Data Visualisation is the most popular method to disperse information online. This is because the human brain assimilates visual information more easily than reading through the text. When it comes to presenting data to the diverse groups of people accessing the same information, web development companies in Chennai, stand by the following data visualization practices.
Distinct design for each target audience:
Data visualization helps the users to comprehend the data faster, by presenting to them exactly what they need to see, and how they need to see it. The relationship between the data could be the same, but presenting it in the same way to different audiences, will defeat the purpose of data visualization.
The designer or the technical people working on the data should decide how much data to reveal and under what context to each group. For novice audiences, the data must be displayed in the simplest form, using target-specific words for the labels and legends. A more granular view can be adopted if the audience consists of subject matter experts, for them to manipulate the data as they see fit.
Let the data presentation be interactive to facilitate exploration:
Interactive data visualization models prompt the audience to probe further, leading to increased engagement time. However, if key data is hidden behind interactions, then it can lead traffic away from your space. Use interactive elements judiciously, where they help expert audiences create new data visualization sets from the existing model.
But interactive elements can be a fun way to present data. Recently Netflix released movies based on episodes of certain series, where the audience can choose how the movie should proceed by selecting alternate scenarios. The combinations ran into the thousands, and each individual got a completely personalized visualization of the same data. This is a classic example of how data visualization needs to satisfy each target audience, while also being interactive and fun.
Use visual salience for better impact:
Making certain elements stand out from the rest of the elements on a screen or page is visual salience. Having the image or data representations pop out, draws the user’s attention to the most important sections. It can effectively highlight details and is quite useful in summarising. You can change the colours, sizes, font styles, etc, to make sure that the required visuals are distinct.
For example in a bar graph, where one of the variables represents different years, you can highlight the most recent year, or the year of interest in bold colours, while the rest of the bars are in desaturated colours. This will immediately give the viewer the important part of the information.
Making structural elements inconspicuous:
There are several structural elements in data visualization that compete with the data for attention. For example in a bar chart, there are the legends, axes, markings, etc. There must be a soft visual contrast that makes the data pop out. Of course, the structural elements give proper context to the data, but they shouldn’t overshadow the data. Keep the background clear, and preferably white with darker elements on the foreground. Make the Axes, grids, etc. a light subtle shade that still features in the background. The most recommended formats are a light grey grid of 0.5 pt weight, and axes in grey or black with a maximum weight of 1pt.
Direct labeling over legends:
Most data visualization techniques rely on Legends to specify what the data series in the chart represents. But designers from Web development companies in Chennai, are changing the rules by individually labeling the data series. Legends are not optimum for the reader, as they have to switch back and forth between the chart and the legends to make sense of the data. An interesting data visualization technique that developed from this was to represent the data in individual charts that are clearly labeled, instead of grouping them on one chart.
Create a visual hierarchy with text headings and annotations:
People often mistake the word data visualization as representing data in numbers or in the form of charts. But the text is as powerful as numbers. Text is best used as short snippets of information to represent the evolution of a certain data series over time.
Use a title that explicitly conveys to the readers what their takeaway from this visual is. The actual data can be represented as text annotations that are scattered among the data points. This creates a clear visual hierarchy and acts like a supplementary to the extended discussion from the data. This makes it easier for the reader to comprehend the data.
Overlay contextual information on the chart:
Like the previous section, where we discussed the importance of annotations, contextual information is just as important. If you are trying to drive a point across by comparing the data series to another, then you need to translate both into visual data. Seeing them side by side, or better with one overlaid on the other, gives the reader far better clarity. What would otherwise appear as distinct or random data, would make much more sense when seen together. It creates the necessary impact that the differential data points between the two were intended to make. You can supplement this graphic overlay with text annotations.
Responsive Data Visualisation:
Static data visualizations do not sit well on a mobile screen. If your data representation is in Jpeg or Png formats, then the image which looks awesome on the computer would be close to illegible on a phone screen. With most of the consumers choosing to use their phones or other mobile devices to access the internet, your primary focus should be how the data is presented on these screens. Web development companies in Chennai suggest the use of Javascript visualization libraries like D3.js or Highcharts that give responsive output, or you can go the way of creating different variants of the same data display for different devices.
Keep it simple:
This last piece of advice cannot be stressed enough. Many web developers, designers, and academics think that complexity shows the depth of their expertise. But recent trends stress simplicity that gets the information across to all kinds of viewers. The visualization should provoke interest even in the novices. Find the delicate balance between complexity and clarity that would make the data visualization align with the context that your audience is expecting.
Data visualization is indeed complex, but when it comes to choosing the right people to do the job, there are none better than the ones at Open Designs. They are a full-service web development agency in Chennai, with experience of 20+ years. Their designers are adept at data visualization techniques that are personalized based on audience analysis. Reach them at www.opendesignsin.com.
