Data analysts aim to support product teams in making informed decisions by presenting research findings.
Among daily tasks, visualizations that reveal insights hidden in tables of numbers are crucial, and analysts must master data visualization to effectively bridge the gap between data and product teams.
Data analysts face the challenge of presenting data in a way that does not lead to misinterpretation and erroneous conclusions.
A well-known example that highlights the importance of visualization is Anscombe’s quartet; Four data sets with the same summary statistics show quite different structures when plotted.
This shows that manifestations need to be visualized for accurate data representation and that visualizations are essential in conveying research results.
Data visualizations are used for two main purposes: exploratory and explanatory analyses.
Exploratory visualizations are more informal, representing the analyst’s “private conversation” with the data, and often lack design details.
In contrast, explanatory visualizations are targeted to a specific audience and focus on context and detail to convey information effectively.
