(Big Data)What is 2017 going to bring for data visualization?

Swedish academic and medical doctor Hans Rosling’s death earlier this year at the age of 68 was a tragic loss for the data community. He inspired many into careers within data visualization by seeking to dispel the myth that data and information are inherently boring, once saying that, ’Having the data is not enough, I have to show it in ways people both enjoy and understand.’

It is partly his contribution that has seen data visualization recognized as an important feature within business. A recent Business2community survey of data professionals found that the data science skill with the highest correlation to project success was data mining and visualization tools. The discovery of novelty is a wonderful thing, but is entirely pointless to a business if decision makers can’t be convinced of its existence and take appropriate action. Data visualization is the best way of doing this, engaging decision makers with a visual narrative that leads them to the insight. It also shows the quality of the data, revealing if your dataset is incomplete by easily displaying where data is missing on the report, and whether it’s valid - with a quick, preliminary visualization on collected data showing trends that indicate problems in the complete data.

Data visualization is constantly evolving, with the technology improving dramatically over the last decade. This year, we are set to see further advancements, with an extremely range of exciting new directions. We asked a number of experts what they thought would be the most significant trends in the field.

Virtual Reality
According to research firm IDC, the market for augmented and virtual reality is expected to grow from $5.2 billion in 2016 to $162 billion in 2020. This is being driven by tech companies like Facebook-owned Oculus, who are making great leaps in the consumer market. Immersive data visualization offers easier pattern recognition in big data sets and more intuitive data understanding. Unlike conventional data visualization models that rely on two axes in a spreadsheet, VR allows users to walk around and look at the data from multiple angles, comparing any number of different factors at the same time. VR works because it focuses your entire field of vision, allowing you to concentrate exclusively on the objective. In early experiments, researchers found that users who interacted with the data through VR reported better retention of perceived relationships within the data than when viewing it through two dimensional data visualization tools.

The possibilities offered by VR are huge, and nowhere is it more important to businesses than data visualization.1 The naturalistic way it allows people to interact with virtual objects, the ability it affords individuals to experience the data through all senses - touch, sound, and even smell - mean that there are many opportunities yet to be explored, and data visualization companies should start preparing.

Abon Chaudhuri, Sr. Applied Researcher, at Walmart Labs:

‘Recent progress in human-computer interaction (HCI) (with the support of computer vision and virtual reality) indicates that it is time to transform the way a user experiences a visualization. Multi-touch gestures, touchless interactions etc. are almost commonplace now. We are probably not far from a time when a person can actually walk into the projection of a 3D visualization (a holograph) and modify it.’

Doug Ireland, VP of Finance at Prezi:

‘In the longer term, immersive experiences like AR and VR. Data visualization is extremely relevant to immersive visual experiences. It makes no sense to communicate anything but the simplest data points in text or verbally in a virtual, richly visual environment.’

Mobile-first Visualisations
The recent Intel’s IT Manager Survey found that IT managers expect 63% of all analytics to be done in real time, and those who fail to understand and act on data immediately risk losing their competitive advantage to someone who will. Data visualization for mobile is subsequently becoming increasingly important, with many data visualization vendors now either adapting desktop experiences to mobile formats, or taking a mobile-first approach to developing their technology.

There have been a number of challenges in the past, with smartphone display space limited. Information needs to be presented more simply than on a desktop, so, rather than just translating a complex desktop view into a simpler mobile one, it is important to consider context. People also have big fingers that can’t necessarily navigate to every facet of a touchscreen, there are also many distractions. Designers are having to exploit gesture-based input as a result in order to help users easily navigate different views and interact with the data.

Tableau, for one, has now launched support for device layouts with Tableau 10, and it is a field that is slowly being perfected as designers establish the difference from building visuals for desktop.

Ken Cherven, Data Visualizations Specialist at GM:

I believe the continued ability to capture information at close to real-time speed from a variety of sources will play a major role. In my experience, most of data visualization today is not functioning this way but is used more in the realm of taking traditional business metrics and making them more visually appealing. I think the future holds real opportunities for data visualization to capture many of the flows that occur within the daily life of a business, regardless of whether they take place on the web, via a call center, or through mobile devices, or even in the way a business processes transacts beyond the customer purview.

Artificial Intelligence
There are very few areas that AI is not going to impact, and data visualization is certainly going to be among the most important. The problem is that Big data is outgrowing the basic dashboards and human data scientists that did data visualization in the past. There is simply too much data and too many insights held within it waiting to be revealed. Machine learning and advanced statistical algorithms are now necessary to keep churning them out them, and automated data visualizations to represent the findings. Natural Language Generation (NLG) is essentially just a variation on data visualization, which can either work alongside or independently of visuals to make it easier to interpret the information. For example, in data intensive industries like finance, workers may have forty different graphs spread across a number of monitors. While these will likely have all the information necessary, it takes considerable human effort and time to unlock the insights they hold. NLG will compare the graphs and provide the worker with advice, explaining the analysis without the need of a highly skilled and expensive data expert.

Michael White, an associate professor at Ohio State University, believes that this means NLG is finally on the precipice of entering the mainstream, arguing that, ‘There’s growing awareness that masses of data and visualizations are not really helpful if they can’t be explained and made relevant. I’d say the time has finally become ripe for natural language generation to have commercial success.’

Gabi Steele, Data Visualization Specialist at the Washington Post:

I think AI (artificial intelligence) systems, machine learning, and emerging technology of this nature are likely to be most impactful to the field of data visualization given the immense dependence and correlation to visually comprehending mass amounts of data that these technologies can quickly process and work with.

Abon Chaudhuri, Sr. Applied Researcher, at Walmart Labs:

We must not forget the role AI can play in transforming this field. Wouldn't it be awesome if I only had to give the data and my question(s) to a program and let it choose the most effective visualization for my goal? Early work along this line is being published already.