Introduction
Making sense of relationships among values and comparing those values is a primary action for day-to-day business decisions. Whether comparing sales revenue across different channels, regions, or products, presenting data in visual form reduces cognitive load on the brain and makes understanding simple.
Two-way tables suit categorical comparison best. However, visual representation of the same data communicates more intuitively and quickly. Data visualization reduces cognitive load and simplifies understanding through visual representation.
Visual Representation Benefits
We've discussed preattentive attributes that leverage our ability to quickly understand visually represented data. This article explores how simple visual patterns simplify data analysis at a glance.
Categorical Comparison
Bar Charts
Bar charts excel at comparisons. When viewing bar charts, observers notice height differences and then seek legends providing context. For example, comparing marketing campaign success levels for different regions by analyzing tweets.
Bar charts enable adding comparisons for different channels (Facebook, Twitter) used in marketing campaigns. Additional bars for channels allow categorical comparisons at the user's choice.
However, comparing very high numbers of categories on one bar chart loses effectiveness. In such cases, treemaps provide better solutions for quickly answering multiple visual queries.
Finding Trends
Line Charts
Line charts spot changes over time periods. Beyond finding increasing or decreasing trends, they reveal emerging patterns showing fluctuations or repetitions. Data patterns with steep or gradual increases communicate much about data context.
Example: Comparing sales data over time for one product with another product launched for the same customer segment.
Trend Comparison
Multi-axis line charts compare trends using different y-axis scales for distinct datasets. This visualization shows difference between trend types (increasing vs decreasing), though intersections don't represent specific meanings (unlike single-axis charts).
Sparklines
Sparklines represent trends over time without axis requirements, excellent for at-a-glance information.
Small Multiples
Splitting data into small multiple charts (trellis), places graphs in close proximity for examining values and patterns.
Example: Salary expenses represented by each department over time. IT department salaries grow consistently while Accounting department salaries remain stable from September onwards.
Part of Whole
Pie charts visualize part-of-whole relationships, but increasing slices make differentiation difficult. Stacked bar charts offer superior part-of-whole visualization, enabling categorical value comparison over time periods.
Example: Sales data as stacked bar charts quickly shows that the East zone had most sales in March, while the South zone had lowest sales in February.
Conclusion
Categorical comparison, trend identification, and part-of-whole visualization become quick and intuitive through simple visual formats. Numerous advanced visualizations reduce cognitive load further and simplify analysis by finding right patterns in data, especially for multidimensional data. Future articles will explore additional data visualization methods and emerging patterns.