Purpose-driven dashboards:
The idea of a “purpose-driven dashboard” is that every dashboard should have a purpose that is clearly communicated. Braden’s group does a good job of clearly communicating that purpose in the sub-heading underneath the title, to examine unemployment rates/counts across industries. The title “unemployment across industries” is short enough, but the sub-heading further elaborates on the purpose specifying unemployment measured through rates and counts.
Choose defaults wisely:
The idea of “choose defaults wisely” is that interactivity should not be necessary, and the dashboard should be self-sufficient in communicating without interactivity. Braden’s group could use some improvement in this area. In this case, the dashboard requires interactivity to fully represent its purpose. The default values for industry and years seem to be chosen arbitrarily, and interactivity is required from the user to capture the full range of industries and their relations with unemployment.
Less is more:
The idea of “less is more” is to stick to the purpose of the dashboard. Here not only do we need to have a purpose, but to effectively communicate that purpose with as little as possible, a simplistic approach. Braden’s group does a good job of not adding additional unnecessary interactivities in the dashboard, and sticks to simple graphs that are comfortable for the user to look at that clearly conveys their purpose. For example, line charts are effectively used to convey less is more.
Allow users to filter data:
The idea of “allow users to filter data” is to allow users to interact with parts of the data they are interested in, out of a large dataset. Braden’s group does an especially good job of allowing users to filter data through their drag and select interactivities. The dashboard provides a plethora of visualizing options that cater to the interests of the user. For example, the user can specify many combinations of year ranges and many combinations of industry types.
Overall, the interactivity definitely helps answer the research question, but without the interactivity the scope of the research question is not fully captured. The visualizations are effectively displayed because they filter the interests of the user in two simplistic but effective steps. First, the user selects the tab of their interest out of three tabs, and second, the user uses the interactivities to further specify their area of interest in this dataset. The visualizations can be improved by having default values that communicates all of the research question without forcing users to use the interactivities. But all in all, the research questions are well-chosen, in the sense that they adhere to the choice of visualizations effectively. Great job!
Purpose-driven dashboards: The idea of a “purpose-driven dashboard” is that every dashboard should have a purpose that is clearly communicated. Braden’s group does a good job of clearly communicating that purpose in the sub-heading underneath the title, to examine unemployment rates/counts across industries. The title “unemployment across industries” is short enough, but the sub-heading further elaborates on the purpose specifying unemployment measured through rates and counts.
Choose defaults wisely: The idea of “choose defaults wisely” is that interactivity should not be necessary, and the dashboard should be self-sufficient in communicating without interactivity. Braden’s group could use some improvement in this area. In this case, the dashboard requires interactivity to fully represent its purpose. The default values for industry and years seem to be chosen arbitrarily, and interactivity is required from the user to capture the full range of industries and their relations with unemployment.
Less is more: The idea of “less is more” is to stick to the purpose of the dashboard. Here not only do we need to have a purpose, but to effectively communicate that purpose with as little as possible, a simplistic approach. Braden’s group does a good job of not adding additional unnecessary interactivities in the dashboard, and sticks to simple graphs that are comfortable for the user to look at that clearly conveys their purpose. For example, line charts are effectively used to convey less is more.
Allow users to filter data: The idea of “allow users to filter data” is to allow users to interact with parts of the data they are interested in, out of a large dataset. Braden’s group does an especially good job of allowing users to filter data through their drag and select interactivities. The dashboard provides a plethora of visualizing options that cater to the interests of the user. For example, the user can specify many combinations of year ranges and many combinations of industry types.
Overall, the interactivity definitely helps answer the research question, but without the interactivity the scope of the research question is not fully captured. The visualizations are effectively displayed because they filter the interests of the user in two simplistic but effective steps. First, the user selects the tab of their interest out of three tabs, and second, the user uses the interactivities to further specify their area of interest in this dataset. The visualizations can be improved by having default values that communicates all of the research question without forcing users to use the interactivities. But all in all, the research questions are well-chosen, in the sense that they adhere to the choice of visualizations effectively. Great job!