As we work with volumes and data varieties increasing, the old saying “a picture is worth a thousand words” becomes more real. A simple example is to use the subway of a big city like London. The tube, as it is called, has 11 lines and 270 stations. In theory, the basic information is a list of stations in alphabetical order and names of the lines. But try this … Compare the alphabetical list with the map. It is crystal clear that trying to go from one station to another in hands only an alphabetical list is extremely exhausting. In own Wikipedia link appears the tube map. It is clear how to get from one station to another, that line pick up and even know how many intermediate stations exist and will need to change line, in a conspicuous and easy way. A picture is worth a thousand words well.

This example shows how important is data visualization. In a spreadsheet, it is difficult to identify trends or do correlations between data. But with graphic images our perception of numbers changes. We can identify very quickly which product sells more and which shop is more profitable.

The concept of big data increases the potential of the analyzes. Let’s take the example above the shops. Analyzing only internal data, we can identify which stores are more profitable and which products sell more within our company. We can take corrective action in case of deviations and negative trends. But what about when we see a drop in sales in a given region? No external data, as the economic situation in the region, our actions will have little effect. If we know the economic crisis, we can, for example, change the product, mix to lower unit prices. Therefore, the concept of big data, despite the big word draw attention, not just volume. A huge amount of data, but without references to help me identify the real causes of the decline in sales is not very useful. Variety, that is, access to other data sources (usually external) allows me to have a broader view of the context. Here simplistically, Big data is internal + external environment context.

Great, we took an important step. But without a good visualization tool, this mountain of data will not help us much …

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With view of modern techniques, we are able to identify patterns or data correlations which were previously invisible. Asking the right questions, we can identify things that are happening, or will happen, if correctly identify trends. Not able to looking at a spreadsheet… By the way, David cites a phrase that is worth repeating here: “visualization is a form of knowledge compression”. A single image can compress a huge volume of data in a color graphics. Therefore, only with views, you can understand immense and varied amounts of data.

After all, the vast majority of the information we gather as human beings is visual. Research shows that the human retina can transmit 10 megabits per second. Second comes the hearing, with 1/10 of this capacity. It is natural that we can explore the potential of visualization techniques to analyze and correlate data more efficiently.

An example of the potential for exploitation of databases is Google’s project, “Public Data Explorer“. Using concepts of big data and dynamic views can break these ideas and make much more correct and accurate business decisions. In fact, as we have more data, the more important the ability to view them, distilling this immense and varied volume into useful information.

Visualization techniques are now extremely important for the generation of value of the concept of big data. After all, big data is not only the concept on data, but how we can extract insights and their intelligence. And visualization is the master key to it.