“If [this] data were published in books, a bookshelf 450 miles long would be required to hold them” (Kroenke & Auer). This quote from “Database Concepts” made me think about different ways of representing the data in a database. It could be written out in physical books, stored in limitless tables online, or it could be visualized. Since, according to this article, the “largest databases are those that track behavior,” I wanted to find a metadata visualization that could communicate that type and that volume of information.
Data Paris (http://dataparis.io/#) is a visualization of the city of Paris in the form of metadata. At first glance there are a lot of different buttons that I’m not sure what to do with, and this is a problem that I’m assuming one runs into when trying to turn so much data into a simple graphic – it would make sense to translate the idea of rows and columns (from a traditional database) into this visual, because the logic of such a structure would be easy to understand. I began to understand the website after playing around with it for a while, but the context of “Paris” was lost for me because I am not familiar with the area. I did, however, find patterns in the metadata that I wouldn’t have been able to detect as easily without visuals. I started by looking at areas with the least amount of single people. I noticed that these areas also had the most married people, most retirement aged people, least population density, most home owners, and highest home prices. All of these metadata statistics made sense that they would go together, so it was cool to click on metadata categories and predict which areas would light up. I had to make these data connections myself, but the visuals confirmed the predictions I had made based on previous information that I had gathered from this metadata visualization.
Another metadata visualization source is http://create.visual.ly/, which allows anyone to create visualizations based on their own or chosen metadata. For example, you can log into Facebook and if you have a Page, you can see basic stats of page fans such as demographics and geographics, how your page is doing in terms of shares, views, and clicks, and data about use over all time vs last 30 days. Another visualization on this website allows you to log into twitter and search any hashtag to see metadata about its lifetime, common sources, and twitter accounts with the most influence on the hashtag. These visualizations are great ways to show relationships between gathered, available data. It puts metadata into context because it is very specific and relevant. This also means, however, that these visualizations stay very basic. They can only give you access to a limited amount of metadata and in a very specific context, but they still give a nice simple visualization timeline that provides insight through contextualized knowledge about the data.
Overall, visualizations are a great way for people to make sense of databases and turn data into knowledge. They provide a seemingly simple process and are an enjoyable way for users to learn.