Data visualization is a very powerful way to generate business insights and data-driven decisions. The way of creating visual data has evolved over time, from simple and static charts in the past to today’s interactive and attractive visually cool charts. The current trend of data visualization gives us a glimpse of the future visualization. What kind of development situation will it look like.
1. Keep users at the center of data visualization design
For some data visualization software, the available traditional user interface and data visualization options (such as reports, charts, dashboards, etc.) provide users with the opportunity to get started quickly and make the platform suitable for users. In traditional user interfaces, the method is usually "more data equals more value", which means that if you are not sure what data users need to achieve their goals, and blindly give too much data to users, it will cause users Misunderstand the information provided by data visualization and make wrong decisions. What we need is a data visualization that can be personalized according to the specific needs of each user.
User-centered design (UCD) is an important trend that puts users first, then data. Regardless of the industry, UCD follows the same thinking process, starting with thinking about users and their specific pain points. What problems are users trying to solve, and what possible obstacles are they facing? What information and functions do they need to solve the problem? How can we create data visualization for them in the best way? Users want a simple and direct user experience, and provide the required information with minimal complexity in order to make recommendations for the next step so that they can focus on more strategic and valuable work.
One of the latest user experience and data visualization trends is to merge the user's workflow with feasible insights, recommendations, predictions, and best follow-up actions for current tasks or decisions. Savvy business users can delve further into the data and discover patterns, trends, and correlations.
2. Data visualization is becoming more and more social
Data visualization is becoming more and more social, and recently it is often displayed on various social media platforms, attracting many followers. The data analysis industry is also aware of this trend and is now more focused on the use of visualization tools to better attract followers on social media.
Due to the short stay of social media users, the data must be expressive, visually appealing, and concise. The visualization of data shared by social media follows the "less is more" philosophy. Some examples of social media include 3D animation, GIF, and content and data visualization in the form of videos, often used in popular video sharing applications.
Dashboards used in internal data visualization software usually provide real-time data that can be used for effective collaboration and decision-making by team members using the software. They also have multi-functional widgets, map components, and trend indicators to help the team understand data visualization and support effective communication between different users. Nowadays, the best data visualization software has the function of graphing and sharing data links on desktop computers to mobile devices.
"Picture from EasyV Data Visualization"
The entire dashboard can be shared with team members and can be analyzed via computers and mobile devices (such as smartphones and tablets). These social and sharing features are very useful for successfully passing information between team members in an organization.
3. Data will become more and more democratized
Another interesting trend is the democratization of data, which means that anyone can use data to make decisions at any time without access rights. Data visualization tools play an important role in democratizing data and analysis and making data-driven insights accessible to all users throughout the organization. It enables users to access visualized data and build interactive personalized dashboards with a single click.
4. Deliver stories through data visualization
When people hear stories, they usually feel emotionally engaged and tend to remember them better. Data and numbers do have important stories to tell, but they rely on you to give them a clear and convincing voice. The concept of data visualization for telling stories and giving meaning to numbers and data is becoming more and more popular.
However, just visualizing the data is not enough. Data visualizers need to transform into storytellers; first discover the meaning of the data itself, and then create a narrative form that helps the audience discover its meaning while keeping them interacting with the data. To achieve this, arrange a series of visualizations together in order to make your audience understand the data and help them come up with meaningful explanations. Creating such a visual story from a complex data set can be a daunting task, but if done correctly, it will help differentiate the data visualization and prove to be a competitive differentiator.
5. Data visualization is no longer limited to data scientists and analysts
IBM predicts through a new study that in the next few years, the demand for data scientists and data engineers will grow by nearly 40%. At the same time, today's employers are now beginning to expect a certain degree of familiarity with data processing and data visualization not only for scientists and engineers throughout the organization. Because of this trend, we can expect continued growth in tools and resources aimed at making the field of data visualization and its benefits more accessible to everyone, not just data analysts.
For example, resources such as EasyV data visualization of Kangaroo Cloud in the big data industry are becoming more and more popular. This resource is composed of more than 1,000 data visualization components and is designed to help people who want to build their own data visualization so that users can easily Create large screens for visualization and data visualization without any coding skills.
6. Artificial intelligence and machine learning will make the creation of data visualization smarter
Machine learning (ML) and artificial intelligence (AI) are areas where technologies are rapidly evolving and are constantly being applied to business goals in new ways. These emerging technologies are also playing an increasingly important role in data visualization.
Too much data is obtained from a large number of different sources, including internal systems (such as enterprise resource planning (ERP) and customer relationship management (CRM) applications), or external resources (such as web and streaming data). The format of this type of data varies widely, but it must be integrated, formatted, and customized to make it relevant to a specific user, which involves finding key insights that help to achieve better results. Machine learning and natural language processing (NLP) have powerful capabilities to interpret unstructured data, including text, audio, and video, and discover important insights. This is the first step in developing effective data visualization. ML and AI can help reduce the workload of visualization through automation.
AI and ML can help collect and process large amounts of data at a speed that humans cannot match. In fact, AI can even analyze the most relevant information for a specific user based on context. In addition, certain AI solutions can visually process and express this information for different audiences. This process is called "visual analysis" because it combines the advanced automatic and visual analysis methods of artificial intelligence with human-computer interaction to gather insights.
7. Mobile-friendly data visualization
Mobile devices have become an indispensable work tool for professional and personal purposes. When we use them to access applications, websites, reports, etc., mobile impressions are usually the first impression. When accessing data visualization on mobile devices, the quality of the user experience is critical and will be even more critical in the future. Data visualization developed for enterprise applications or websites must be tested on various mobile devices and different browsers to make the visualization content clear, simple, compact and concise.
Because the attention span is very short when using mobile devices, you must make the data as attractive as possible while simplifying the interactive features. The most important point must be clear and easy to understand so that users can appreciate and deal with it.