used analytics. My goal was to be able to take away details that I could somehow use to advance myself in the analytic industry.

1) I learnt how to present complex data into simple forms of graphs and charts. No matter how huge is the data, the skill lies in presenting it! Few of the example graphs and charts shown by Todd were tremendously interesting.

The most important takeaway that I had from the presentation last week which I think is counterintuitive in other exercises is to know what answer you are looking for before you decide which data you need. Often I see more benefit in the exploration of data but when it comes to visualization this seems more appropriate.

I think, more than anything, I just enjoyed the speakers excitement about data. I could tell that the two gentleman really enjoy what they do and it’s given me a slightly different perspective about a topic that can, at times, seem dry.

Last week’s speakers were incredibly engaging. The visuals were especially powerful and their personalities brought the concepts to life in a very engaging manner. Overall, the most critical takeaway for me was that companies should avoid waiting for the perfect candidate (i.e. the MIT post-doc). At this stage, even moving the needle from 50% on point to 60% in your analysis will crush competition that have not yet begun to incorporate data visualization and analysis into their offering. My own company is incredibly guilty of this practice, so I was able to immediately leverage some of their lessons in my own day-to-day to continue advancing the need for better data management internally.

My take away from our speakers were that if you have a good infographic it will convey important information that management will be able to use to make important decision about their business. All the data that you need will be available in one medium readily available.

The digital information good that I could think of from my experience is a beacon (that’s what the company calls, not sure if its widely popular).
So the basic job of this good is to send text messages to people as soon as they enter certain area or a property. They might be greeting messages or coupons or any texts that the company wants to tell its customers.

My take away from last week’s speakers’ presentation was that how different infographic can infer different information. So, it is very important to choose the right infographic to present the information

Q1: I am not really surprised by any of the comments of the speakers. Instead my understanding about visualization and the importance of story telling was further strengthened. I realized the importance of various tools and the need to be able to see the holistic picture as well as the granular information.

1.The most important take-away from the speaker’s presentation during last class was: as important as what you are saying is how you are saying it. Good insights will only become obvious if appropriate visualizations are employed. The keyword in that sentence is “appropriate”, given that different visualization tools will yield different insights, and it is extremely important to make sure tools are selected with that in mind.

The most important take away for me was that you might have a sexy visual to show off but unless it tells a story your audience can understand then it’s useless. I run a small music production company and therefore the information good is the “album” or final product of the artistic ideas and inspiration. The most expensive part: times and creativity. One, is invaluable and the other you can’t buy.

The most important takeaway in my opinion was that the power of visualizations really lies in how your audience takes and interprets them. Careful consideration should be made not only in how the audience will be able to understand the visualization but also what implications it will have. This includes whatever biases the viewer brings to the table. Having multiple versions of the visualization configured in different layouts is a good exercise in case they can visually digest it in only certain forms.

At the company I interned with this past summer, I helped create several use cases for their previous projects. These documents are similar to case studies which we read at school. This might not be sold as an information good but is a good which is circulated across platforms to add value to the work done by the company, which is what an information good does.

Thanks Prof. This is great. Thank you for arranging the talk.

The Venn diagram strategy seems to big at Netflix, as it has used the same scheme while deciding the price for Favela Rising as it chose the common customers for both City of God and Born into Brothels which was estimated to be 250000 and made the decision on price. Cinematch was the biggest data analytic application for Netflix. The way it used data from its employees to see how they react if the DVD doesn’t reach on time was interesting.

This might align with today’s class.

http://www.informationweek.com/software/information-management/metlife-uses-nosql-for-customer-service-breakthrough/d/d-id/1109919?

Very creative use of big data. This makes you think of other possibilities and using different data sources and merging them together to make a very meaningful dataset. I think a lot of other industries change along with data analytics, healthcare being a common one. Another one is the finance industry. They use data analytics in different ways to reevaluate positions in the markets, whether in mutual funds or ETFs, hedge funds, or options.

We have seen how sports and entertainment are changing (Chip Kelly article and Netflix discussion and the above link). All industries are changing with the new era. Most easily seen is the sales and marketing departments who now have better information on to whom to market new and existing product lines – and who is buying or making purchasing decisions. Companies are shifting from guesswork when it comes to strategic competitive maneuvers. Data will show precisely how a competitor is aligning in the market. Accounting is even influenced as better data will allow internal accounting departments to gain a better understanding of industry benchmarks (other companies will not give their information up easily). According to Forbes, data analytics is changing the competitive landscape and has become the top priority in aviation, wind, and manufacturing industries.

http://www.forbes.com/sites/louiscolumbus/2014/10/19/84-of-enterprises-see-big-data-analytics-changing-their-industries-competitive-landscapes-in-the-next-year/

What was the total cost in the years 2009 and 2010?
In 2009, the sum of cost is 10338384.53 and in 2010 it is 23696862.69.

