Muddiest Point Questions from 1/20

Q: How do we know what to prepare when it’s our group’s turn to present?

A: I’ve created a step-by-step guide for this here. If you still have questions after reading this, please let me know and I can clarify and update the instructions.

Q: How can we ensure standardized quality between and among units?

A: We won’t be able to ensure it perfectly, just as we can’t ensure standardized quality between different classes. However, I will be working with each group as they prepare their unit to make sure they meet certain basic requirements.

Q: I’m scared about teaching?

A: First, that’s perfectly normal. Any time we speak in front of others we feel a little scared. But I will be helping guide your group as you prepare, and you’ll be working with a group and not on your own, so there will always be others to help you. We’re not here to evaluate how well you do in front of the class. We just want you to help us learn interesting things. We’re all working on that together.

Q: What is data visualization?

A: It’s depicting some information visually. It could be something as simple as a chart created from an Excel spreadsheet or something as complex as an interactive web graphic. It’s important because humans are generally able to take in information more quickly visually, and are better at finding patterns when information is visualized.

Q: What are some “kinds” of data to use on our visualizations?

A: There really is no specific kind of data for your project that is required, but I do have some suggestions that might help you get started. First, check out the NCSU Code+Art page for information on what types of submissions they are accepting. Then browse Open Access Databases to think about the type of data that you can potentially access. Then think about what type of visualization tool you’d like to use. We’re going to discuss P5.js and Tableau in class.

Q: What other examples of how data is being used that people do not approve of have you actually seen happening?

A: For an in-depth answer, the book Dragnet Nation probably offers the most interesting examples. For a specific example, Facebook’s study on influencing voting in 2014 was probably the most recent example that a wide number of people in my circle discussed. I’ve also published a book chapter about the way businesses use data to construct their site in a way that influences its customers to purchase something through modifications such as the colors used on the page and the size and placement of the checkout button. Yes, these differences actually influence how many people complete orders. My work argued that exerting this influence is actually unethical by the standards of all major ethical systems. Most people probably don’t consider that problematic anymore – but that itself might be problematic!

Q: What other problems might arise if researchers don’t disclose the limitations of their data?

A: From a research perspective, one major issue is that the studies will not be reproducible. Generally, if research is done well, other researchers should be able to complete a similar study and get the same results. If there are different results, this might mean there were problems with the original research. However, if limitations are disclosed, it is not even possible for other researchers to attempt to duplicate the original research.

Q: What are your opinions on big data generating patterns that might not exist?

A: I think the biggest concern is that big data might show connections between things that are not causally related, and this could lead to decisions that are problematic. For example, see this post from Freakonomics about a decision made based on the correlation of the number of books in a child’s home and their success at school.

Q: What do you think will be the impact of big data in 20 years?

A: First, I think it will potentially have a chilling effect on the types of things that we do, out of a concern that those actions are being monitored. For example, there are certain phrases I won’t use or discussions I will not have via email for this reason. However, I also think it will lead to many amazing discoveries that will help make the world a better place, particularly through medical and genetic research. Regardless, I can only imagine that big data will impact almost every facet of our lives, because the way businesses, governments, and even individuals craft their day-to-day activities are increasingly based on insights from data. And yet, much of this will continue to happen in the background as it does now.

Q: What is more important? Visualization or analysis?

A: I think it’s hard to separate these two. One of things that big data is making clear is that there is a lot of analysis that can only be done once the data is visualized. Of course, it’s ultimately the analysis that seems to matter, so in that way it may be more important. But it may not be possible without visualizations.

Q: How could the average person monetize big data?

A: If we’re talking very big data, it’s probably very difficult for the average person to monetize it because of the gap in access to big data that boyd & Crawford discussed in their article. The average person just doesn’t have access to big data. However, the average person can pretty easily get access to a lot of web analytic data, especially for a site that they create. Using this data, many people have created and tweaked websites that allow them to most effectively sell a wide variety of products.

Q: Am I the only one with a positive view of big data?

A: No, not at all. Actually, I would venture that more people probably have a positive view of big data than not. Within the business world, everyone loves it. From the perspective of this class though, we want to take the less common view and think about some of the potential problems that might arise with big data. In my philosophy classes, I often play devil’s advocate for whatever position the majority of my students seem to take. In the same way, we are being more critical of data since most people don’t immediately see it as problematic. Despite this, I think there are some amazing uses of big data and that it has and will continue to lead to amazing discoveries. The Human Face of Big Data highlights some of these well. And we will be ending our class with a unit on big data and the public good.