With the advent of increasingly larger sources of data it is becoming even more difficult to view or imagine patterns within these data sources. This has become very important in areas such as science; the Genome project for example identified 2,000,000 Genes in the Human Genome, imagine looking at that as a series of numbers.


In 2013 Greg McInerny, Senior Research Fellow in Information Visualization for the Biological Sciences at Oxford University attempted to do some research on how visualization is used by scientists. There is an excellent blog on this published by @FutureEarth. Scientists are inherently skeptical of visualizations. Moritz Stefaner referred to it as “Dumb Blonde Syndrome” the idea that if something looks good, it is suspect. But even skeptical scientists are coming round to the idea that visualizations have their place in detecting patterns and outliers within massive amounts of data.





The visualization above shows the structure of a molecule. This would be impossible to view with the naked eye and can only be viewed by rendering a visualization utilising huge amounts of data. But its not just a case of taking huge amounts of data and creating a pretty picture, the following example proves the point.






It is impossible to view all of the slices and don’t even start to work out the percentages.

A good visualization becomes even more important when the stakes are really high. In the pharmaceutical industry it takes on average 12 years to take a drug from discovery to market and the process can cost around $4 billion. Only 10%-20% of new drugs make it to market and at any point the process can fail either due to adverse patient reactions or the drug just not being as effective as first thought.

You can imagine anything that can increase the likelihood of a drug getting to market is embraced. Data visualization can allow Researchers and Data Scientist’s to explore hugely complicated data sets and also then relate discoveries to non-technical audiences such as investors and regulators by using story telling.

What this says is that although we concentrate on specific subjects such as, Visualizations, collaboration, and storytelling none of these can work in isolation. The scientist will not trust the visualization without data and you can’t rely on data on its own without collaborating with your peers. So what you need is a harmonic join between the three factors.

Please don’t think I am trying to simplify things there are obviously many more pieces involved in this complex puzzle. But as the heat is turned up in the visualization arena and battle is joined between the main players, we will see the creativity of many a web developer let loose on even more and more fantastic visual delights. But embrace the scientist in you and look for substance in that style.