What is the most likely time of day for someone to arrive at the emergency room complaining about chest pain?
4:00pm

Total number of people admitted at the hospital for allergic reaction–187 (female-151 and male-36)

Question: During what month do these least number of patients visit the ER? What is the most common visit reason during this month?
Answer: November- difficultly breathing

What is the most common visit reason for patients aged 70 and over?
Answer: Difficulty breathing

Question: Which month had the least amount of patients arriving to the ER by walking? Which month had the highest amount of patients arriving by walking to the ER? How many patients for each month?
Answer: September, 1 patient arrived by walking to the ER; May, 96 patients arrived by walking to the ER.

How many patients visited Emergency Room? Ans: 17580
How many of them were male and how many female? Male : 6867 , Female: 10713
How many of them had Headache? Male : 217 , Female : 521

Q: What reason has the average longest duration of stay for women? How much?
A: Chest Pain, 0.51 days

What is the ratio of 30 year old men to that of women ? Answer: 79:54 (158 men and 108 women who are 30 years old)

Which visit reason has the highest number of high urgency visits?
Chest pain

Q: What are the top three most common reasons for visiting the ER?
A: Chest Pain (2,285), Difficulty Breathing (1,508), and Pain, Abd General (1,352)

What was the average hospital duration of males and females at the age of 53?
Males = 17 minutes
Females = 22 minutes

Q: What is the average stay duration for people aged 29 years old?

Answer: 29 year old stay on average 16.08 minutes, where male patients spend on average 14.4 minutes and female patients spend 17.01 minutes.

Q.How many visits are made by people 20 and younger. What is the most common reason.
A. 651 in total. Flu Symptoms is highest at 85.

Question: What is the average age of the males and females who go to the ER?
Answer: Female: 52.72 years old Male: 52.21 years old.

1) Dart mart example: information regarding no. of employees completed a particular certification.
Parameters: Department, Job titles, and number of attempts

Data mart example: Information on media insertions placed through the agency

Facts: Estimate ID, Property/Station ID, Weeks Active

Measures: Quantity Ordered, Spend

Dimensions: Client, Media Type (Product), Time

Data mart example: Information on student’s contact information and interests
Facts: Address, Phone #, Email, Social media use
Measures: How many students are interested in what topics and where they are located
Dimensions: Group students together by region and interests

For the Data Mart Exercise, the project that I was working on for my internship was looking to set up several data marts for the governmental organization we were consulting for. In terms of facts we were looking at dollar amounts for contracts that were awarded, how much had been set aside for research topics, researcher names, contact information and research subject. Measures were variables such as awards or research citations for research that had award money tied to it, or how much money had been spent. Finally the dimensions were primarily measured in financial terms.

Q: What is the most expensive month on average to go to the emergency room?

A: August 2010 = $1,978.49

What is the total number of males admitted for allergic reaction?

How many teens went to the ER with the highest urgency in 2010?

2

What was the average ER length of stay for 65 year old + male or female? Male, .576 days

While working at Merck this past summer, I dealt extensively with several Merck data marts, including a data mart of the sales of pharmaceutical representatives. Measures included sales in particular regions, number of calls, and doctor visits. Dimensions were primarily financial. This data mart served to evaluate sales representatives, and helped to make sure different reps didn’t cross over each other’s territory.

Data.gov could encourage better use of data by doing the following:
1. Simplicity: Create the website and data interface with advance search features to ensure using the site is intuitive and easy
2. Nomenclature and Classification: Give detailed description of data sets , identify source of data, define meta data to help users understand the data set better
3. Application: For each data category display some useful case studies on the Data.gov website.

If I were in charge of data.gov, I would encourage people to create and share visualizations of the data they cared about most. This would encourage collaboration and make the data sets even more readable by non-technical users. The maximize this potential, rewards and incentives could be given to those with the best/ most accurate/ most useful interpretations of data.

This mashup shows the correlation between high school graduation rate and teen birth rate. Note that those states with the highest ranking (darkest color) are those with either a high percentage of high school graduates, a low teen birth rate, or a combination of the two. The opposite goes for those states with the lowest ranking.

http://www.datamasher.org/mash-ups/people-25-yrs-who-completed-high-school-divided-teen-birth-rate-1000-population

The following link shows the mashup on unemployment and poverty rate in the US. The states with higher resultant figure (product of unemployment and poverty rate) are shown with darker colors in the map and vice-a-versa.
http://www.datamasher.org/mash-ups/unemployment-times-poverty-rate#map-tab

To encourage people to use data.gov, I would make different competitions that would require the use of Data.gov sites and data. We’re a culture that’s all about competition so I think this would be a good motivator for the U.S. I would work with different universities or organizations to see if they could facilitate the competitions and in return get additional federal or state aid. Getting corporate and organizational sponsorship would help with prizes and aid for participants.

The mashup shows the correlation between adult obesity rate and % of adults who are moderately/vigorously active. The darker states have higher correlation between these factors.

http://www.datamasher.org/mash-ups/adult-overweight-obesity-rate-divided-adults-who-were-moderatelyvigorously-active-1

I would encourage healthcare organizations to use Data.gov for insight on trends regarding admission rates, the length of stay and what the return rates are to see what the national average is. They possibly can use the information to make informed decisions concerning their particular organization and see if they are part of that average.

