Digitalization has become a consensus of social development. If enterprises want to take advantage of future competition and gain access to the ever-developing digital economy, they must regard data as their strategic resources, use data visualization to transform data into information, and promote enterprise development.
What is data visualization
In the early field of data analysis, most companies could only use data and text for logical deduction when conducting business analysis, and only presented charts as the results of analysis reports. However, with the deepening of digitalization, the amount of enterprise data has grown rapidly, and the emphasis on data has also continued to increase. At this time, the demand for data processing such as data analysis and data mining has continued to increase, and data visualization has officially entered the stage.
Data Visualization – Parker Data BI Visual Analysis Platform
Data visualization is a compound word that comes from the combination of data and visualization. It is easy to understand after taking it apart. Data visualization refers to using graphical means to transform data into visual charts, and using statistical analysis methods to obtain the value information hidden behind the data in a more intuitive form.
Data visualization is still in a stage of rapid development, and new features are constantly being added and new analysis methods are being explored. Interaction is a very important feature in data visualization. It ensures that after the data is converted into visual charts, it still has strong logic and complex analysis capabilities.
This ever-increasing feature endows data visualization with great vitality, making it gradually popular in the era of data processing and becoming a basic requirement of enterprises.
What are the benefits of data visualization?
1. Make data easier to digest
I often joke with people that it’s not that we choose to use visualization to process data, but that the brain is a processor that is better at processing image information and can quickly digest information using charts. This makes data visualization naturally better adapted and can Maximize the power of your brain's processor.
Data Visualization - Pico Data BI Visual Analysis Platform
2. Make it easier for data to convey information
Data visualization analysts can use graphical methods to place large pieces of data into small charts, simplifying the content and making data communication more concise. Analysts can also use rich charts and various colors to show differences in the data. It's smoother and more intuitive to watch.
3. Make it easier for data to display logic
Through data visualization, analysts can display changes in data trends through column charts, line charts, etc., that is, the logic between data. Not only trends, pie charts can show the percentage of data in the total, and scatter charts can show data correlation... These make the data logical and better show the results of data analysis.
4. Make data easier to analyze in depth
Data visualization has functions such as linkage and drill-down, allowing analysts to visualize in-depth analysis. Linkage means that different charts are related to each other and can change their own content according to the attributes of other charts. Drilling means that charts can be advanced layer by layer. Clicking on the indicator data in a chart can drill down or drill up to other levels.
What are the data visualization tools?
At present, visualization tools are mainly divided into two types, one is visualization tools mostly used by individuals, and the other is business intelligence BI tools mostly used by enterprises.
1. Visualization tools
The advantage of visualization tools is that they are more lightweight, and simple charts can be made through templates. Visualization tools can also be subdivided into two types. One is free and paid. Such visualization tools generally have restrictions on watermarks, functions, import and export, etc., and payment unlocks full functions.
Code visualization tool – ECharts
The other is open source visualization tools. Generally, all functions can be used for free, and complex data visualization reports can also be produced. However, it usually requires writing code to produce visual charts, which requires relatively high IT skills from users.
2. Business intelligence BI
Business intelligence BI has relatively complete functions and rich component templates. It is a complete set of data technology solutions consisting of data warehouse, query reports, data analysis, data visualization, etc. BI can directly connect to the database and store data from different sources in the data warehouse. It also has data processing capabilities such as ETL and data models, and hierarchies and classifications of data in the form of indicators and labels.
Business Intelligence BI – Parker Data Business Intelligence BI Visual Analysis Platform
Business intelligence BI is generally deployed in enterprises to provide services for a fee, and is a very popular visual analysis platform for enterprises. In BI, data visualization can produce visual reports for PCs, mobile terminals, and large screens respectively. Just drag and drop to complete data visualization analysis and create visual reports. It also has detailed user permission setting functions to protect data security.
How to do data visualization
1. Determine needs
Before designing data visualization, analysts must first complete the analysis of business requirements, split the analysis requirements into tasks of different levels and topics, capture the business data indicators and labels, and divide them into different priorities to obtain data for the next step. prepare for.
