Common Starting Points

You've found what appears to be the perfect dataset for your research! But now you face new challenges: what insights can I actually glean from this data? How does this data help me understand my research questions better? Your challenge is to explore your data to understand it better.

Or: you've just completed your analysis. Your research questions are answered, more or less, and now your task is to communicate the results of your research. How do you help others understand the trends and ideas you've identified? Your challenge is to communicate data effectively to your audience.

Effective Data Visualization

What makes for a good visualization? Nathan Yau, blogger at Flowing Data and author of the excellent Data Points, offers this definition:


"What is a good visualization? It is a representation of data that helps you see what you otherwise would have been blind to if you looked only at the naked source. It enables you to see trends, patterns, and outliers that tell you about yourself and what surrounds you. The best visualization evokes that moment of bliss when seeing something for the first time, knowing that what you see has been right in front of you, just slightly hidden. Sometimes it is a simple bar graph, and other times the visualization is complex because the data requires it." (Yau 2013:xi)

Example: Tree Map Visualization

Tree map visualization of cultural significance

View the "Pantheon: Mapping Historical Cultural Production" interactive tree map visualization.

Steps in Data Visualization

Here are some of the steps you might follow in your data visualization process:

1. Generate research questions. What questions would you like to investigate?

2. Identify data sources relevant for your research. (Need help finding a dataset? Try Asking a Librarian).

3. Use data visualization software like Tableau to explore your data - are there any missing or incomplete values? Do you need to format or clean your data in a specific way? What correlations may exist in the data? (Exploratory data analysis)

4. Clean and format your data as needed (using tools like OpenRefine, Python, etc.)

5. Once your data is ready, carry out your analysis/research using methods from your discipline(s).

6. Compose visualizations to communicate your findings in an effective manner for your users. (Explanatory data analysis/communicating findings).