How do you choose the right visualization for small datasets?

While handling data, verifying its source for accuracy is something I have always found to be of utmost importance. Better the input, better will be the output. The data can be in structured or an unstructured format. Differnt approaches are required to present each of such data points and sometimes converting the data in the right manner for consistency is an important step as well. In any data, outliers skew the data and the decision making process eventually if not addressed appropriately. Data needs to be sorted, arranged in a logical fashion and depending on the variables, the tool that can be used shall be decided.

When working with small datasets as a data analyst, opt for simple and clear visualizations that highlight key insights. Bar charts are effective for comparing values, line charts for showing trends, and pie charts for displaying parts of a whole. For example, if analyzing sales performance across a few months, a line chart can reveal trends over time, aiding in quick comprehension and decision-making without overwhelming the viewer with unnecessary complexity.

To choose the right visualization for small datasets, understand your data, identify the main message, select appropriate chart types, consider data distribution, avoid complex visualizations, use color sparingly, label axes clearly, consider interactive features, and think about readability. Evaluate alternatives, consider the context, and seek feedback before finalizing the visualization. The goal is to make complex information more accessible and understandable, enhancing the clarity of your message while maintaining simplicity and accuracy.

Different data visualization tools can be used depending on the purpose of the decision making whether its descriptive, diagnostic, predictive or prescriptive in nature. Furthermore, for each of these decisions, the target audience differs such as analyst may need to research documents so the visualization required by them will be more detail oriented than a stakeholder looking at performance of the quarter. Choosing the appropriate visualization tool based on the purpose is key here. Recent data visualization Tools such as Tableau, Power Bi are gaining popularity because of the easy user interface and ability to rearrange based on specific needs.

There are a few underlying characteristics of any database such accuracy, simplicity, ease of navigation, relevance to the topic discussed, comprehendible, etc. Each of these determine the usability of the data shared. The most accurate data shared in the wrong format may be deemed unfit and vice versa. Factors such as data source (whether holistic and reliable), format used (whether apt for the target audience and the nature of data in consideration) and scalability (whether its a one time extraction or will be consistently used in future) must be considered for determining the best layout.

Choose the right chart type: There are various types of charts to consider, such as bar charts, line charts, scatter plots, pie charts, and more. Each has its strengths and weaknesses. For example, bar charts are great for comparing categories, while line charts are effective for displaying trends over time.

Experiment and iterate: Feel free to experiment with different visualizations to find the one that best represents your data. If one approach doesn't work well, iterate and try a different visualization until you achieve the desired clarity and impact.