Jeta Gjana

Out of the eight principles I think Compare is the most important because this step shows what data is better than the other. If a company wants to know whether they should keep producing a product or not, the company would compare data from previous years to figure out if they gained or lost money. Comparing data allows one to question what happened or how can we improve the data for the future.

Simplify principle is the most important because talking about data is one thing but having a visual of your data can back it up and make the information you are displaying more effective and believable.

Of the eight principles Few describes, I believe simplify to be the most important. In this day and age were people consume more data than they realize, having something simple and powerful is what stands out. By keeping data simple it allows it to be understood by anybody. A good data visualization can be more complicated, but the most effective ones are the simplest.

According to Few, good data visualization should take the burden of effort off the brain and put it on the eyes. From the eight core principles he mentions I think that “simplify” is the most important. I believe that because we are surrounded by an enormous amount of data and it is an artistry to pick and choose, and present complex concepts in a way that can be understood in one glance.

Simplifying data is most important because a visualization make things much easier to comprehend. Simplifying data helps give the viewer a better understanding of what they are looking at instead of someone trying to retain a list of random data sets.

Of the eight principles, I would argue that Compare is the most vital. Data/information can be completely useless without a comparison of other data sets. For instance, how can a data analyst at a corporation report effective information for decision-making without knowing which data is correct? The entire process of data collection is to compare each component of data/information to other pieces, hoping to conjure a basis to make a decision.

I think that all of the eight principles are important but if I had to choose one I would choose the ‘ask why’ principle. We do a lot of things in life but without an explanation as to why we do it it is almost meaningless to us. Especially when you are dealing with confusing data and numbers I think it is crucial to ask why instead of just throwing numbers around without a real reason. Asking why is an important part in many aspects of life not just data.

I think that the most important principle is simplifying. I think this one is the most important because data can be very complex and intricate. The data would make no sense to anyone if it isn’t broken down. In order to create a visual presentation, and so the data can be understandable it should be simplified.

Out of the eight principles listed in the article, I think that simplifying is the most important. As the word suggests, keeping things/data simple is the most beneficial form of presentation because it makes voluminous data easy to comprehend and reduces ambiguity through a straightforward display. Simple data can also reach out to a larger section of the population, as even uneducated people may be able to benefit from easy to read infographics.

Of the eight principles, I would argue that the ask why is the most important. I feel as though to run a successful business you need to be able to know what the data is telling you and why it is happening. If there is a problem and you do not know why the problem is occurring you can not fix it.

I believe that simplifying is the most important of the 8 core principles. Simplifying the data gives you the basis to execute the other principles. Without simplifying the data, it could be difficult to compare or draw any conclusions from the data. When your data is simplified, there is minimal to no confusion and all of the irrelevant information is pushed aside so that you can focus on the problem.

Of the 8 core principals of data analysis Hoven provided, I believe it is most important to remain skeptical. Too often data visualization can be misleading, intentional or otherwise, due to misrepresentations and poorly executed delivery of information. Modern data analysis tools such as Tableau allow researchers to delve into the various relationships present and form a greater understanding of the information overall.

To me, the most important principle is to be skeptical. A lot of times, we get answers and look no further when often, that answer is incorrect. Sometimes, if we had just questioned the answer a little bit and did some double checking to make sure the answer was right, we might find our mistake before someone else points it out. With data, this is especially true because analyzing the data can seem so intimidating that reanalyzing it is even more overwhelming but a little extra work can make a lot of difference.

Niels Hoven presents 8 core principles to data visualizations in his work “Stephen Few on Data Visualization: 8 Core Principles,” in which, in my opinion, simplify is the most important. I believe that it becomes a simple task for people to be able to find the data visualizations, but the difficult task comes when they have to capture what it is all about. It is an important step to simplify because it is essential in realizing what the overall purpose of the visualization is.

The most important principle is “Be Skeptical” without this step we would never look back and second-guess us. So we would just believe the first thing we do. Which often the first time isn’t the correct way or product.

in my opinion,i think all of those core principles are important, but if i have to pick one, i would choose the simplicity, because in order for the data user to use the data visualization, the visualization should be easy to understand as well as attractive to readers. data visualization could be long and detailed but it should be simple enough to readers so they can continue to look at it.

After reading the Hovan article, I believe that the core principle of ‘be skeptical’ is the most important. While data visualizations are a great tool to have, data can often be misleading or biased towards a certain opinion to get the reader to believe what they are saying. By validating the data presented in front of us, we will be able to have an accurate representation of what we are trying to accomplish.

I think simplifying the data is the most important principle. Without simplification, data would be complicated and difficult to follow. It’s one thing to have good data, but if nobody can understand it it does not matter how good the data is. It is easier and more beneficial for everyone if data is simplified, which is why I think it is the most important principle.

Of the 8 Core Principles “Be Skeptical” is the most important. If we do not question the results of our data and just take our answers from the data analysis we are not learning all we possibly can from the data. Hoven points out that to dig deeper with the research is difficult because of the lack of tools to allow us to do so, but when we do find the right tools, we can uncover another level of information we couldn’t before. At the end of the day, we are analyzing the data to learn the most we can, so why not get the most out of it that you can?

Data visualization is a very exciting topic because of its quick growth in importance for so many aspects of business. I believe the most important aspect Stephen explores is simplify. When evaluating any object of data visualization it is key that the information is translated easily. Something too complex can create confusion and actually counter act the purpose of the piece which is to convey information in a easy to view way.

Simplify. I believe with data, numbers, and graphs the average person feels intimidating by its presentation. This problem can be address by not oversimplifying your data, but making it easier to read, this can be done by choosing the graphs where the data is capture correctly and it’s presentation is not overwhelming. To me this is why simplifying is so important.

Data visualization is a very exciting topic because of its growing importance in so many aspects of business. I believe of the principles explored by Stephen the most important is simplify. The purpose of data visualization is to make it easy for someone to understand a more complex set of data. By making a piece too complex this is blocked and can create more confusion than what would’ve originally occurred.

I believe that the simplification principle is the most important of the eight mentioned in the Hovan’s article. Data visualization is a tool that is extremely effective in understanding data, if the information conveyed inthe visualization is obscure for common folk to read it is useless. I also believe “be skeptical” is important but if a person can’t understand data at all then that data becomes just as useless as biased data.

I think that the most important important principle of the 8 listen in the Hoven article is the “Attend” principle. It focuses on ensuring that one isn’t thrown off by irrelevant data and is focused solely on information significant to what one is trying to find out. A tool that allows us to focus solely on data that is important to the topic in hand is the most important thing of all because what good is data if you don;t know which data sets to attend to.

Simplifying the data visualization is the most important step in the whole 8 step process in my opinion. The whole point of info graphics is to take the strain off of your brain, and put it on your eyes. So without simplifying the data the whole process is ruined. We also have to keep in mind that not everyone is a data analyst, so therefor keeping the models simple is the best way to present and sell whatever it is that you’re researching.

Simplifying is the most important out of the eight principles. We live in a society that constantly throws data at us every day and people need to be able to quickly absorb and internalize the data they are working with. Simplifying the data ensures that many more people will understand the data and be more interested in what it’s saying whether it’s in a company or social setting.