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The 5 Biggest Challenges Facing Data Visualization

Data visualization has some hurdles to get over

19Oct

Data visualization has changed our society considerably. From the most simple projected line across a football field through to complex graphs outlining market fluctuations, they are changing the way that our society is approaching and understanding data.

However, despite the huge impact visualizations have had, they still face considerable challenges in the future. We take a look at the 5 most pressing.

VR

Virtual reality is going to have a huge impact on the potential for data visualizations, allowing people to interact with data in the third dimension for the first time. Imagine being able to pick a data set and move it around on any axis to compare it to another, it isn’t too far away. According to SAS we can process only 1 kilobit of information per second on a flat screen, which can be increased significantly if it’s analyzed in a 3D VR world. In fact, we have already seen Goodyear collaborate with Dr Robert Maples to use VR data visualization to improve their Formula 1 tyre performance.

However, the challenge with this comes with trying to get it in the hands of businesses who would benefit from the technology. Virtual reality is something that is currently seen as predominantly for entertainment so trying to get a senior leader in a fortune 500 company to wear one to look at sales data would certainly be a struggle. At present there are some moves to try and make VR headsets more compact, but this is going to take several years and data visualization needs to stay front and centre until then.

AR

Augmented reality may well be the single biggest change that we are going to see regarding the use of data visualizations. To some extent we have seen some of it already, with HUDs like the now defunct Google Glass, overlaying data onto what you can see in front of you. Bizarrely, one of the key reasons for the sudden concentration on AR is the huge success of Pokemon Go, which not only showed the capabilities of AR, but also introduced it to a wide and diverse audience.

The challenge that data visualization is going to have is that those creating them need to make sure they are doing so in an understandable and non-obtrusive way. It creates a new dynamic, where the data overlaid needs to be clear, concise and not distracting. It’s a fine line to balance on and a real challenge for those who are used to creating traditional visualizations.

Development

VR and AR are likely to be interesting technologies in the future, but for the time being, we are still going to be consuming the majority of our data through traditional 2D screens. As the number of data visualizations increases in almost every area, the chances of yours standing out decreases too as you’re trying to get to the top of a larger and larger pile.

It means that whilst these other technologies are developing, people working in data visualization need to try and find a way of making their visualizations stand out from the crowd, without making it overly complex. This could mean more vivid colors, increased interactivity or simply using the most interesting data, but finding the correct way is certainly a hurdle to overcome in the next few years.

Differing Levels Of Understanding

As data has spread throughout society one of the elements that has become evident is that there is a huge variation in the levels of understanding. This could even be in a high powered business setting, where people who are used to seeing basic excel graphs do not understand anything more complex. The idea of interactivity within visualized data is not something they would ever feel necessary. However, there are others who would benefit from more complex visualizations, where they can see as much as possible in as smaller space as they can, through interactive design or just more complex features.

It is, therefore, difficult for those designing visualizations to match up to the wide-ranging understanding of data and data visualizations. It could be that multiple visualizations are created for different levels of data literacy, but that’s simply wasting resources and is hardly a practical solution.

Technical Skills

As we move toward more interactive and complex trends for data visualizations, we are going to be seeing an increased need for technical skills to first understand and translate the data then create visualizations around the results.

We already have a shortage of data scientists and people who can feed the right data to the right people, so this is going to be a key challenge for the creation of decent data visualizations that can pinpoint important data. Although this is likely to increase in the future with an increasing number of universities offering data science courses, this is unlikely to see data scientists becoming prevalent for several years.

As discussed previously with VR and AR, working on new technologies is not easy, especially for those with little experience of similar areas. At the same time, these technologies are not developing for data visualization alone, with those with the training and qualifications having the option of working on other popular mediums, like gaming or movies, which may also have higher salaries given the focus of the technologies on these markets. Therefore, trying to find somebody with the technical expertise who hasn’t already joined these other industries is going to be a huge hurdle to overcome. 

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