Building Better Data Visualization Experiences: Part 2 of 2
As early as 2006, Clive Humby, a renowned British mathematician and marketing genius, declared, “Data is the new oil.” Business intelligence (BI) and data visualization are still ever-growing concerns. Data went from being scarce, expensive, and difficult to collect and process to being abundant, inexpensive, and incredibly difficult to analyze.
This is when the idea of big data emerged. Large amounts of information were so vast that they were hard to capture, store, understand, and analyze using traditional software. This information is useless if individuals can’t understand it. A gigabyte of data housed in a data center is a nuisance, but if properly handled, it may be transformed into digital gold.
However, if you don’t have a Ph.D. in data science, the raw data might be difficult to comprehend. This is where data visualization comes in. Big data is frequently coupled with algorithms to develop predictive analytics for various learning methods that continue to highlight the value of the data. It allows us to change the way we perceive the data in order to gain a more vertical understanding of the information presented and identify new patterns and trends. Data visualization truly empowers users of almost any kind because it gives them specific and actionable perspectives.
Modeling of Graph Data
Data modeling is the process of turning your ideas about your data into a logical model that can be used to make sense of it. During the graph data modeling process, you decide which entities in your dataset should be nodes, which should be links, which should be discarded, etc. The result is a blueprint of your data’s fundamental elements, relationships, order, and properties. You can use that blueprint or schema to create a visualization model for your charts.
“You can add significant value to your users’ experience simply by adding some custom interactivity to your data visualizations using an API and an embedded framework.”João Matos, Data Scientist, Walgreens
Common Visualization Methods
Choosing the Right Data Visualization Style
With so many available, choosing the best method can be critical. Some kinds of data visualization are uniquely suited to the way our minds operate, and we begin to make meaning of them instantly. For example, while humans are extremely precise and efficient in estimating the length of objects and their location in two-dimensional space, we are less adept at rapidly and accurately estimating angles, area, or color.
For this kind of thing, pie charts aren’t very useful. They take integers and encode them as circular slices. This implies that the numerical data is shown in three ways. The angle of the slice, the area, and the outside circumference’s arc length. It is easier to compare lengths on straight lines than it is on curved lines, especially when they do not begin in the same location. That implies these kinds of charts are less effective for comparing statistics with any degree of accuracy. If you have a few slices of the chart that are all drastically different in size, this is OK but requires significantly more effort to interpret than a bar chart containing the same data.
In the case of a dashboard, there are likely going to be several of these on the same page. Thus, the cumulative impact is that it is far harder to discern the information. Along with this, because the statistics on a dashboard change rapidly, there will be frequent and minute changes.
Choosing the Right Data Visualization Type
As mentioned earlier, there are many types of data visualization. Some methods are better suited for customization or ease of use, whereas others may provide more advanced interactivity. Deciding which method is best suited for your needs is important. Some things to consider are listed below.
- Customization – What kind of style and design features are available? Some situations require very strict control of color and styling to stay in line with the brand’s rules.
- Rendering Methods – The render method is responsible for drawing the chart on the page. It is the primary method that has to be called after configuring the options.
- Easy-to-use – One often overlooked area is the learning curve associated with the library. The ability to add new team members and transfer knowledge are important to think about.
- Support and Documentation – In the current era of constantly changing trends in development, documentation is more critical than ever. Without good support, developers are limited in the value they can extract from the data.
- Interactivity – Consider if the data needs to be interactive for the user. Some of the libraries are much more robust in their interactive capabilities.
There are many solutions for integrating your data into custom and powerful data visualization tools available these days. Choosing the right tool and fully understanding how the users intend to use the tool is critical. These kinds of products have UX processes and technical problems that are unique to the tasks they are meant to help people with.
Throughout this article, we introduced a number of important factors to consider before undertaking a data visualization and analytics product design project. These include data modeling, libraries, requirements, and more. I hope that in your next project you will be able to ask the right questions and use your user-centered tools to help individuals learn.