Lawrence Dignan

When we were distinguishing in class about good and bad infographics, the most noticeable similarity between the bad infographics was the complexity. Because of this, I believe that the simplification principle is the most important when trying to visualize your data. The main point of data visualization is to make it easy for your viewers to understand your information, but it’s easy for your main idea to get lost in the wash if you over-complicate your data and make your visualization hard to read.

I think the most important Data Visualization principle is to simplify. No data set is easy to read or interpret if there is too much data, or irrelevant data in the mix. The point of data visualization is to get a clear picture of the data without having to look through it. Although other principles are important such as comparing and attending, they would not matter if the data was not simplified.

In my opinion, the most important data visualization principle is to be skeptical. Few is correct in stating that most people accept the first answer provided in a data set because it is difficult to accurately perform data analysis on their own. Data can easily be manipulated and anyone can use data in multiple ways to prove their own preconceived biases. It is important to take any “fact” confirmed by data skeptically because certain individuals could have poor motives when claiming what is fact/fiction.

I believe that asking why would be the most important data visualization principle. Asking why further investigates where actionable results come from. Knowing whats happening can be easily be stated, noted, and examined, however understanding why it happened can result in a more beneficial way. For example, if a hazard, such as a fire, were to burn down a building, the “what” occurrence would be the fire which resulted damage to the building, however understanding why the fire was caused the building to burn could be the result of multiple answers. By pinpointing why the fire was caused, fires could be further prevented from the same occurrence happening again.

Of the eight principles, I think “Attend” is the most important. If the data visualization is not drawing a viewers attention, or drawing his or her attention to the wrong thing, the data visualization is not effective. Bringing a viewers attention to the most important information is the main point of creating a visualization.

I believe “ask why” is the most important principle of the data visualization. It’s easy to tell the users what’s the result. However, if a data visualization is failing to address what is the cause of the result or reason of the result, the data visualization is meaningless. By allowing the viewers to understand why such result was drawn, the user can use the data to draw other results at different times.

I think that the most important data visualization principle is to “Simplify”. Data visualization should quantify a relationship by a very select amount of variables. Too often are figures calculated and shown that incorporate too many variables and leave the audience in a state of doubt as to what the relationship is trying to prove. Having a concise and straightforward graphic allows someone to understand the basic relationship between two maybe a few more variables without overwhelming their perspective. Within the other 7 core principles, one can incorporate a variable’s relationship with other variables and compare each relationship to one another. Simplification must give people enough information to be drawn in, but not too much information that they feel overwhelmed or disinterested.

I believe the most important of Few’s data visualization principles is ask why. It is very easy to just look at a set of data and some visualized data, and take it in on the surface. It is crucial to know why the data says what it says, that is how you learn from it most effectively.

I think the most important data visualization principle is simplify. Simplifying the data is important to understand the data. There is a lot of big data and simplifying it into terms that are easily understandable is crucial for data scientist.

I think that Simplify is the most important core principle. That is to restrict the results to simple options that would not overwhelm a user. Tool like Tableau that proactively gives users options to display their data is the ideal tool for users that just want easy to work with information.

I think the most important principle is to simplify. This is because it will simplify all of the data so it becomes easier for the user. When there is too much data that is tried to be put in the same visualization, everything will be confusing. Once simplified, the data can be used to complete the rest of the principles and to capture the best visualization possible.

I believe that the most important principle when it comes to data visualization is the be skeptical principle. It is important to explore the data to find the reason that this data came up. If you can find out why you have a better chance of predicting future data in that field. This can be difficult when dealing with big data but is very necessary to find the underlying reason for the change in data.

I find that “Simplify” is the most important principle. My favorite aspect of Tableau is that it is highly navigable and very user-friendly, and that is very easy to create clean, straightforward data visualizations. It doesn’t take a data specialist to maneuver through the processes of creating data visualizations with Tableau, and so far I have found the process to even be fun, which I didn’t expect.

I see a lot of responses that say simplification is the most important, which is hard to argue with, but to make things interesting I would suggest that asking why is the most important data visualization principle. Asking why is such a critical aspect of data visualization because it the reason–the story– as to why something is happening. It is not enough to have the data, it is integral to data visualization that we know why something is happening. The why helps us decide the next step to take.

I think the most important of data visualization principles is “view diversely” and “ask why”, because different views of data could provide a different insight to information which i think could lead to asking questions. “Ask why” is just as important because after having different views of the information, now you can provide types of questions based on more than one view of the data.

