Data Visualization Techniques
Data Visualization Techniques and Tools
The ever-growing volume of data and its importance for business make data visualization an essential part of business strategy for many companies.
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 methods.
- Major data visualization techniques and their application.
- Tools for data visualization, exploring and analytics.
- Tips to create efficient and responsive data dashboards.
What determines data visualization choices
Visualization is the first step to make sense of data. To translate 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 only way to make data understandable. Vice versa, poorly selected tactics won't let to unlock 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 specific target audience. For example, fitness mobile app users who browse through their progress can easily work with uncomplicated visualizations. On the other hand, if data insights are intended for researchers and experienced decision-makers who regularly work with data, you can and often have to go beyond simple charts.
- Content. The type of data you are dealing with will determine the tactics. For example, if it’s time-series metrics, you will use line charts to show the dynamics in many cases. To show the relationship between two elements, scatter plots are often used. In turn, bar charts work well for comparative analysis.
- Context. You can use different data visualization approaches and read data depending on the context. To emphasize a certain figure, for example, significant profit growth, you can use the shades of one color on the chart and highlight the highest value with the brightest one. On the contrary, to differentiate elements, you can use contrast colors.
- Dynamics. There are various types of data, and each type has a different rate of change. For example, financial results can be measured monthly or yearly, while time series and tracking data are changing constantly. Depending on the rate of change, you may consider dynamic representation (steaming) or static visualization techniques in data mining.
- Purpose. The goal of data visualization affects the way it is implemented. In order to make a complex analysis, visualizations are compiled into dynamic and controllable dashboards that work as visual data analysis techniques and tools. However, dashboards are not necessary to show a single or occasional data insight.
Are you looking for a skillful team to create effective and responsive data visualization and dashboards to deliver important insights for your business? Contact our team and tell us about your needs and requirements. Our analysts, developers and data scientists have profound experience in working with different types of data and will find a way to help you get the most of your data assets.
Data visualization techniques
Depending on these factors, you can choose different data visualization techniques and configure their features. Here are the common types of visualization techniques:
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 the relationship between elements over time to pie charts that demonstrate the components or proportions between the elements of one whole.
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 some of the most widely-used visualizations. When it comes to big data, analysts often use more complex box plots that help visualize the relationship between large volumes of data.
Maps are popular ways to visualize data used in different industries. They allow to locate 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 one of the advanced data visualization techniques that help determine the correlation between multiple constantly updating (steaming) data sets.
Image credit: duke.edu
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. Newbies and professional analytics companies like Statista rely on this platform to derive meaning from data and use insights for effective storytelling.
Image credit: Statista
Apart from a user-friendly interface and a rich library of interactive visualizations, Tableau stands out for its powerful capabilities. The platform provides large integration options including My SQL, Teradata, Hadoop and Amazon Web Services. This is a great tool for both occasional data visualizations and professional data analytics. The system can easily handle any type of data, including streaming performance data and machine learning insights, and allows to combine visualizations into dashboards.
Also in this category:
Among other popular data visualization tools in this category are easy-to-learn
Data visualization tools for coders
This category of tools includes more sophisticated platforms that allow to not only visualize data but also add data analytics features to applications.
Also in this category:
Sisense is another data visualization tool with full-stack analytics capabilities. This cloud-based platform has a drag-and-drop interface, can handle multiple data sources and supports natural language queries. It can be a bit complicated for rookies though.
IBM Watson Analytics is known for its NLP capabilities. The platform literally supports conversational data control alongside strong dashboard building and data reporting tools. However, IBM Watson Analytics is not cheap and works best for large-scale data visualization and analytics tasks.
Tools for complex data visualization and analytics
The growing adoption of connected technology places a lot of opportunities before the companies and organizations. To deal with large volumes of multi-source often unstructured data, businesses search for more complex visualization and analytics solutions. This category includes 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 moderate pricing ($9.99 per month for a Pro version).
Image credit: Microsoft
Thanks to Azure services, Power BI became 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 prevent it from being one of the best data visualization tools for log data.
