Data Bending


We respond to data differently depending on its presentation.

A tightly filled table with sprawling labels and an endless depth do nothing to engage the viewer, and if the contents are foreign enough, you’ll find a user lost in a sea of confusion. There exists various ways to transform data into living, breathing entities. We can colorize, animate, and build the interface in ways that help convey the insight portrayed within. Flow is also important. We need to think about data in relation to the user story and how it contributes to the application as a whole. If all of this information is at the forefront, it needs to be engaging. First impressions matter.

Thankfully, good design decisions make all the differences in the world. You’ll convert new users who share the same data-driven appreciation. In a world brimming with I/O, it has never been more important to process data in meaningful ways.

Data is becoming more intimidating, and its never-ending volumes can present tricky problems. In order to obtain appropriate information from massive amounts of data, we utilize machine learning techniques. Researchers in this field investigate the ways in which programs can “intuitively” consume data on scales so massive it would be impossible otherwise. The amount of data humans and programs output each year have become too large to consume and it’s becoming vital to deploy programs that teach themselves the filtering process. Unfortunately, machine learning is beyond the scope of this post, and perhaps this entire blog. We will focus instead on the steps succeeding a successful data extraction.


Developing mean visualization skills require a bit of patience, and small, data-driven projects are the way to go. Once you’ve extracted the necessary information, it’s onward to finding an optimal way to present it. There are a myriad of methods a developer can take, but the main concern is app functionality. Who’s the audience? What’s the user story? Hopefully, you’ve had enough time to think through these questions. There are times when presentation of the data matters less, especially when the end user has no need for such levels of analytics. A recipe application does not need ten pie charts examining the various hours in which the user cooked with sugar instead of salt.

But then there are occasions where beautiful data presentations are vital. Is your extracted data telling a story? Do you depend on the data to construct a bridge between the user and some complex analytics? If you need some hits of inspiration, head on over to the New York Times Interactive and see how the visualized data brings another dimension of depth and context to its articles. Examine some effective designs and ask yourself about the effectiveness of the data being presented. Could it be better, or worse?


Now that you’ve had some inspiration for visualizing data, you can dive into a wide array of tools made just for the task. Do you speak Javascript? Try heading over to D3.js and play around with the sample code until you get a feel for the various configurations. There exists a large amount of tools out there, each facilitating the creation of visual data. Find a few that you enjoy working with, and build a few toy projects with them.

Embrace the overload of the information age and learn to swim through its currents, and you’ll be well on the way to transforming ordinary stats into captivating statements.


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