Kibana dashboards: Comparison of two cars

Real-time data visualization: comparing two cars

With this post, we reach the end of a series of 4 posts, where we have addressed the importance of studying real-time data through a practical example: analyzing two cars in motion. In the first post, we looked at the benefits that real-time visualizations can bring to a company. After that, in the second post, we went on to describe the ELK stack, the tool that would allow us to receive, process, and display the data. Next, in the third post, we saw the potential of Kibana, a tool included within the ELK stack, as a business intelligence tool. And finally, we arrive at this last post, where we will review the results obtained.

Chart generation

In the previous post, we generated several charts for the data of interest to monitor. Now it is time to group them into dashboards.

The first step is clear: once we are on Kibana’s home screen, we go to the Dashboard tab, where we will find existing dashboards and where we can create new ones.

Once we have created our dashboard, we can include the charts we consider appropriate and arrange them as we wish. In our case, we have included 9 visualizations.

In the previous dashboard, we are displaying the average speed, the average engine RPM, the average airflow range, the average intake manifold pressure, the average control module voltage, the average barometric pressure, the average coolant temperature, the average catalyst temperature, and the average ambient temperature captured by the sensors of both cars between 11:40 and 11:55 on February 1, 2018.

Data correlation

It is true that these data are purely informative; however, if we recall the use of timelion, explained in the third post, we can include charts with correlated data to view related metrics and assess whether there are relevant situations. In our dashboard, we can see the comparison between the two cars.

In this dashboard, we have added 8 charts. In them, we can observe: the engine RPM of both cars, the absolute intake manifold pressure of both cars, the barometric pressure of both cars, the ambient temperature captured by both cars, the catalyst temperature of both cars, the coolant temperature of both cars, the speed comparison, and the maximum RPM of each car during the period studied.

With the above representation in a dashboard, business experts will be able to analyze the data in depth, draw conclusions, and improve decision-making.

Finally, at LISDATA we would like to thank everyone who has followed us throughout this series of posts. If you believe we can help you make your company data-driven, please do not hesitate to contact us.