I’m doing some form of data visualization learning for 180 days straight because I need to #JFDI. See post explaining how and why I’m doing this.
Decomposition of a Visualization:
- What are the:
- Variables (Data points, where they are, and how they’re represented):
- Number of home sales: size, shape, color
- Months of Supply on x-axis
- % Change in Price on x-axis
- Dates on y axis
- Number of homes x-axis, bar charts
- Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
- Quantitative, Continuous
- Encodings (Shape, Color, Position, etc.):
- Shape, Color Hue, Position, Area
- What works well here?
- The interaction to zoom in on specific dates
- What does not work well and what would I improve?
- The tooltip data is hard to follow given the small size of the time series graphs in the panes.
- What is the data source? Do I see any problems with how it’s cited/used?
- Tableau mentioned “one Seattle blogger.” Suspect but they’re putting this up to sell Tableau, not necessarily convey info like a journalist or even advertiser would.
- Any other comments about what I learned?
- Color contrasts on Tableau may need to be handled carefully, some default settings how a high contrast without obvious meaning
Reading and Learning Data Visualization Theoretically/Critically:
- If it isn’t fast in the database, it won’t be fast in Tableau. If your Tableau workbook is based on a query that is slow to run no matter what tool you use to submit it then your workbook will in turn be slow. If it isn’t fast in Tableau Desktop, it won’t be fast in Tableau Server. A workbook that performs slowly in Tableau Desktop won’t get any faster by publishing it to Tableau Server.
- Think about the experience of using this dashboard and think about things flowing in a natural path, left to right, top to bottom.
- Relational data sources are the most common form of data source for Tableau users, and Tableau provides native drivers for a wide selection of platforms Partitioning a database improves performance by splitting a large table into smaller, individual tables (called partitions or shards). This means queries can run faster because there is less data to scan and/or there are more drives to service the IO. Partitioning is a recommended strategy for large data volumes and is transparent to Tableau. Partitioning works well for Tableau if it is done across a dimension – e.g. time, region, category, etc. – that is commonly filtered so that individual queries only have to read records within a single partition. Be aware that for some databases, ranged date filters (not discrete filters) are necessary to ensure the partition indexes are correctly used – otherwise a full table scan can result with extremely poor performance.
- Beauty in dashboards = meaningful design
- Simplicity: When the user is presented with the dashboard it should be easily understood at a glance and not overwhelm.
- Clarity: The golden ratio is most helpful in achieving balance on this kind of dashboard. Human minds seek harmony and balance. Use it as a general guide. You don’t need to rigidly calculate the size of each view or place them exactly according the dimensions of the golden ratio.
- Efficiency: Dashboards are supposed to make things easy. Give users option to filter and break it down and have summarizations if possible.