We are in a business environment in which around 3 trillion bytes of information are generated every day. This large amount of information has grown due, in part, to the Internet and the continuous technological increase in communications and data collection.
Such amount of data is generated and produced daily that it has been possible for organizations to analyze, discover and verify this enormous amount of information through specific technologies for it.
All this means that huge amounts of information come to us from multiple sources and what happens is that, on many occasions, there is not enough time for its correct interpretation.
At this point the field of big data visualization comes into play, the main objective of which is to show, through graphs and in a simple, measurable and understandable way, the information collected in the analysis and modeling of the same.
Although graphics have been used in business for decades, data visualization technologies have evolved according to the needs of the new business paradigm in which business analytics and big data systems proliferate.
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For this reason, we present in this post those most relevant aspects to take into account to get the most out of data visualization in our professional projects:
1. What are we going to tell. A common mistake when viewing information is trying to cover too much. We must know what we want to count and be precise in the information that we are going to show in the visualization.
2. Who is it aimed at (target audience). We must take into account what type of audience is going to work with the data to be shown in the visualization. Think about who is the user, who is going to interpret the graphs (if it is a data specialist, a marketer or a user without specialization) and based on this, decide what type of data we can show.
3. How are we going to tell it? This is where another aspect to take into account comes into play: storytelling with data, a new way of presenting and analyzing data. We recommend Cole Nussbaumer's book, "Storytelling with Data," in which he offers a practical guide to using the concepts of storytelling - telling a story with a beginning, middle and end - to communicate data visually and effectively.
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4. Simplify. Although we have already mentioned it in the first point, we must give special importance to simplification. We must be concise and only show what is relevant, discard data that is not illustrious or is "noise" so that there is a correct visualization or good understanding.
5. Use appropriate graphics according to the type of data. We must use graphic resources wisely. Not all graphics are good for everything. For example, always use column charts to make comparisons instead of pie charts, as it is visually better appreciated. To make it easier to organize graphics, using BI software is highly recommended.
6. Do not abuse tables. Tables should only be used when we need to show accurate values. Likewise, it must be taken into account that the tables are designed at an appropriate scale so that there is no confusion in the data analysis.
7. Use appropriate colors to facilitate the perception and understanding of the data. Selecting colors seems straightforward, but not all colors are effective at displaying data. It is advisable to use pastel colors or different shades of the same color instead of bright colors such as red, fuchsia, yellow and orange in the same graph. On the other hand, using color correctly can make a graph more understandable.
8. Clear and memorable statements. We must choose a good headline to contextualize and direct attention to the most important information. The use of human-readable fonts and the use of easy-to-read labels, axes, and legends can be an important factor in visualizing the data.
9. Organize the data logically. We ensure that the different graphics and screens on the display are aligned horizontally and vertically so that they can be accurately compared and not create any misleading optical illusions.
10. Cite sources from which information is extracted. Being transparent gives you greater credibility. If we have extracted information or data from other sources, we must cite them to give credibility to the information we show.