Saturday, July 11, 2020

Can Data Visualization Improve The Mobile Web Experience?

Since more and more web traffic comes from mobile users, our websites need to be in the best position to serve them. The easiest thing to do would be to remove unnecessary content from the site. However, it may not always be the best solution. In this article,  proposes some ways to turn essential content into graphics to conserve space, create a more engaging UI and preserve the overall integrity of your content on mobile.

It can be tough to prioritize the mobile experience when it often feels like a compromise. Don’t include as much text. Remove some of your images. Stay away from features that get in the mobile visitor’s way. It’s kind of like a parent who tells you, “Go out and have a good time, but don’t do X, Y or Z!”
It’s not necessarily that a mobile visitor needs a shorter page, less text or fewer images to more easily consume content on a smartphone. They just need the content you give them to not feel like so much work.
If you look more closely at your pages, you may find that some of the written content can be converted into data visualizations. So, today we’re going to look at some things you can do to start converting more of your content into graphics and enhance mobile visitors’ experiences in the process.

Many UX designers are somewhat afraid of data, believing it requires deep knowledge of statistics and math. Although that may be true for advanced data science, it is not true for the basic research data analysis required by most UX designers. Since we live in an increasingly data-driven world, basic data literacy is useful for almost any professional — not just UX designers.
Aaron Gitlin, interaction designer at Google, argues that many designers are not yet data-driven:
“While many businesses promote themselves as being data-driven, most designers are driven by instinct, collaboration, and qualitative research methods.”

— Aaron Gitlin, “Becoming A Data-Aware Designer
With this article, I’d like to give UX designers the knowledge and tools to incorporate data into their daily routines.

But First, Some Data Concepts

In this article I will talk about structured data, meaning data that can be represented in a table, with rows and columns. Unstructured data, being a subject in itself, is more difficult to analyze, as Devin Pickell (content marketing specialist at G2 Crowd, writing about data and analytics) pointed out in his article “Structured vs Unstructured Data – What’s the Difference?.” If the structured data can be represented in a table form, the main concepts are:


The entire set of data we intend to analyze. This could be, for example, an Excel table. Another popular format for storing datasets is the comma-separated value file (CSV). CSV files are simple text files used to store table-like information. Each CSV row corresponds to a row in the table, and each CSV row has values separated (naturally) by commas, which correspond to table cells.

Data Point

A single row from a dataset table is a data point. In that way, a dataset is a collection of data points.

Data Variable

A single value from a data-point row represents a data variable — put simply, a table cell. We can have two types of data variables: qualitative variables, and quantitative variables. Qualitative variables (also known as categorical variables) have a discrete set of values, such as color = red/green/blue. Quantitative variables have numerical values, such as height = 167. A quantitative variable, unlike a qualitative one, can take any value.

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