How to Use Data Visualization Techniques and Tools for Business
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 review major data visualization instruments and name the key factors that influence the choice of visualization techniques and tools. We will talk about the most widely-used tools for data visualization and give a few pro tips on how to combine data visualizations into effective dashboards.
Visualization is the first step to make sense of data. To translate and present complex data and relations in a simple way, data analysts use different methods of data visualization — 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 you 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 data 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 equipped with different tools for visual data analytics (comparison, formatting, filtering, etc.). However, dashboards are not necessary to show a single or occasional data insight.
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Depending on these factors, you can choose different data visualization techniques and configure their features. Here are the common types of data 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 to visualize the relationships between large volumes of data.
Maps are popular techniques used for data visualization in different industries. They allow locating 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 in one visual representation. 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
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 technologies and 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 and data representation techniques, 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 allows to combine visualizations into functional dashboards. Tableau invests in AI and augmented analytics and equips customers with tools for advanced analytics and forecasting.
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. They stand out for rich functionality to fully unlock the benefits of data visualization and also allow to add visual data analytics techniques and features to complex scalable systems built on modern web app architecture approaches and cloud technologies.
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 supports conversational data control and provides versatile 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 you to create customized reports from different data sources and get insights in a couple of clicks. Secondly, Power BI is powerful and can easily work with streaming real-time data. 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. 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 in data science — 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. Rich toolkit for developers and APIs come as a cherry on top.
Last but not least in the list is Grafana — professional data visualization and analytic tool that supports a wide range of 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 for building IoT applications and creating visualizations for telemetry systems that use different IoT data collection methods.
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 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 tools and techniques of data visualization. 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.
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.
We have worked on a range of data visualization and analytics projects for cleantech, healthcare, retail and IT companies and successfully integrated custom data solutions into their operations. Here are a few use cases that show different approaches to data visualization and their effect on business outcomes.
Efficient performance monitoring helps a printing company handle a 200% traffic increase with a 0% slowdown
Our long-standing client, one of the leading printing companies in the U.S., was dealing with a traffic upsurge challenge every holiday season. They needed an effective monitoring and control solution to avoid website slowdown or performance decrease.
Our dedicated team has been working with the lab's tech infrastructure for a decade. To address the challenge, we developed a custom data analytics and visualization tool based on the combination of Elastic Stack solutions. It collects and analyzes real-time performance data – website traffic, backend load, the quantity and status of submitted orders, printing status and queue. The system then visualizes data for real-time monitoring, registers failed operations and sends alerts to the support team if any problem is spotted.
From the moment this tool was integrated into the lab’s operations, the company could better manage growing traffic and go through intensive seasonal sales without slowdown or lost orders. Since then, we have upgraded the monitoring system and scaled the client's entire infrastructure to better address the constantly growing load (microservices development services to enhance scalability and resilience, migration to modern .NET technologies, adoption of cloud and data services, etc.)
Monitor IT infrastructure in real-time to improve security and employees’ performance
A Finnish tech company developed a SaaS product for real-time IT infrastructure monitoring. The system was originally focused on Windows products, and we were asked to scale it up for the macOS platform.
We developed a multifunctional monitoring system to track performance and provide real-time data on the health and security of the customers' IT infrastructures. The system collects data on registered software and hardware performance, sends it to the cloud-based backend for analysis, and provides customers with visualized insights on dashboards and reports.
When integrated into the company’s operations, the system helps improve employee productivity and mitigate possible security and performance risks. It is an effective tool for IT asset tracking and management.
These are a few examples that demonstrate how effective data visualization systems and tools affect business operations and help companies deal with major performance challenges.
We work with a wide toolkit of data visualization and analytics platforms and apply a range of techniques, including popular data visualization techniques in Machine Learning, IoT and big data. If you are looking for an expert to help you make sense of your business data and develop custom digital tools to work with it, send your request over our quick contact form. Check our big data services to learn how we can help you turn your data into source of income and growth... without losing track of your costs.
In this article, we answered many questions: What are data visualization techniques and tools? How to use different visualization tools to unlock the power of data? How to build effective data dashboards? You can use this foundational knowledge to start working with your data and select tools that will help you extract real value from your assets.
If you are looking for a skilled team to help you design and create a data visualization system for your business, consult on the use of professional data visualization tools and integrate them into your decision-making process, we can help. At Digiteum, we have solid experience working with top data visualization and analytics platforms. Our designers and developers create stunning visualizations and data tools for such companies as Oxford Languages, Printique, Diaceutics. Check our portfolio and contact our team to get a consultation on your project.
- Power BI
- Create consistent color coding, styles and visualization elements for easy navigation.
- Choose the right data visualization techniques for different types of data and use cases.
- Personalize dashboard layout and functionality to specific audience.
- Take into consideration device constraints and provide additional tools to manipulate data on small screens.
- Group and align data to maximize value, not volume.
- Test dashboard performance using different volumes of data to see how it can handle the real data flow and dynamics.