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A number of industries are now dependent on data science for insights into various aspects of their businesses and services. The data that they churn out becomes the basis for a lot of business decisions. This makes the presentation of the data gathered and interpreted a very important task.
Data visualization is simply, the art of presenting the data derived and analyzed, using visual elements. It is necessary that the data visualization captures the audience and delivers the appropriate inferences. Good data visualization can go a long way in determining the capability of a data scientist.
Here are five things you should consider when you prepare to visualize your data:
1. Know your audience:
Understanding the end users is the first step in data visualization. The data presented to the audience should set them into action and not just stop at their comprehension. So, one must consider the target audience that they are catering to and determine what action needs to be taken for each of the data presented and how to achieve it. Creating a wireframe that will elucidate the actionable information will help visualize the data best.
Thus, data visualization should be carried out in a way that it engages the end users and enables them to act upon strategies that can be derived from the information provided to them.
2. Organize the data to set context:
One of the key elements for good data visualization is its ability to set context; what is the data measuring, interpreting or analyzing needs to be made clear in the way it is presented. For instance, if the given data is a measure of performance for a team, then the visualization must clearly represent the parameters such as number of sales made, revenue generated etc. It should also include data that can help derive actionable insights such as where more effort needs to be put, etc. to enable a way forward.
It is necessary to organize the data in a way that all of the audience’s questions are answered and their attention is drawn to a call for action.
3. Choose the visual approach that best represents your purpose:
There are a multitude of visual representation options that one could choose from – pie charts, graphs, infographics, etc. It is vital that the choice made for the data you have, suits it best. For example choosing a pie chart to represent comparative data might not be the best choice; it might confuse people and lead to a misinterpretation of the data.
Therefore, it is indispensable that the visual representation approach chosen should communicate the data accurately and effectively to the end user.
4. Keep the dashboard simple and categorized:
The dashboard is a consolidation of all the important data from the complete visualization in one place. Dashboards are designed for information to be monitored on a single page/ box.
Organizing the dashboard in accordance to the type of data available makes it easier to set a structure for the complete visualization. Classifying the data makes for easier access and can help determine, which data becomes actionable content and, which data is supportive content.
Simple and categorized dashboards make for precise and intelligible data depiction.
5. Consider the aesthetics:
It is irrefutable that data visualization is an art and like with any art form, it is important to consider the aesthetic values for this as well. Choosing a colour pattern that does not comply with the data or visual approach can set the entire data visualization off and render it ineffective.
Consistency is the key to aesthetically pleasing data visualization; make sure to keep the fonts, type size, colours and designs consistent throughout.
It is always important to remember that the aesthetics of your visualization will catch your users’ attention first!
With data insights impacting businesses with every passing day, effective data visualization can go a long way in building the credibility of your data science practices. Data that is depicted rightly will drive its audience towards an action that is called for. The best data is the data that is easily comprehensible and the best data visualizations are the ones that can convey them.
What are some other data visualization practices that you know of? Tell us via comments!