The sports industry has become extremely affected by data science. Unlike the past, General managers in all major sports are not solely relying on scouts evaluating talent. Instead complex algorithms are now created to find talented players that may not catch the eye of the average scout. It is now also possible to create incredibly complex statistics that delve much deeper than the ordinary batting average or yards per game. With raw data collected from games, advanced statistics reveal new insights into a players behavior on the field that wouldn’t be possible 30 years ago. Data Science will have a huge impact in all fields. Businesses will use data science to make decisions and it will be interesting to see whether CEOs start seriously using the meta data they collect or resort to their old way of making a difficult choice by intuition.

According to Richard Beckman, I also believe that the financial industry is mostly affected by data science, most especially with Banking. Vast amounts of data are present in banks consisting of deposits, payments, balances, investments, and interactions with customers. Big data and analytics are also being improved constantly as banks compete for competitive advantages, according to Deena Zaildi in her article, “Data Analytics in Banking.” Also stated in that article, big data and social media create relevant databases that cold help a banks understand its customers’ habits, lifestyles, and appropriate financial products. As innovations and technology continue to improve data science will drive the banking or other financial institute industry.

The industry that utilizes data science the most will be affected by data science. That’s a norm. In my opinion, investment industry is affected by data science the most. Sometimes investors are responsible to manage a huge fund that they are managing for someone else or some other company. I do not hear much of data science incorporated to finance/investing industries, but I think it’s very important for the investors to utilize data science to make a judgment of which ones to invest into and how much to invest.

It is a complicated task to determine which industry is affected most by data science, since it can be used in every industry. In terms of my career, the media field relies on data science in plenty different ways. From social media and finding what elicits views and clicks, to determining television viewership and the impact of online streaming and it’s affect on cable, media can use data science to see what works best. The biggest question in media is the “dying out” of print journalism and cable television, and data science can be used to determine if that is true or not.

I see all industries being affected by data science; it is just a matter of how long it will take for these industries to utilize it. For example, technology and business are two industries that have already been impacted by data science. For industries like art and music, it will take longer to find ways to incorporate data science in a useful way. The industries that take longer to adapt are likely to experience bigger disruptions.

As an actuary, data science is helpful for my career. It allows actuaries to make more insightful decisions. However, some companies have begun to replace actuaries with data scientists because of the similarities between the two roles and data scientists are less expensive.

The industries that I believe are most affected by data science would have to be the investing industry and marketing industry. Both of these industries deal with data science and are significantly affected by data science. Investing includes looking at all the data and making a decision whether or not to invest in what is being looked at. For marketing, data is a necessity to achieve the best profit and see what people are buying. Data science is a huge part in my major and career, which is actuarial science. I will look at data and find the probability of something happening. The risk all depends on the data that is given to me, and has a huge affect on my career.

I believe corporations that rely on profit are immensely affected by data science. The market and intended audience change so frequently, in terms of demand and interests, that monitoring these changes through data is crucial in order to sustain sales. That said, these corporation’s marketing efforts are very important and the need to constantly re-orient the message to gear advertising to a specific market becomes a major challenge. This would effect my career if I were to pursue an editorial position for a corporation as I would need to consult with a marketing team, who would need to be fully informed on our audience’s demands, so that I can write relevant information to that audience to pull in interest and sales — or risk losing both if I miss the mark.

I think the Industry that is most affected by data science is sales industry. In sales, people need to analyze a huge amount of data to predict the current trends. They need to figure out what is the most likely product that customers would buy and Try to keep ahead of their competitors that would do the same. Like the class material, I think that my work would likely be analyzed and my growth be decided on the benefits my works will bring to the company.

Personally, I believe that the marketing field is most affected by data science. In marketing, you have to find who your target market demographic is, where they are, and how to get their attention. Advertising is everywhere, but most of it isn’t just blindly thrown out there. I am a marketing major and most likely will be minoring in MIS so data is about to be a very important part of my life. I have a good opportunity this summer to work for a company where I will be doing market research on their competitors and analyzing big data is probably going to be a daily task.

I believe two industries that are affected by data science are accounting and sales. These two industries need to track data from consumers and analyze it to match current trends that can constantly change due to countless activities. In sales especially, you need to track several different trends in order to streamline businesses. This makes data science very important as well as many other industries that data science is associated with.

One industry majorly affected by data science is streaming services such as Netflix. Netflix wants to know everything a user does on their service to form algorithms to recommend movies, shows, and even create new original series to tailor to the preferences of their audience. With more information and big data, Netflix can better find the trends of their users and keep them coming back for more movies and shows. As an actuarial science major, data is extremely useful. My field is all about calculating risk throughout a given population and with more data, I can make more accurate predictions and create safer and more reliable analyses.

