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Data Visualisation is Dead

The term « Data Visualisation » is a trend that has rapidly grown alongside « Big Data ».

The Data visualisation concept is quite simple. Nowadays, it’s almost impossible and very time consuming to analyse on a « big data » scale with figures on spreadsheets. Besides, the analysis wouldn’t be as accurate, with companies receiving and generating increasingly large amounts of data every second. It’s all about turning unreadable data into more visual and easily understandable graphs. It assembles useful data to express it better, understand it, and highlight analytics. It becomes a way of storytelling the data.

The benefits of Data Visualisation are numerous. First of all, it has increased the usage of data infographics in general, whether it be in journalism, in factories where it isn’t rare to see more screens displaying real-time indicators of data. This is because Data visualisation makes data more accessible and readable with the continuously improving speed of process and responsiveness to data.

The term User Experience (UX) was invented by Don Norman, author of The Design of Everyday Things, while he was working at Apple. According to him, “No product is an island. A product is more than the product. It is a cohesive, integrated set of experiences”. This is because UX answers the customer’s needs by improving user-friendliness and providing pleasure when they use the product or service.

User Experience became crucial in the digital world when people realized that, to have more customers, their product or service had to be easy to use and more pleasurable for better customer satisfaction. UX becomes a time and money saving solution, as it prevents managers or bosses from making wrong decisions and costly mistakes that can take time to fix afterwards.

The methodology used for User Experience is about design thinking, it’s about « Designing the right things/Designing things right ». There are steps to follow for this; usually 5 that are iterative.

Firstly, Empathizing. UX research must be conducted for this, to identify the target (not the bosses or managers but the actual users). Doing a benchmark is also very useful for this step, to figure out what is working for a potential competitor, what is missing or to know how to make it better by adding new functionalities. Surveys and interviews are helpful. Then, the UX designer creates a user persona(s) (that represents user types) and user journeys (a map of processes that highlights their pain points). These tools are necessary for the second step, which is Defining, finding the real problematic and brainstorming around it by Ideating. Then, the fourth step is Prototyping. This step can be done in various ways; on paper, or digital form. To be sure that all of this fits the needs of the users, the final step is important; Testing. There are two types; on one hand, the usability test to make sure that all functionalities are working well and that the product or service can be used properly. On the other hand, the desirability test, to see how much they want to use it, the outlook, and how nice the design is.

The Double Diamond approach can be applied to this iterative process. Meaning, divergent thinking is necessary when identifying a problem, taking into consideration any hint, feedback. Then convergent thinking narrows and focuses on a problem. After that, another divergent thinking is necessary to find a solution and brainstorm, then finally converge to have a specific solution.

Despite common belief, UX design is not only for interfaces like websites, or apps but is also used in every field, even in everyday life.

The ultimate goals of UX:

  • Answer user needs,
  • User-friendly → more understandable (no training needed)
  • Bring interaction between users and the product

Now, the question is, how are those goals achieved while using data analytics?

Users have many needs to be answered. For this, first we need to understand the business context and know which data is essential. Then, the method used to navigate through it is important, to access a particular data from the one they already have, to filter it, or drill down to match exactly what they’re looking for. Also, the user might want to access data visualisation on a PC and/or a mobile device, they might need it quickly, without any delay. If the user is not tech-savvy, the analytical app has to be designed for a newbie.

If this app is not user-friendly, it won’t be used as much. Users search for usable data, not just artistically presented or incomprehensible data. They need to be able to read actionable insights without prior training, demonstration or tools to understand.

The user will prefer to interact and navigate autonomously with the data.

Data visualisation should not only show data for the sole use of the current user but also reflect the usage of his team or data stakeholders. This can be done by having the ability to add your own comment within the data to share with others.

It’s not only about turning unreadable data into just graphs anymore. It’s vital to understand that Data visualisation without UX comes short of real value. This is why we urge you to bring them together by implementing UX to keep your users happy.

For more specific information on how to do this, don’t hesitate to ask our expert UX designer team.

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