The following graph shows the correlation between the SAT scores and high school graduation rates. The darker states have higher correlation between these 2 factors.
http://www.datamasher.org/mash-ups/sat-scorescombined-reading-mathematics-divided-people-25-yrs-who-completed-high-school

The following mashup shows sat scores times graduation divided by population. The darker states are smarter per capita.

http://www.datamasher.org/mash-ups/sat-scorescombined-reading-mathematics-times-high-school-graduation-rate-divided-populatio-1

This mashup shows the correlation between the percentage of adults who were told they have diabetes and the number of fast food restaurants per
100,000 residents.
http://www.datamasher.org/mash-ups/adults-who-were-told-they-have-diabetes-divided-fast-food-restaurants-100000-residents-0

This may be outlandish, but if I were in charge of data.gov I would hold an open competition to have individuals run the data and find inefficiencies with the government. It seems like something that would probably resonate with a lot of people, and the reward (while also monetary) is to help the government operate better. This in my mind would certainly generate a lot of attention, but potentially cause someone to lose their job!

If I were in charge of data.gov I would advertise the publicity of data being shown around the internet. I would really try and drive traffic towards the data.gov website and encourage people to use this data freely. Once people arrive at the site data.gov should provide tutorials and walk-thrus for people who would like to learn more about how to properly utilize data. I would also encourage people to share their data outputs for some sort of incentive.

If I were in charge of Data.gov, I would hold events akin to Hack-a-thons, where the open source data could be used and analyzed to solve real governmental issues. This would both solve issues and raise awareness of the valuable resource at the same time.

For data.gov I would make it more visible by advertising it so people know it exists. Also possibly taking user feedback to improve it as well.

If you were in charge of data.gov, how would you encourage people to make better use of its data?

Data.gov needs a radical makeover. This should include a rebranding of the website to include an easier process to obtaining information, as well as including a tutorial section to support new users. Not only does the data.gov website need to improve, but so does the marketing strategy to promote the website. Data.gov should take a few notes from the ‘Obama Care’ marketing strategy.

If I were in charge of data.gov, I would create a portal for people to share their creations based on the government data. This would be split into sections based on the focus of the data and how it is used. I would also consider running a contest for data.gov, awarding a prize to the most innovative use of the government’s data. Ideally, this would result in an innovation that could be used to improve government process, and the prize money would be minuscule in comparison to the cost savings resulting from the innovation.

This mashup shows the relationship between the presence of a loaded gun in a household and deaths by firearm within state populations (per 100k people):

http://www.datamasher.org/mash-ups/households-loaded-firearm-times-deaths-due-injury-firearms-100k-0

http://www.datamasher.org/mash-ups/number-births-times-population-covered-health-insurance#table-tab

This is a data mash of number of births times population covered by health insurance. This could help healthcare companies identify target markets based on knowing the largest pockets of the future population that will be covered by health insurance.

So, if I were in charge of data.gov I would take some pretty innovative measures in order to encourage folks to make better use of the data available. I would ensure to hold an annual national data analytics competition and offer monetary compensation. Furthermore, I would have tiered contests for different age groups and have challenges targeted directly to children, thereby fostering their interest in data.

Q1: I think the problem with data.gov is not a lot of people are aware about the existence of it. In order to make better use of data, firstly, they need people to be aware about the existence, for which data. gov should advertise. It can also incentivize people to provide examples for creative usage of the data on its website. In this way, people visiting their website will be aware and motivated to use the data in a better way.

If I were in charge of data.gov, I would initiate an analytics challenge where people are invited to make a case on how to improve governance with regards to delicate issues in their municipality or state. These cases would then be posted in a separate website, and other members of the community can vote on them. The cases would need to rely on really good evidence in order to quality. This way, the governing bodies can have an idea of what sentiments are being shared by their communities and have an opportunity to improve their practices and provide an opportunity for a truer democracy.

1. If I were in charge of Data.gov, I would take the following steps:
a). Agree with Andy’s idea of holding a competition, this opens up the loop holes and helps me improve the website.
b). Classify data according to categories, provide sufficient explanation to headers and he process through which it was collected.
c) Make data easy to analyze (having proper headers in CSV format)
d). Provide basic information about analyzing data.

1)
If I were Data.gov, I would have focused on making people and organisations aware of Data.gov and its usefulness. Having a marketing plan would help Data.gov increase its presence.

The below mashup shows the SAT score compared to the high school graduation rate. States with high high school graduation rate had people with more SAT scores.

http://www.datamasher.org/mash-ups/sat-scorescombined-reading-mathematics-times-high-school-graduation-rate

I have included a mashup of CO2 Emissions by Fossil Fuel Combustion compared with the States population density. What it indicates is that per person, more densely populated states actually produce less CO2 from Fossil Fuel combustion, which was actually the opposite of what I had expected.

http://www.datamasher.org/mash-ups/co2-emissions-fossil-fuel-combustion-metric-tons-co2-divided-us-population-density-state-s-1#map-tab