Image credit: Elasticsearch
Kibana allows you to explore various big data visualization techniques - interactive charts, maps, histograms, etc. Moreover, Kibana goes beyond building standard dashboards for data visualization and analytics.
This tool will help you create advanced analytics: combine visualizations from multiple sources to find correlations, explore trends and add machine learning features to reveal hidden relationships between events. Set of developer tools and APIs come as a cherry on top.
The last but not least in the list is Grafana — a professional data visualization and analytic tool that supports 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 the case of Grafana, it’s metrics. It makes this tool popular among IoT data visualization solutions.
Grafana allows you to visualize and compile different types of metrics data into complex dynamic dashboards. 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 has perks for fast data analytics, such as creating custom filters and making annotations — adding metadata to certain events on a dashboard.
Video credit: Grafana
These are just the major data visualization tools and techniques. All the mentioned platforms and services are evolving very fast and introduce new features and capabilities to keep up with the market needs especially given the growing pace of big data and Internet of Things development.
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Tips to create an efficient data dashboard
Choosing the right data visualization techniques and tools is the key point to figure out when working with data. However, it is not the only one.
Often visualizations are combined into dashboards to provide analysts, management and other users with complete information on a subject. Dashboards have different functions (show changes in conditions, help track activity and location in real-time, provide remote monitoring and control of a system, etc.) and specifics (dynamic vs. static, historical vs. real-time, KPI/goals dashboards, etc.) that determine their design and features. However, there are several important factors to consider when you create a data dashboard of any type or purpose:
Tip 1. Consistency
Consistency is the key to fluency and fast dashboard navigation. It’s important to stick to specific color-coding, fonts, styles and visualization elements when showing the same metrics across different dashboards.
Tip 2. The right choice of visualizations
No visualization is one-size-fits-all, even a line chart. It’s crucial to choose the right visualization technique for each type of data on a dashboard to ensure its usability and avoid confusion or even misinterpretation. Check the examples
Tip 3. Personalization
Not only does the audience impact the choice of individual visualizations but also determines how to create a data analysis dashboard. It’s essential to keep the goals of different end-users in mind when deciding what visualizations and data should be included in a dashboard. After all, important information for one user can be unessential or even meaningless for the others.
For example, a health tracking app used by patients and doctors should have two personalized dashboards. The patient’s dashboard can include basic health data such as blood pressure, medication intake, activity tracking, while the doctor’s dashboard can combine this data with test results, EHR notes and other medical information to provide a more comprehensive picture of the patient’s condition.
Tip 4. Device constraints
Screen size is an important parameter when we are talking about multifunctional dashboards that are supposed to be used on different devices. A dashboard on a small mobile screen should provide no less value as a dashboard on a desktop screen. For this purpose, designers should consider responsiveness and provide tools and features to easily manipulate dashboards on limited smartphone screens — quickly navigate between views, drill data, compile custom reports, etc. This point is particularly important when creating UX/UI design for IoT apps for they are usually data-heavy.
Tip 5. Value
A dashboard should provide value the moment the user accesses it. However, it does not necessarily mean that all the data should be stuffed to screen one. On the contrary, visualizations should be carefully selected, grouped and aligned on every screen to immediately answer all important questions and suggest ways to further explore the data.
Tip 6. Testing
Whether you create an automated data visualization dashboard using the tools like Grafana or design a custom dashboard for your system, it’s important to test your visualizations on different volumes of data and in different conditions before going live. In many cases, dashboards are developed based on test data. Once released, dashboards show real data which can be quite different from the test data. As a result, these dashboards look and behave as intended. Testing in different conditions helps bridge this gap and avoid inconsistency.
Are you working on a data visualization project? Or looking for a skilled team to help you choose data visualization solutions, design and create a data dashboard for your application? At Digiteum, we have solid experience working with all top data visualization and analytics platforms and tools. Our designers and developers created stunning visualizations for such companies as Oxford Languages, Printique, Feed.fm. Check our portfolio and contact our team to get a consultation on your project.
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