One field that has seen a huge rise in data science and analytics is professional sports. Specifically basketball which used to only record points, assists, rebounds, blocks steals and turnovers; now uses advanced metrics to measure a players efficiency rating, plus minus rating and true shooting percentage. All of which goes beyond the basic statistics the game was founded on. There was even a movie made a movie about data science and baseball (Money Ball) staring Brad Pitt and Jonah Hill!

https://www.vox.com/technology/2018/2/1/16945120/strava-data-tracking-privacy-military-bases
I chose this article because it showed me how scary Data collection can be. Although many argue it to be solely for safety, articles like these that suggest such simple things like fitness apps can put people at such great risk. I’ve always thought of all the pros technology brings, but now it makes me question what are the cons?

https://www.wsj.com/articles/dilly-dilly-bud-light-tries-rallying-wholesalers-as-sales-slump-1517567401

I chose this article because I think Anheuser-Busch created the best marketing campaign I have ever seen. Budweiser is all in on “Dilly Dilly” and the toast has now become a social media catchphrase. Budweiser’s market share has been slowly declining since 2004 and just last year they had suffered a 2 million barrel loss in volume sold. Decreased volumes sent Bud’s pretax earnings per case down 2% last year at one distributor in Florida. Bud is betting big on this marketing campaign and from my personal experience I believe it is paying off. Time will tell how that will increase sales and/or market share, but they are definitely on the right trajectory. Dilly Dilly.

https://www.forbes.com/sites/baininsights/2018/01/22/breathing-new-life-into-life-insurance/#7b4e39a04819
I chose this article because it is about life insurance and the price increase over the years. I think this article is interesting because even though insurance companies are trying to reduce the price of their plans, the cost is still rising due to bloated costs. I also found it interesting that the productivity of agents has been slacking, which can be another reason why the prices are not being trimmed.

https://www.theguardian.com/world/2018/jan/28/fitness-tracking-app-gives-away-location-of-secret-us-army-bases
I found this article on Flowing Data and was directed to The Guardian. The article talks about how Strava had released a map detailing the routes taken by the app’s users which included active soldiers. This article should serve as a warning to people to be more responsible with what they are mapping. This data release by these actives soldiers reveals our military bases and routes in the US and in hot zones such as Afghanistan. This map could be used to attack these bases.

https://www.economist.com/news/leaders/21721656-data-economy-demands-new-approach-antitrust-rules-worlds-most-valuable-resource
This article talks about how data is becoming the worlds most valuable resource. It looks into big companies like Amazon, Apple, and Facebook and talks about the data they have gathered and how it can be resourceful to us. It compares how companies compete in the amount of data they gather through the internet from computers to smartphones. Primarily, this article talks about how much technology has changed with introducing data and that large resources like oil are not as important as they used to be by society.

I found this article interesting because in today’s world Artificial Intelligence (AI) has grown tremendously in the past few years, and is going to continue to grow. This article specifically talks about Deep Learning (DL) updates in artificial intelligence which trains machines to act like humans. Right now data security is going to be the most important part of the data science world in 2018, right now many of the Deep Learning AI systems will fail the security regulations set forth. General Data Protection Regulation (GDPR) will go into effect by May 25, 2018. Currently I am a marketing major and likely to be an MIS minor, I will need to be well informed about AI systems and data acquisition to stay up to date with our expanding technologies.

Deep Learning Updates: Machine Learning, Deep Reinforcement Learning, and Limitations

This article examines the bias of news media globally. As someone who studies media, this is an important question and issue, since it is difficult to report without any bias. The data shows that the majority of people believe news should be reported without any bias, but, globally, only a median of 52% believe it is being done well. Interestingly, it showed that Latin America is the most critical, while sub-Saharan Africa and the Asia-Pacific Area had the most trust.

Publics Globally Want Unbiased News Coverage, but Are Divided on Whether Their News Media Deliver

I came across an article about data literacy, which is a topic relevant to my major and my intended career path. This article discussed findings from Qlik, an organization dedicated to improving data literacy, which concluded that deficient data literacy in the workplace is keeping employees and companies from making “actionable insights,” or strategic, purposeful, and objective decisions driven by evidence and numbers. It is especially crucial for companies and organizations to make data literacy a priority as data continues to be created at a rapid pace; this resource will ultimately help them keep up with the ever-changing cultural, social and economic landscape. With this information, depending on the organization, they will be better able to serve their audience, whether they are attempting to make a profit or successfully implement outreach programs.

https://www.businesswire.com/news/home/20180129005256/en/