Brief introduction: Quick BI is an intelligent data analysis and visualization BI product specially designed for cloud users. It helps enterprises to quickly complete the transformation from traditional data analysis to data cloud + analysis cloud, and push the business data of enterprises to various organizations for consumption and use at the fastest speed after output. This article focuses on the capabilities and evolution of Quick BI in visual analysis.

This article focuses on the capabilities and evolution of Quick BI in visual analysis.

Capability map for Quick BI visual analysis

The result of a traditional report is an interpretation of the statistical model rather than the business. Therefore, the expectation of visual analysis is not the report form, but the DataStorytelling with analysis (insight) and narrative (insight). The definition of visualization of Quick BI is also extended from “visual chart” to “insight + interpretation”.

Part1 visualization

Quick BI visualizations include visual diagrams, templates, and themes. Themes support 6 sets of default theme backgrounds (including light and dark skin colors), as well as user-defined themes to adapt to different product styles.

Visualization charts are divided into comparison charts, trend charts, distribution charts, relationship charts, proportion charts and space charts according to the analysis intent. Detailed details will be developed in another visualization.



Part2 Data Insights – Analytical components

Visualization has always been the core competence of BI products, and Quick BI believes that data visualization should be upgraded from simple data presentation to rapid data analysis, while interactive visualization can significantly improve the efficiency of data analysis.

In addition to quickly discovering associations and constituent relationships between data through interactive operations (drilldown, linkage, jump, circle selection, exclusion, etc.), Quick BI further opens up a whole new way of fast insight by providing interactive diagrams (metric disaggregation diagrams).

The newly added index disintegration diagram can help users customize the indicators and latitude required for analysis, and users can adjust the analysis dimension and disintegration sequence at any time, so as to quickly locate the cause of influence when the indicators fluctuate or are abnormal.

At the same time, Quick BI’s new online dynamic components support the expression of static data in dynamic form. The conventional pie chart, bar chart, bubble chart and so on can only express the data of slices, but can not express the data that changes with time. By combining the playback axis with the time line, Quick BI can make even the most common graph intuitively express the dynamic changes of the business.

For example, the dynamic bar charts used in the epidemic this year give you a clear picture of how the epidemic has changed over time in different countries and regions. Here, for example, the offline and online user activity changes over time.

Part2 Data Insights – Volatility Analysis

In addition to describing the current information in the data, Quick BI enables analysts to quickly capture and discover the value hidden in the data by combining machine learning, artificial intelligence, and visual analysis capabilities. For example, fluctuation cause analysis can help users automatically disband and analyze the causes of fluctuation of core indicators in trend graphics, and present the influence of various factors on the fluctuation in natural language. For example, the change trend of the order quantity of a store is as follows. Based on the analysis of the reasons for the fluctuations, the key influencing factors of the current order quantity are that NEW_ADD is 1111, and the region is North China. Therefore, we can ask specifically whether some operation activities and operations have been carried out.

Part3 Build the data story

In order to solve the business of multiple reports and scattered. It is also a problem that a reader cannot understand if there is a large capacity in a single report but no explanation from others. Therefore, it is necessary to introduce Data Storytelling.

Data Storytelling is a form of Data Storytelling that combines interactive Data visualization with Storytelling techniques to present your analysis in an eye-catching and easy-to-understand format. In the past two years, Gartner paid more attention to the value of Data storytelling in ABI. Quick BI also introduced two levels of Data story-building capabilities by combining its own capabilities and the scenarios of domestic users.

The first level is the multi-page level. Quick BI supports the organization of a large number of reports and links into a “data portal,” which can help enterprises present their data analysis system from a business perspective and in a hierarchical manner. Such a portal can be the overall situation of the company to the form of sub-business, sub-sector, can also be in accordance with the production of each link of a data content. A portal is a perspective to analyze usage data, and a good portal can often clearly express the business composition and even strategic direction of the enterprise.

The second level is at the page level, where a single dashboard tends to focus on an analysis topic or several analysis indicators, often in the form of an overall overview, trend changes, and segmentation of different dimensions within a single page. However, analysts often encounter problems such as lack of analysis ideas, too many analysis components, and not knowing how to choose. The newly introduced “story line” component of Quick BI can greatly improve the efficiency of analysts in organizing analysis ideas and presenting data value, so that visitors to reports can quickly grasp the meaning and value behind the data.



The above three parties will be explained later, so stay tuned!

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