As the amount of data available for comprehension increased exponentially, the need to represent the same in a coherent, concise and simple manner popped up as well. This resulted in the emerging field of data representation and this has a dynamic effect on our society. The evolution of data representation from a something as simple as a graph to interactive applications and advanced 3D representations has completely changed the face of the data analysis. The advances made in the field may be immense but there still remain a few stumbling blocks that has to be overcome in order to maximize its potential.
Paradoxically, it is the advancements made in the made in the field of computers and animations that are proving to be a hurdle now. Virtual reality, for example, has the potential to augment the existing ways of data visualization and elevate it to a superior level. This has been put to test by Goodyear tyres and used the interpretation to enhance the performance of their F1 tyres. But VR has been associated with the entertainment sector for so long that efforts to incorporate the same technology for data analysis in say, a Fortune 500 company has met with ridicule. Research to make the VR headsets compact are ongoing but it might take a few years to come to fruition.
Augmented reality is another technology making waves in the market currently, not least because of the popularity of Pokemon GO. Among all the new technologies, AR has the best and immediate chance of improving on the existing data representations. The challenge though lies not so much in its implementation as its augmentation. The overlaying data used to augment the representation should be clear, concise, and non-befuddling and should serve the purpose of augmenting, not distracting.
The other challenges of data representation are contingent on the users and developers. The majority of data representations are still done in 2D and as such, banal. Hence, to make the data representation distinct and interesting, the developers would have to resort to innovative ways of data representations. This could include vivid colors, interactive applications and collection and representation of more interesting data. As such, there is an increased demand for technical expertise and a channeled scientific approach in processing data representations. There is a dearth of data scientists currently, something that a lot of universities are trying to combat by offering new courses pertaining to data analysis. The differing levels of comprehension among the group the data representations target is another challenge presently faced by data representation. This particular problem is much more difficult to tackle as there is no individual solution to this. The best way to tackle this would be to form a protocol for interpreting data representations.
Even though there are challenges facing data representation these days, they pale in comparison to the progress made in the field in the past decade. Judging by the pace with which the field is evolving, it wouldn’t be surprising if the challenges listed above do not exist anymore by the next few years.