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As a self-service data analysis product, FineBI provides rich and powerful functions for enterprise data analysts to make data decisions through efficient self-service analysis.
This article explains the unique concepts of FineBI products in accordance with each module, so that users can use them.
"Group-Business Package-Data Set-Field" is the basic hierarchical structure of data preparation configuration.
definition
"Data set" is a table that users analyze and use.
classification
"Data set" is a data table entity, divided into four categories according to different sources:
Database DB table, SQL data set, Excel data set, self-service data set
But despite the different sources, they are essentially the same, and they are all data tables that can be analyzed and used by users.
purpose
The table (data set) is the basis and foundation of data analysis, and data analysis is the analysis of the data in the table.
"Business package" is a data management concept, a container for storing tables (data sets), and can be understood as a folder for storing data sets
The "Business Package" exists for the purpose of controlling tables (data sets). Therefore, only tables (data sets) can be placed in the "Business Package".
For details, see: Business Package Management, Business Package Grouping
"Grouping" is a data management concept. By adding different groups, the business packages are classified and stored according to requirements, which is convenient for data search.
"Grouping" is equivalent to the upper folder of "Business Package", and "Grouping" is a folder that exists to control the "Business Package". Therefore, only the "Business Package" and the next-level group can be added under "Grouping".
For details, see: Group Management
The "field" is a column in the "data set", each data set is composed of multiple fields.
According to the form classification, it is divided into three fields of numeric, text and date types. Different types can be identified by the signs in front of the fields.
For an introduction to field types, see: Introduction to FineBI Data Types
It is convenient for users to analyze data with data column as the smallest data analysis and processing unit.
During data processing, you can add specified fields (columns) to the independent data set; when the dashboard is performing data analysis, you can select the required fields (columns) for analysis.
For field operations in the basic table, see: Edit the basic table
For field operations in autonomous datasets, see: Field Settings
For the selected fields in the dashboard, see: Overview of Building Visual Components
"Basic table" is a table (data set) that has not been deeply processed by the user.
The self-service data set is a deeply processed table. Therefore, "basic table" represents the three types of "data set": database DB table, SQL data set, and Excel data set.
Note: DB tables and SQL data sets support self-circulating columns and row-column conversions are also a kind of processing, but they are all simple processing that conform to the SQL mode, and there are generally not many steps.
effect
The basic table is often a good original table provided by the administrator (the user of the Excel data set can upload it by himself). There is no analysis if there is no basis. All the analysis can be traced back, and a basic table can be found as the foundation. Therefore, it can be understood that the basic table is the source of analysis, the cornerstone of upper-level analysis, and the root of analysis.
For details on adding basic tables, see: Add database tables, add SQL data sets, and add EXCEL data sets
"My self-service data set" is equivalent to a special group that belongs only to the user. New business packages and groups can be created in it to meet the needs of personalized and private data analysis.
If you do data analysis only for personal needs, you can store these tables (Excel data set and self-service data set) under "My Self-Service Data Set" for viewing and analysis. The administrator will not be unable to view and disclose the contents of my self-service data set through permission settings.
feature
The table in "My self-service data set" is a self-service data set for data analysis.
For details, see: Analytical self-service data set
The "Self-service data set" is a processed table of the depth data in the data set.
The original intention of the emergence of self-service data sets is that ordinary users can perform data processing on their own to meet their needs, rather than uniformly letting the administrator do it for them. Realize the effect of in-depth processing and in-depth analysis of data.
The self-service data set does not distinguish the source of its underlying table. Whether it is a "basic table" or another "self-service data set", data can be processed through the self-service data set again, so it can achieve more in-depth analysis requirements, which is relatively complex Analysis process.
A prepared self-service data set can be used as the basis for subsequent visual analysis, or it can be used by other autonomous data sets.
For details, see: Overview of Self-Service Data Set
The self-service data set created by the "data processing user" under the normal business package (not "My self-service data set grouping") is the "processing nature data set".
"Processing data set" is mainly a table (self-service data set) created based on the "basic table". It is a self-service data set that can handle large amounts of data, and set permissions on it.
In order to give others the basis for data analysis.
"Processing data set" is generally used to provide a larger number of "data analysis users" with tables (self-service data sets) that can perform data analysis. For some "data analysis users" who need in-depth analysis, they can perform personalized data processing based on the "processing nature data set" and perform visual analysis.
"Processing data set" is more complex than "basic table" and simpler intermediate table than visual analysis table. It can meet the general needs of most data analysis users.
For details, see: Self-service data set (data processing users)
The data set created by the "data analysis user" is the "analytical data set";
In addition, the data set created by the "data processing user" under "My self-service data set" is also an "analytical data set".
Note: Created under "My Self-Service Data Set" is not a basis for other people's analysis, so they are self-service data sets of the nature of data analysis.
"Analytical data set" is to achieve a smoother data analysis experience, but it cannot be assigned ranks and permissions, and the amount of data that can be processed is relatively small.
