“The greatest value of a picture is when it forces us to notice what we never expected to see.”
John W. Tukey
Before data visualization, let’s take a look at how our brain perceives visuals faster, hence this will enable us to understand the importance of data visualization more clearly. Compared to the text, an average person can perceive visual information much better. This is related to the fact that 90% of the information transmitted to the brain is visual, and images are processed in the brain at 60,000 times faster than the speed of text.
Therefore, it was inevitable to use data visualization in big data analysis, which our perceptions should be much clearer and faster. Thanks to data visualization, which is developed to understand the difficult language of the data world consisting of complex numbers and letters, we are able to make stories of these data and transform them into much more easily perceivable aesthetic visuals. By this means, we can interpret the data much better and make faster decisions. We can consider this as mosaic painting, which is the art of creating portraits by combining independent images.
Our data contains crucial insights that can help us to move our business forward. However, if these insights that is effective for our business cannot be captured, the data remains as just a pile. It is not always easy to interpret these data correctly. With data visualization technologies, also known as “Interactive Visual Exploration”, we can highlight patterns and trends in data we research on, so we are able to listen to the story of our data and we can more easily notice the insights needed for our business. We can achieve this through manipulation of graphic images with graphics, colours, shapes and movements. To sum up, we are able to make the complex and confusing data easily understandable with easily perceptible graphical interfaces.
When did complex numbers and shapes begin to form a “mosaic picture”?
In fact, the use of images to facilitate understanding data has existed since the use of maps and graphics in the 17th century. However, the visualization of data evolved much more comprehensively since digital age has started. With computers and other digital tools, the data pool started to grow exponentially and this data could be processed much faster. In these conditions, data visualization tools and technologies are now necessary to embed data-driven decision-making mechanisms in institutions in order to make big data world apprehensible. It can be called as the combination of art and technology.
Data Visualization: A concept much more comprehensive than infographic.
If we perceive data visualization as it is just an infographic presentation, we make a great mistake. The term infographic emerged in the 1960s as a combination of “information graphics”. Thanks to these infographics, the numbers were presented in larger font and different shapes to attract attention. However, while infographics aim to attract viewers’ attention to these “numbers” again, data visualization is trying to tell us more than numbers. Data visualization techniques present facts as intangible information and thus allow us to make inferences. Moreover, data visualization is subjective, not objective; in other words, each user can interpret the data considering their own purpose and make different inferences.
So, in which situations is data visualization necessary?
f you have data stack that is waiting for interpretation, you can start to benefit from data visualization. You should start to use the most efficient data visualisation techniques when you need to make your data more catchy and easy to understand, to evaluate the tendencies and contradictions in your data stack, to understand the stories of your data and to consolidate a thesis and to emphasise the importance parts of your data sets.
In addition, with data visualization, you can identify issues that need attention and improvement, reveal the factors that trigger your customers’ behaviour, make your market research more comprehensive, and predict sales volumes.
Additionally and perhaps most importantly, it can show you a problem you are not aware of at all, it can help you prevent losses by taking solutions in advance and developing an action in similar scenarios.
“But I’m not a data scientist …”
With the adoption of data-driven strategies, the interpretation of these data is no longer managed by only one unit, and also existing technologies are rapidly developing for easier access and agile analysis of data. Therefore, interpreting the data correctly is now on the way to be the duty of everyone in business life. In other words, in today’s competitive environment where institutions that cannot adapt to technology are beginning to disappear, being able to read the data correctly keeps both you and your organization one step ahead.
Let’s look at the most commonly used data visualization methods:
Graph Bar: Usually used for comparison data by value. For example, performance evaluation.
Histogram: Evaluates the data over a period of time. For example, your annual sales figures can be visualized with a histogram.
Scatter Chart: You can benefit it to see the correlation between two different variables. For example, you can use the scatter chart to look at the correlation between unemployment and inflation values over a period of time. You can also make these graphics in 3D.
Relationship-Oriented Visualization: You can identify the connections and relations between different data groups much easier with this visualization method for your inquiries. This method is a very common and preferred method especially for detecting crime and fraud.
Heat Map: You can choose this method in which colours are skilfully used, especially in risk analysis.
With data visualization, you are able to monitor much more data in a very short period of time than you can look at individually, so you can identify insights and problems much faster. While data visualization provides us the easiest options for the decision-making process and problem solving, it also allows us to explore opportunities. In this case, how data visualization is important for us and our organization, isn’t it?