FineBI Glossary

My Analysis

It is a storage space for resources used for personal analysis. You can explore and analyze data throught the 

whole process in this space.

Analysis Subject

It is the fundamental unit of data analysis (core module of FineBI). Within an analysis subject, you can perform 

data editing, visual analysis, and dashboard export to complete the analysis process.

Meanwhile, you can collaborate with others on an analysis subject.

Collaboration

This function is provided for you to collaborate with other design users on a folder or an analysis subject.

Users you collaborate with can view or use the content related to the analysis subject.

You can edit an analysis subject with others collaboratively.

Filter

FineBI provides the data filter function, allowing you to filter and save data for subsequent analysis.

Field

A field corresponds to a column in a data table. To create a visual component, you need to drag fields from a 

data table into the analysis area. Therefore, fields serve as the basic for achieving visual analysis.

Dimension

A dimension refers to the perspective from which you analyze data. You can analyze data from different 

dimensions. For example, you can analyze the sales fluctuations across different months or years. In this case, months/years serve as dimensions.

Indicator

Indicators quantify dimensions. Dimensions help analyze data from various perspectives, while indicators are 

the outcomes (numerical values or ratios) of analyzing these dimensions.

For example, you can analyze the sales fluctuations across different months or years. In this case, 

sales serve as indicators.

Data Visualization

Data Visualization is provided for you to convert data to easily understandable and interpretable visual forms 

such as tables, charts, and maps. It utilizes visual elements and interactive designs, helping you better 

understand and analyze large amounts of data to discover patterns, trends, correlations, and insights.

Component

Visual components refers to independent modules or elements that constitute the data visualization system or 

tool. Serving as the basic building blocks for data display and interaction, these components can be utilized and 

combined in data visualization applications to create rich pages and functionalities.

Visual components utilized for data analysis in FineBI include tables, charts, time filter components, and 

text components.

Aggregation

This function is used for aggregating multiple rows into one single row according to certain criteria, namely, summarizing data as higher-level row-level data.

Indicator aggregation is used for displaying all indicators on the same value axis, enabling you to compare the 

values and trends of different indicators within the same dimension.

Aggregation functions are used for summarizing a set of data. Generally, the values obtained through

aggregation functions are used for re-calculations. Moreover, as you switch analysis dimensions, calculation 

fields automatically adjust to the changed dimensions.

Indicator Name

Indicator Name stores names of indicator fields in charts.

You can drag the Indicator Name field into Graphic Property (for example, Color) to generate a legend.

Filter Component

When viewing a dashboard, you may want to change the field to be filtered or need to filter data in multiple 

components at the same time. In this case, the filter component can help.

Linkage

By setting linkages, you can click a component so that the relevant data is displayed in other linked components.

Jump

To jump from the current dashboard to other pages (such as web pages, other dashboards, and FineReport 

templates), you can use the jump function.

Drill

Drilling (including drilling up and drilling down) allows you to dynamically change the level of dimensions while 

you are viewing a dashboard. You can drill down to view data of specific cities when viewing data of provinces.

Public Data

Public Data is a storage space for data tables provided by enterprises (namely, enterprise-level data) for 

staff to view and use.

Data Platform

Targeted at data processing personnel, the data platform boasts the core value to support synchronization 

and complex processing of data within the same database or across different databases. Building on this, the platform also supports the orchestration of multi-branch andsteps, facilitating enterprises in constructing 

underlying data that is higher-quality and more analyzable.

AARRR Model

The AARRR model (also known as the Pirate Metrics), commonly used in the user operation process, is an 

acronym for a set of five indicators (namely, Acquisition, Activation, Retention, Revenue, and Referral) for 

facilitating user growth. From customer acquisition to spreading and recommendation, the model forms a 

closed-loop pattern of a user's entire lifecycle, explaining the process of continuously expanded user base and sustained growth.

RFM Analysis

RFM analysis, a simple and practical customer analysis method proposed by American database marketing 

research institutes, evaluates customer data based on the following factors:

Recency (R): interval between the current time and customers' last purchase time.

Frequency (F): number of times customers purchase within a specified period of time.

Monetary (M): customers' consumption capability, usually based on the average amount of consumption per 

transaction.

The analysis evaluates and classifies customer data through the three key indicators to determine values of 

each segmented customers. Corresponding marketing strategies are tailored to customers with different 

features.

ABC Analysis (Pareto)

By classifying objects according to their main technical or economic characteristics, you can distinguish between prior and general objects, and thus apply different management methods. ABC analysis divides the analyzed objects into three categories: A, B, and C, and there are no fixed thresholds for each class.

Market Basket Analysis

Market basket analysis is an analysis method that associates two different products and explores their 

relations by analyzing user consumption data.

DuPont Analysis

The DuPont analysis method leverages the interrelationships among various key financial ratios to 

comprehensively assess a company's financial status. It is employed to evaluate the profitability and return on equity of a company, analyzing the company's performance from a financial perspective.

The core idea is to break down the return on equity of a company into a series of financial ratio products, allowing for more in-depth analysis and comparison of the company's operational performance.

KANO Model

Based on analyzing the impact of user needs on user satisfaction, this model serves as a useful tool in 

classifying and prioritizing user needs, helping reflect the nonlinear relationship between product performance and user satisfaction.

BCG Matrix

BCG Matrix is also known as the growth-share matrix, Boston Consulting Group Analysis, portfolio diagram, 

and product portfolio matrix. The matrix analyzes and determines a company's product portfolio 

structure based on sales growth rate (reflecting market attractiveness) and market share

(reflecting company strength).