Ever-growing volume of data and its importance for business make data visualization an essential part of many companies’ business strategies.
In this article, we provide a profound view on data visualization techniques and instruments, the factors that influence the choice of visualizations and a concise review of the most widely-used data visualization tools used in business today.
In this article:
- Factors that determine data visualization choices
- Major data visualization techniques and their application
- Tools for data visualization, exploring and analytics
What determines data visualization choices
Visualization is the first step to make sense of data. To transcript and present data and data correlations in a simple way, data analysts use a wide range of techniques — charts, diagrams, maps, etc. Choosing the right technique and its setup is often the true way to make data understandable. And vice versa, wrong tactics may fail to present the full potential of data or even make it irrelevant.
5 factors that influence data visualization choices
- Audience. It’s important to adjust data representation to the target audience. If it’s end customers who browse through their progress in a fitness app, then simplicity is the key. On the other hand, if data insights are intended for researchers or experienced decision-makers, you can and often should go beyond simple charts.
- Content. The type of data determines the tactics. For example, if it’s metrics that changes over time, you most probably will use line charts to show the dynamics. To show the relationship between two elements you will use a scatter plot. In turn, bar charts are perfect for comparison analysis.
- Context. You may use different approaches to the way your graphs look and therefore read depending on the context. To emphasize a certain figure, for example serious profit growth compared to other years, you may want to use the shades of one color and pick the bright one for the most significant element on the chart. On the contrary, to differentiate elements, you’ll use contrast colors.
- Dynamics. There are various types of data, and each of it implies a different rate of change. For example, financial results can be measured monthly or yearly, while time series and tracking data is constantly changing. Depending on the type of change, you may consider dynamic representation (steaming) or a static visualization.
- Purpose. The goal of data visualization also has serious influence on the way it is implemented. In order to make a complex analysis of a system or combine different types of data for a more profound view, visualizations are compiled into dashboards with controls and filters. However, dashboards are not necessary to show a single or occasional data insight.
Data visualization techniques
Depending on these 5 factors, you choose among different data visualization techniques and configure their features. Here are the common tactics used in business today:
Charts
The easiest way to show the development of one or several data sets is a chart. Charts vary from bar and line charts that show relationship between elements over time to pie charts that demonstrate the components or proportions between the elements of one whole.
Plots
Plots allow to distribute two or more data sets over a 2D or even 3D space to show the relationship between these sets and the parameters on the plot. Plots also vary: scatter and bubble plots are the most traditional. Though when it comes to big data, analysts use box plots that enable to visualize the relationship between large volumes of different data.
Maps
Maps are widely-used in different industries. They allow to position elements on relevant objects and areas — geographical maps, building plans, website layouts, etc. Among the most popular map visualizations are heat maps, dot distribution maps, cartograms.
Diagrams and matrices
Diagrams are usually used to demonstrate complex data relationships and links and include various types of data on one visualization. They can be hierarchical, multidimensional, tree-like.
Matrix is a big data visualization technique that allows to reflect the correlations between multiple constantly updating (steaming) data sets.
Data visualization tools
Together with the demand for data visualization and analysis, the tools and solutions in this area develop fast and extensively. Novel 3D visualizations, immersive experiences and shared VR offices are getting common alongside traditional web and desktop interfaces. Here are three categories of data visualization tools for different types of users and purposes.
Data visualization tools for everyone
Tableau is one of the leaders in this field. Both newbies and professional analytics companies like Statista rely on this platform to create stories and derive meaning from their data.
Apart from a user-friendly interface and rich library of interactive visualizations, Tableau stands out with its powerful capabilities. The platform provides large integration options including My SQL, Teradata, Hadoop and Amazon Web Services. Therefore, this is a good tool for both occasional data visualizations and professional data analytics. The system can easily handle any type of data, including streaming big data and machine learning insights, and allows to combine visualizations into smart dashboards.
Also in this category:
Among other popular data visualization tools in this category are easy-to-learn Visme, Fusioncharts with varied integration capabilities, free and open source Datawrapper and ZingChart for JavaScript and HTML5 charts.
Data visualization tools for coders
This category of tools includes more sophisticated platforms that enable not only data visualization, but also a certain level of data analytics.
Plotly is one of the most popular ones in this category. It’s more complex than Tableau, however, comes with analytics perks. This visualization tool allows to create charts using R or Python, build custom data analytics web apps with Python, and even use and collaborate in rich open-source libraries for R, Python and JavaScript.
Also in this category:
Sisense is another data visualization tool with full-stack analytics capabilities. This cloud-based platform has drag-and-drop interface, can handle multiple data sources and supports natural language queries. Not good for rookies.
IBM Watson Analytics is known for its NLP capabilities. The platform literally enables conversational data control alongside strong dashboard building and data reporting tools. However, IBM Watson Analytics is not cheap and works best for serious data visualization and analytics tasks.
Tools for complex data visualization and analytics
Growing importance of connected technology, available data sources and fast-changing environment make companies deal with different types of multi-source data and search for more complex visualization and analytics solutions. This category includes 3 of them: Microsoft Azure Power BI, ELK stack Kibana and Grafana.
Power BI is exceptional for its highly intuitive drag and drop interface, short learning curve and large integration capabilities, including Salesforce and MailChimp. Not to mention $9.99 per month for a Pro version.
Nevertheless, thanks to Azure services, Power BI today is one of the most powerful data visualization and analytics tools that can handle literally any amount and any type of data.
First of all, the platform allows to create customized reports from different data sources and get insights at a couple of clicks. Secondly, Power BI is strong enough to process streaming real-time data and therefore easily handles big data analytics. Finally, it’s not only fully compatible with Azure and other Microsoft services, but also can directly connect to existing apps and drive analytics to custom systems.
Watch the introduction here.
Kibana is the part of the Elastic Stack that turns data into visual insights. It’s built on and designed to work on Elasticsearch data only. This exclusivity, however, does not impede Kibana from being the best data visualization tool for log data.
The tool enables all sorts of data visualization techniques — interactive charts, maps, histograms, etc. Moreover, Kibana goes beyond traditional dashboards for data visualization and analytics.
The system allows to build advanced analytics: combine visualizations from multiple sources to explore unique correlations between different insights and turn on machine learning features to reveal hidden relationships between data events. Not to mention a set of tools and APIs for developers.
The last but not least in the list is Grafana — a professional data visualization and analytic tool that support up to 30 data sources, including AWS, Elasticsearch and Prometheus.
Even though Grafana is more flexible in terms of integrations compared to Kibana, each of the systems works best with its own type of data. In case of Grafana, it’s metrics. It makes this tool popular among IoT data visualization solutions.
Grafana allows to visualize and compile into complex dynamic dashboards different types of metrics data. It has a wide variation of admin roles which makes it perfect for complex monitoring and control systems.
Additionally, it enables alerts and notifications based on the predefined rules. And finally, Grafana provides a set of perks for fast data analytics, such as creating custom filters and making annotations — adding metadata to certain events on a dashboard.
These are just the major data visualization tools and techniques. None of the mentioned platforms or services is static — all of them are developing very fast and introduce new features and capabilities to keep up with the market needs. Whereas the increasing volume of IoT and connected technology and growing interest in data and its potential are fueling this dynamics.
