Companies and websites generate a lot of data that need to be parsed and organized in a form that would provide meaningful information for interested parties. Lengthy reports on website visitors are generally not the most effective way of communicating that information, and this is where dashboards come into the picture.
Data is best viewed in the form of a visualization of graphs and charts rather than text reports. Dashboards help optimize data visualizations, especially considering they can be optimized to suit the needs of the target audience. To optimize dashboards, there are a few tips and tricks that can help get the best use out of all the data that has been gathered.
It is important to know who you are audience are and optimize dashboards accordingly. For example, analysts would want to go through data to find patterns and interesting trends, or if there are any anomalies that need looking into. Executives, on the other hand, would be more interested in getting quick, clear and reliable summaries about a campaign. When you optimize data visualizations, you need to make sure the data is neither too much or too little, based on the audience and their familiarity with the subject.
Creating a dashboard not only involves optimizing data visualizations but also needs to be free of corrupt or inaccurate records. Clean data improves efficiency as well as the decision-making process, productivity and revenue.
For marketing dashboards, it is important to identify the right metrics that will reflect the objectives of the specific organization. If the metrics are not chosen correctly, the information presented to the decision makers would result in the wrong conclusions being made. Therefore, it is necessary to figure out what the purpose of the dashboard is and who will be seeing it.
Optimizing data visualizations is an art form. While pictures certainly are worth a thousand words, it is also necessary to make sure that the dashboard is optimized such that it is not cluttered with images. Every dashboard should have between five and nine visualizations to make optimal use of the space as well as the user’s attention. A simple rule would be that the user should not have to take more than five seconds to find the information they are looking for in a dashboard. Also, all the most important metrics should “pop” from the screen.
It is also important to keep in mind the medium in which the data visualizations will be presented. If it is to be accessed on a mobile screen, then the data need to be to the point and should not have too much information. It would mainly be used for a quick reference. When it comes to desktops, the dashboards need to have detailed insights of the data, as it would mainly be used for analysis. TV dashboards are mainly used for tracking real-time data and an optimal data visualization would be bright and the numbers shown clearly.
When presenting the data, it is important to make sure that you choose the right charts. When you are comparing values across various sets, the best way would be through columns or bar charts or even a scatter plot. Instead, if you want to show the composition of something, a pie chart or a stacked column would make more sense. When you want to understand the distribution of your data, a scatter plot or a line graph or a mekko would be useful, whereas if you want to know more information about how a data set performed during a specific time, a dual-axis line or a column would prove valuable. Similarly, when you want to understand the relationship between value sets, a scatter plot or a bubble chart would be sensible.
If the above tips and tricks are implemented while creating the dashboards, users will find the data visualizations clear and useful to work with and can make decisions with more clarity than would have been possible with long and boring reports.
While color and font may not seem all that important in comparison with the data being presented, they can be used strategically to optimize dashboards. Color aids can be used to differentiate highs and lows, opportunities and concerns, etc. However, it is also necessary to restrain from using too much color or too less, as either extreme would mean the user will lose interest in the data being presented. More importantly, it is necessary to maintain consistency in terms of the message being sent with every usage of color. For example, if red was used for danger, it should not be used for any other purpose in the rest of the charts.