In the process of confirming requirements, analysts need to pay special attention to the correspondence between business and data, confirm the indicators and labels in the data warehouse according to the data dictionary, conduct research on data quality, and maximize the accuracy of data visualization.
2. Prepare data
Before performing visual analysis, analysts should prepare the data required for the task in advance and make preparations before analysis. At this stage, analysts can work with technical staff to retrieve indicators, labels, dimensions and other data required for subsequent data visualization from the data warehouse to prepare for data analysis.
Business Intelligence BI – Parker Data Business Intelligence BI Visual Analysis Platform
In the process of preparing data, analysts can further confirm the business data, communicate and collaborate with front-line business personnel, and confirm that the data and business are consistent with each other, and that the data is consistent with business changes. Then you can think about the relationship between the data, organize and mark the key data.
3. Select the chart
After the requirements and data are confirmed, analysts can think about the relationship between data, pair data according to business, indicators, dimensions, measures, etc., and select charts that can correctly express the data logic.
The choice of charts is directly related to the visual presentation effect. A suitable chart can transform the relationship between data into intuitive information and demonstrate business development logically, while the wrong chart may lead the viewing object in the wrong direction. .
Data visualization analysts must understand all mainstream chart types, know what analysis each chart is suitable for, what logic it expresses, and what type of information it can display. Here are a few simple examples:
(1) Column chart
Usage scenario: Column chart is one of the most commonly used charts. Because there is only one variable, it is usually used to compare the size of different data and can also reflect the trend between different nodes to a certain extent.
Advantages: The differences between different columns are obvious, and the comparison between data is very obvious.
Disadvantages: The amount of information presented is less and can only be applied to small-scale data sets.
(2) Line chart
Usage scenario: Line charts are generally used to present changes between continuous data, and are also used when comparing different data sets.
Advantages: It can intuitively show the change trend between data.
Disadvantages: It is difficult to see the relationship between individual data and overall data.
(3) Pie chart
Usage scenario: Pie charts are generally used to represent the proportion of data in the total.
Advantages: It can clearly show the proportion of each component module.
Disadvantages: The data ratio is not intuitive enough to show the details of individual data in detail.
(4) Dashboard
Usage scenario: Dashboards are generally used for data visualization reports, presenting values and proportions.
Advantages: It can achieve beautiful and rich chart effects, and can handle complex data.
Disadvantages: The usage scenarios are small, and the data types presented are relatively single.
4. Page layout
After the preparation work is completed, data visualization analysts can officially start producing visual reports. Page layout is a great test of data visualization staff's sensitivity to size. They need to display as much information as possible on one page without appearing bloated and to convey information clearly.
Proficient mastery of page layout requires analysts to have rich experience in data visualization and have studied different charts and actual cases in different fields.
- Sensitive to page size, able to spread data across the entire page with appropriate visualization charts;
- It needs to have a certain sense of beauty to make data visualization simple, beautiful and watchable;
- Have a sense of priority and be able to divide different charts on the page into different levels to highlight key indicators;
- Good at using functions such as linkage and drill-down to deeply dig into the business information hidden behind the data;
- Have the ability to make logical deductions, avoid too much explanatory text, and let charts convey most of the information;
- Master the relationship between data and business, place related data adjacent to each other, and improve the efficiency of data information transmission.
(1) Group signboard
Divide the data into different topics, present the associated data on one page, take the center map as the core data, and the secondary information is scattered around.
Data Visualization – Parker Data Business Intelligence BI Visual Analysis Platform
(2) Management cockpit
The top and bottom layers of the page are designed, the core data and the secondary data are clearly separated, and the key indicators of the enterprise are highlighted through the bold and bold design.
Data Visualization – Parker Data Business Intelligence BI Visual Analysis Platform
(3) Customer relationship screen
The page is divided into three layers to display a large amount of business data information, and the core data is displayed in the center of the top layer in the form of a cockpit, which is intuitive and effective.
Data Visualization – Parker Data Business Intelligence BI Visual Analysis Platform