Best Practices for Using Kibana for Data Visualization
From business meetings to public announcements, big data needs to cater to employees from various backgrounds. Here are the best Kibana practices.
Join the DZone community and get the full member experience.Join For Free
We're at a point in time where we can extract detailed and accurate data from the large majority of applications, software and servers. That also means often that the data is too complicated to understand in its raw form. It proves to be a big issue since now data isn't only of use to experts, but also to the average person who needs to understand what it entails.
From business meetings to public announcements, big data needs to cater to employees from a variety of technical backgrounds. there's no better solution than representing the dense tables of numbers and statistics visually. However, bad data visualization is a thing, even if you're using specialized software to automate the majority of the process. There are several factors you need to take into consideration when visualizing data.
But first things first.
What Is Data Visualization?
Data visualization is the process of representing large sets of complex data using images and graphics. The point of visualization is to break down dense data into its core concepts and findings, ensuring viewers get as much information as possible with little time and effort.
There are many ways you can present data, from line graphs and scatter plots, to maps, pie charts, and bar charts. Choosing the right graphics depends on the type of data, context & the initial source of the data. Text-based data, on the other hand, can be a challenge to visualize similarly to the other types because it’s not represented by numbers.
Why Use Data Visualization?
Studies show that our brains process images 60,000 times faster than text, while 80 per cent of people are more likely to remember what they see, over what they hear or read. When data is easy to understand, the viewer is less likely to be misled with false conclusions.
What Is Kibana?
For sleek and flexible graphics, it's better to use a specialized tool rather than creating them from scratch. That's where Kibana comes in.
Kibana is a free, open-source analytics, monitoring, and visualization platform that was created in 2013 by Elastic. It offers a variety of features, that makes it a noteworthy competitor to other visualization tools, such as:
- Graphics Analytics.
- Machine Learning.
- Anomaly Detection.
- Log Categorization.
- Log Monitoring.
- Flexible Data Visualization.
- Data Exploration.
- Cloud Integration.
Kibana's intuitive and straightforward user interface (UI) consists of four sections: discover, visualize, dashboard, and settings. Having most features on display makes it one of the easiest tools to use and master, even for complete beginners.
Even though Kibana is an open source tool, it can become hard to scale upgrading & maintaining the platform for ongoing use with larger businesses, this is where a platform that offers hosted Kibana (like Logit.io), can become useful for mitigating ongoing costs associated with a growing infrastructure.
The Best Practices for Using Kibana for Data Visualization
Just because you have access to a specialized tool, doesn't mean your resulting graphics are going to be great without putting in the work. There are a few factors you need to take into consideration and practices to follow in order to create note-worthy visuals.
Know Your Audience
Before you even start planning your graphics, you need to determine your audience. Your reporting dashboards are going to contain very different information depending on if your viewer is a systems administrator, a key company stakeholder or a business analyst.
Take into account how knowledgeable your audience is on the topic you're sharing, along with their age group and attention span. This will help you determine the complexity of the data displayed and which key information to focus on.
Aggregate the Data
It's impossible to represent massive amounts of data in a single graph. Instead, you need to profile and section it into categories that work well together. Kibana offers two types of data aggregation; bucketing and metrics.
Data bucketing — also known as binning — is the process of sectioning-off sets of data that meet specific criteria defined by the user. It helps reduce minor observation errors and anomalies without distorting the data's integrity.
Metrics, as the name suggests, keep track of your system or infrastructure's metrics in real-time. When visualizing metric data, it helps with troubleshooting system problems.
Choose the Right Visuals
Choosing the right graphics depends largely on the type of data in question. Different charts focus on different elements and results, making this decision tie back to knowing your audience and what information you want to be at the centre of attention.
For example, to showcase the composition of a data set, use pie charts and stacked charts. Trends work best with line graphs, while bubble charts compare two or more sets of data. Of course, different charts can be used on the same sets of data to highlight different findings.
Ambiguous graphs can be misleading, whether intentionally or not. To avoid that, make sure you use clear language and label your graphs when in doubt. Deceptive graphs are rarely wrong, but they take advantage of people's lack of attention to distort findings.
Some examples of misleading graphs include:
- Changing the baseline of the vertical axis to anything other than zero.
- Manipulating the Y-axis to exaggerate or undermine findings.
- Using the wrong figure — using pie charts to compare separate data sets.
- Flipping the norm — switching dark and light colours for density, and left and right for positive and negative values.
The Need for Visualized Data
By allowing engineers to find the information they need promptly, visualized data contributes to monetary gain in commercial businesses and increases overall staff productivity. It also speeds up business meetings, shortening them by an average of 24 per cent, which is a game-changer, especially with curtail decisions’ deadlines growing narrower. Shorter meetings and a smoother understanding of complex data can do wonders to a company’s growth. The gain is most noticeable with companies that take full advantage of their big data, as they’re twice as likely to be top-performing financially in their industry.
Data visualisation can also be useful as an HR tool as this can reveal key metrics related to employee engagement for easily.
Lately, people have become more interested in the ins and outs of the companies they work with and buy from. Misinformation or the lack of it can greatly affect how the public views a company. Easy-to-understand visualized data offers a level of transparency and public accountability, which increases the trust between companies, and their clients and key stakeholder.
Opinions expressed by DZone contributors are their own.