1. Strategies for creating impactful cataloging visualizations
One note before
Last year I took a graduate school class in data visualization. We started every class by looking at one visualization and critiquing it. Discussing what we liked and what we did not. We almost always had large disagreements about what worked and what did not. One of my biggest take aways from that class is that there is not an unanimous opinion on what works well and what does not. The very thing you may like a lot, others may think it does not work. So try to remain open to many view points. Also I would like to stress that when I am critical of various aspects of a visalization I am not trying to be mean or insulting. I admire everyone who puts their visualizations up in public and my comments are merely suggestions to be considered.
Sarah Theimer
Catalog and Metadata Librarian
University of New Hampshire

2. Talk Outline Why should catalogers care? Principles and steps Examples
This talk has three sections. I will start by talking about why catalogers should try to use data vizes. I will discuss basic principles and the stages. Hopefully the largest prt of the talk will be walking through an example of a data viz construction

3. Why should catalogers care?
Human attention is a scarce commodity
No matter what our role, we need to communicate our value clearly and effectively
A data visualization presents data in a way that exploits our visual perception abilities to amplify cognition.
Creating visualizations blends skills from cognitive science, statistics, graphic design, and computer science.
Visualizations should be attention grabbing and interactive
Example 1
Example 2
As a unit head I spend a fair amount of time running and writing reports. I write annual reports for my unit, I write my self evaluation, I need to justify the time and money spent on projects and vedors. I look at changes in format, in quality control checks. Until recently I communicated the results of this in charts and in text. It is in my best interest for all of these to have the greatest impact possible. Anything that increases the impactfulness of these is in my and my units best interests.
Human attention is a scares commodity. I could write a brilliant report. But if no one reads or or if people only skim it, it is not having the desired impact. The idea behind data visualization is that our visual perceptions move more quickly than our thought process. Our attention is drawn in by images and color. Studies have shown that when text and visuals differ people believe image. We need to take advantage of these facts when we are communicating.
To examine this point further lets look at a couple of examples

4. This is an example of the classic way to display data
This is an example of the classic way to display data. We have columns and rows. We even have slightly different colors in our rows that help us read across a long line of numbers. It works but it doesn’t really grab attention.

5. Example 2
Here is the second example from Nevada State. Many people would find this more interesting. It has a varity of shapes. Jagged at the top and three circles at the bottom. It also has bright colors. By the way if we were in my class and we were critiquing this some people would be ctitical of the use of pie charts when the wedges get that small. You can decide if that is an issue that bothers you.
If we can go out to tableau public we can try out the interoperability. When you click on the freshmen wedge of the pie chart. All the other parts of the vislization are limited to freshmen. We can see they come at very specific times of the semester. The sophmores come more often. The juniors come more and the seniors come in the most regularly. This interactivity helps them feel empowered and allows them to investigate various aspects of the topic.

6. Tufte’s Principles (what to do)
Graphical excellence consists of complex ideas communicated with clarity, precision and efficiency.
Graphical excellence gives the reader the greatest number of ideas in the shortest time with the least ink in the smallest space.
Example (Could be better)
Before leaping into visualization construction lets go over a few of the basic pronciples. The New York Times called Edward Tufte the Leonardo da vinci of data visualization. His pronciples are straightforward, emphasizing the need to be clear, precise, and efficient. These ideas all apply to all forms of communication, oral written and graphic. Don’t get overly flowery with words or with images. If you can get a point across with one image don’t use four.
His second point is similar to the first in that we want to be as impactful as possible with the fewest number of words or in the case of graphic the least amount of ink. We want the reader to be inspired by the data and have ideas, hopefully this may happen when the data is interactive.

7. Tufte’s What not to do
“Beautiful visualizations are a byproduct of the truth and goodness of the information”
Your visualization is only as good as your data
You should not lie
Don’t include ‘chartjunk’ (unnecessary elements). Discussion ensues.
Example
There are certain things we want to avoid when creating visualizations. You are only as good as your data. You must have reliable data as your foundation or you cannot build anything good or meaningful on top of it, Do not use data if you have many missing or null values. Do not use survey data if it is a badly done survey. When possible clean your data before using it. When you have dramtic outlier values look to see if there is a mistake somewhere.
A second thing to avoid is lying with your data. If you are only using part of a data population you should say that. If you cleaned up the data to get rid of the outliers you should say that. If it is a sample make sure you are using good sampling techniques.
Tufte coined the term “chartjunk” It means unnecessary elements. In my data visualization this is one of the items we often disagreed on. Some people see text blacks as junk others find them helpful. One persons junk is another persons useful information.

8. Steps to build a visualization
Identify question to explore. What do you want to learn about?
Cataloging examples: What are our costs? Adherence to standards? Maximized use of available resources? Capacity vs demand? Production and performance measures?
STEP 2
Identify the data you need to answer that question. If you don’t have what you need you may need to start collecting it on a short term basis.
STEP 3
Play with the data. Create multiple visualizations. Ask for outside opinions
There are 3 simple steps to building a visualization. First identify the topics you want to explore. In a cataloging department you may have questions about vendor supplied records compared to locally created records, what are the trends in format purchasing? What about the cost, the time the size of our records. Has gift cataloging increased or decreased does that depend on the subject?
The second step is to identify the data upi need and the data you would like. This may be data from circulation or aqcuisitions, cataloging. It may be that you need data you do not have. You can asl people to start collecting it or you can accept that you will not beable to get that data.
The third step is to play with the data, Make different visualizations and see what they tell you. Ask people for their opinions. Ask other people to play with them Get feedback. During this process you may identify data that you wish you had included. You might need to go back and get it. You may identify other topics you need to create visualizations around. It is an iterative process.
Now lets look at an example.

9. I don’t have a slide for step one so I will just talk about it
I don’t have a slide for step one so I will just talk about it. In my cataloging unit we wanted to compare the work that is done by vendors and the work done by local cataloging staff. Especially considering the fact that over 90 percent of our library budget is going to electronic resources. What can we say about the material that we buy that is not electronioc.
My second stpe was to identify my data. You can see on this excel sheet some of the data that I harvested. Bib Level, location, cat date and so on. Notice that I have circulation data so we know I looked beyond the marc record. I want you to pay special attention to columns F, G, and H. I started with a call number. From that I created a column with the class number. I then created two separate columns. I used the LC Classification table for Broad and Broadest subjects based in the class number. What you cant see from this slide is that this spreadsheet went out to column U I harvested a lot of data in case I needed it.
The third step is to start playing with the data. Make different visualizations and see if anything jumps out.

13. Visualization resources
Tableau public:
There are many places to get Tableau help online.
Many talks from the annual conference are on Youtube.
The Visual Display of Quantitative Information. 2nd ed. Edward Tufts
Thinking Fast and Slow by Daniel Kahneman
More Damned Lies and Statistics: How Numbers Confuse Public Issues by Joel Best
If you want to learn more about tabeal there are many good online sources. If you go into Tableau public you can download thise workbooks and look at the data and the dashboards to see how those worked,

14. THANK YOU! QUESTIONS?
This is a picture of the UNH Wildcat being admired by a timid dog.
I encourage you not to be afraid of experimenting with your data. Stand up to your scary wildcat.
Thanks