For details, see: Self-service data set (data analysis users)
In the component production, the fields are classified according to the angle of analyzing the data, and are divided into "dimension" and "indicator" fields. The indicators are analyzed from different dimensions, and the dimensions of the analysis are quantified by the indicators to obtain the data analysis results.
The text and date type fields default to the "dimension" field, and the value type defaults to the "indicator" field.
A field is a column in the data set. For details, please refer to the definition of the field under "Section 2".
The fields in the data set in the visualization component are usually divided into two categories: "dimensions" and "indicators".
Dimension fields include: date type field, text type field, indicator name
Indicator fields include: numeric type fields, calculated indicator fields (a field obtained by adding a calculated index to the dashboard), and the number of records
Visual component production is to drag the fields in the data set into the analysis area for analysis.
The dimension refers to the angle from which we analyze the data.
Example
Dimension field
Dimension
Analyze sales changes in different months/years
Month/year (date type)
time
Analyze the proportion of sales in different provinces/cities
Province/City (text type)
area
Dimension fields include: date type fields, text type fields, indicator names (fields automatically generated when the visualization component is made)
Analyzing data from different dimensions allows us to have a more comprehensive understanding and understanding of the data, and also allows us to make better plans and decisions.
"Indicator" is the quantification of dimensions. Dimensions are about analyzing data from different perspectives. Indicators are the results of analysis from different dimensions. This result can be a numerical value or a ratio.
Indicator
Sales
Proportion of sales in different provinces/cities
Proportion of sales
Indicator fields include: numeric type fields, calculated indicator fields (a field obtained by adding a calculated indicator to the dashboard), and the number of records (a field automatically generated by making a visualization component)
Through the quantification of indicators, we can accurately see the business output, so as to better measure the results of the goal.
Aggregation means that several rows become one row according to a certain standard and summarized into higher-category row-level data.
Indicator aggregation: Indicator aggregation means that all indicators are displayed on the same value axis.
Aggregate function: summarize a set of data. Generally, the recalculation is performed using the aggregated value of the aggregate function.
Indicator aggregation: It is convenient for users to compare the size and trend of different indicators in the same dimension. For details, please refer to: Chart indicator aggregation/parallel
Aggregation function: Different aggregation functions correspond to different aggregation methods. The aggregation methods include "sum, average, median, maximum, minimum, standard deviation, variance, de-duplication counting, and counting". Meet the different needs of users. And as the user analyzes the dimension switch, the calculated field will automatically adjust dynamically with the dimension. For details, see: Overview of Aggregate Functions
Direct connection is to directly connect to the database to fetch data, using the direct connection engine. Direct connection cannot support multiple different data sources to be associated with each other. Once associated, you need to enter the spider engine, that is, local mode.
1) Avoid redundancy of data resources: At present, many companies have more professional big data platforms. By directly connecting the engine to fetch data, data resource redundancy can be avoided while ensuring the performance of data analysis.
2) Satisfy real-time data requirements: the direct connection engine fetches data in real time, and achieves data refresh at the highest millisecond level to meet the user's requirements for real-time data.
For details, see: Introduction to New Direct Connection, Independent Description of Direct Connection and Draw
The data in the local mode needs to be extracted and stored in FineBI's Spider engine, which uses the Spider engine. Subsequent analysis needs to be performed by extracting offline data from the engine.
1) Cost savings: Use local data warehouses to support cross-database fetching, multi-table merging and other functions, saving the cost of enterprise data warehouses.
2) Improve performance: The calculation performance of extracted data is faster, which can meet the rapid analysis of large data volumes, help customers solve data performance problems well, support self-service analysis, and improve users' analysis and viewing experience.
For details, please refer to: independent instructions for direct connection and lottery
The real-time data is in the direct connection mode, and the direct connection engine is used to directly connect to the database to obtain real-time data (the latest data).
characteristic
Changes in real time with the database changes.
For details, see: Introduction to Real-time Data & Extracted Data
Extracting data is in the local mode, using the Spider engine to extract and store data from the database, which can support data for offline use.
After the data is uploaded to FineBI, it will not change with the update of the database. After uploading the data, you need to update the data before you can use it.
It is currently tied to the "data set of data analysis properties".
FineBI usage rights of data analysis users:
1) View the authorized dashboard in the "Directory";
2) Enter "Dashboard" and create a dashboard;
3) Enter under "Data Preparation",
You can create "self-service data set" and "EXCEL data set" in "my self-service data set";
Under the "Business Package" with editing permissions, you can also add "Self-Service Data Set" and "EXCEL Data Set";
The created "self-service data set" defaults to "inherited permissions", which is consistent with the permissions of the basic table used, and only the self-service data set creator and super administrator have the permission to inherit the configuration permissions. For details on permission inheritance configuration, please refer to Permission Inheritance.
For details, see: BI design users
Currently, it is bound to the "Data Set of Data Processing Properties".
FineBI usage rights of data processing users:
Under the "business package" with editing permissions, you can also add "self-service data sets" and "basic tables". Contains all types of basic tables: database tables, SQL data sets and EXCEL data sets;
The nature of the self-service data set created by the data processing user see: Self-service data set (data processing user)
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