To a certain extent, data visualization has an element of artistic expression, and successful data visualization is breathtakingly beautiful. Success, although there is no single "right" answer, there are some routes and best practices worth following. Effective data expression often relies on many "principles."
The chart is the most common and basic element in a data visualization project. Its selection and use are often the first step in the process of a data visualization project. Appropriate selection of charts can not only present the situation and logic between the data more clearly and clearly, but also more in line with the human visual sensory experience.
In layman's terms, it is nothing more than to graphically display complex data information. The purpose is to facilitate users to understand or analyze more efficiently from a pile of messy data, so that data information that can be summarized in an hour can be transformed into a glance. Readable data chart.
However, a good visual design must be easy to read, highlight the value of the data, easy to analyze, and beautiful. It will ultimately make the data simpler and facilitate communication. On the contrary, it will not only make the data more complex, but also bring errors. Induce. Therefore, how to make data analysis easy, smooth and easy to read, so as to improve the user's work efficiency and reduce the user's workload.
Chart design
Step 1: Clarify data indicators
First of all, we must first understand how and why these data come from. If we don't even know this, it will be difficult to start the discussion or design. Data is the prerequisite for a good chart design. There is no doubt that a series of numbers is boring for the designer. Fortunately, the previous data collection work has been done by someone, but as a designer, it is necessary to ask them to give you everything. Possibly accurate data, otherwise, it will lead to the loss of previous work. Therefore, it is best to address the following points when initially contacting data:
1. Understand the data and indicators
2. Analyze the data
3. Refine key information
4. Clarify data relationships and topics
Step 2: Design for whom and what information users want
What needs to be clear is that the same set of data sees different information in the eyes of different users, because the differences in roles and positions cause their focus and position to be different, and the information found by different people is different. The conclusions are also different, so the information and interaction methods emphasized by different users in the chart design are different. the main factor of influence:
1. Who is the user group? What are the characteristics
2. What information needs to be extracted from the data
3. What problem do you want to solve through the chart
4. Focus of attention
Step 3: Clarify the design purpose and value
In fact, graphic design is similar to a product design. The process of defining design goals is easily ignored by designers. Design goals are not static, but it does not mean that there are none at the beginning. There is a lack of definition of design goals in the early stage. As a result, designers often don't know why they are designed in this way, so the next design work is like a headless fly, without a sense of direction. Sometimes, the design plan is overturned. The root cause is often caused by unclear thinking about the source. The design goal needs to be defined and agreed on by everyone. Otherwise, the direction is wrong and the efforts are in vain.
Step 4: Plan the design plan and choose the appropriate chart type
In work, some students spend a lot of time on finding chart materials when designing charts. However, this kind of solution is looking for solutions on the surface. In fact, it turns the cart before the horse and cannot solve the essential problem. Data visualization design is not a simple chart style design. Although understanding charts is also very important, it is just a change in form that just turns data into beautiful charts, and is far from enough.
Basically, the following six common chart types cover most of the usage scenarios:
The graph is used to reflect the trend of time change
The histogram is used to reflect the comparison between classified items, and it can also be used to reflect the time trend
Bar graphs are used to reflect comparisons between items
The pie chart is used to reflect the composition, that is, the proportion of the part to the whole
Scatter plots are used to reflect correlation or distribution relationships
The map is used to reflect the classification comparison between regions
If you really don't know how to design beautiful and appropriate charts, you can try Smartbi's self-service dashboard. Smartbi supports the use of Excel as a report designer, which is perfectly compatible with Excel configuration items. It supports all built-in graphics, background images, conditional formatting and other design complex dashboard styles in Excel. Through the excel plug-in function, all Excel graphics such as characteristic graphics: mini-chart, Pareto chart, bullet chart, small and multi-chart and other characteristic graphics; commonly used graphics column chart, pie chart, line chart, radar chart, etc., combined with data warehouse Dynamic data in the data display.
Step Five: Refine the experience
Earlier we talked about a lot of the early stage of chart design. Next, let’s talk about some details that need to be paid attention to. Dan Saffer said, “The best products usually do two things well: functions and details. Functions can attract users’ attention to this. The product, and the details can keep the users concerned." After all, detailed design makes excellent products~
to sum up
The chart design process is more like a series of processes that establish a dialogue between users and data. Our general consideration is how to make complex and confusing data easier to present to users, and allow users to quickly and efficiently understand and analyze Give the correct feedback, and finally build a round of interactive behavior. A good chart design should firstly express the data directly, simply and accurately. Don't let readers guess the chart information, ensure the effectiveness of information transmission, and pay attention to beauty and details without errors.
Data visualization can connect seemingly unrelated data, discover laws and gain insights from it, and gain valuable business insights. Smartbi supports not only Excel static graphics but also Echarts dynamic graphics. Excel data visualization (conditional formatting, etc.) and echarts data visualization (maps, word clouds, etc.) work together, and the rich combination of dynamic and static effects clearly and intuitively express the story hidden behind the data.