Hello, ladies and gentlemen! Starting today, we will update the detailed articles about the functions of BI (Business Intelligence) tools, which are divided into seven articles: data management, data editing, super function capabilities, data visualization, sharing and collaboration, data development, and operation and maintenance platform. Each article will contain three parts: functional definition, content and specific operation process, after reading these seven articles, you will not only be able to have a comprehensive knowledge and understanding of BI tools, but also have the opportunity to get started by yourself!
Let's start with the first part of the day - data management:
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At the beginning of all articles, we will explain the data analysis terms that will be used in the future, so that you can understand:
1.What is Data Management?
Data management refers to the process of collecting, acquiring, and managing raw data. In the data management phase, data analysts or researchers manage data from a variety of sources, which can include various databases, files, sensors, questionnaires, social networks, and more.
The purpose of data management is to manage enough data to support subsequent analysis and decision-making.
Data management is the foundation of data analysis, and high-quality data management can provide a reliable data foundation to support accurate analysis and decision-making. Therefore, in the data management phase, it is necessary to ensure that the collected data is of high quality, the information is accurate, and the bias and error of the data are minimized.
2.What is an analytics topic?
The analytics topic is a core element of your data analysis and visual presentation in BI. When you need to conduct data analysis, you can create an analysis topic and conduct your own business analysis in it, and the analysis topic supports data processing, visualization charts and dashboards. At the same time, the analysis theme supports collaborative editing between different users, which greatly facilitates the sharing of analysis content by users.
3.What is a multi-table association?
Multi-table association refers to the followingIn a relational database, the process of joining multiple tables with common fields to obtain related data. In a multi-table association, there is usually a master table and one or more slave tables. The master table contains the primary information, while the slave table contains the secondary information related to the master table.
The process of multi-table association is based on matching common fields between two or more tables. These common fields are used to establish associations, so that combinations of data from multiple tables can be obtained through query statements.
In multi-table associations, common types of associations include inner joins, outer joins, and cross-joins. An inner join returns only rows that match the criteria based on the join criteria, an outer join includes rows that match and those that do not, and a cross join returns the Cartesian product of the two tables.
Through multi-table association, complex data query and analysis requirements can be realized, and related data scattered in multiple tables can be integrated together to provide more comprehensive information.
OK, after explaining the relevant concepts that need to be used in this article, let's start to explain to you, what exactly does data management consist of?
1.Data access: Multi-source data integration solves data silos
It supports more than 30 big data platforms and SQL data sources. This includes common database management systems (such as MySQL, Oracle, SQL Server, etc.) and other popular big data platforms (such as Hadoop and Spark).
Excel files are supported as datasets.
With finereport, richer data sources can be supported, including multidimensional databases and program datasets. A cube database is a database used to store and analyze cubes, which can provide more flexible and efficient data query and analysis functions. A program dataset is a dataset obtained through an interface provided by a programming language, which can be used for data interaction and integration with various external systems.
2.Data space: Smooth links create an immersive analysis module
Separate public data and personal analytics into two modules to better manage your enterprise's data resources and users' personal data.
Public data is a shared data resource for an enterprise that can be used by multiple users for analysis and reporting. Users can perform data analysis and visualization operations on public datasets as needed.
On the other hand, personal profiling is the user's personal data analysis space, where the user can upload or import personal data into the Finebi system for analysis and processing. Users can create their own datasets, apply various analysis functions and algorithms, and generate personal reports in the personal analysis area.
It also supports publishing the results of individual analyses to public data. In this way, the valuable analysis results obtained by users in the process of personal analysis can be shared with other users, further enriching public data resources.
By separating public data from personal analytics, it can better meet the different requirements of enterprises and individuals in terms of data resource management and analysis needs.
3.Data classification: Distribute data to users in a controlled manner
Data management is carried out in the form of folders, allowing users to classify and organize data according to different business package topics.
Users can create folders and place related datasets, reports, and other resources in those folders. In this way, users can classify and organize data into different themes according to their own needs and business logic for easy finding and management.
For example, a user can create a folder of sales data and place sales-related datasets and reports under that folder; Or create a folder of financial data under which you can place datasets and reports related to financial analysis. In this way, users can quickly find the data and reports they need according to the business package topic, improving work efficiency and data management convenience.
Data management in the form of folders provides an intuitive and flexible way to organize and categorize data, enabling users to better manage and utilize data resources.
4.Data association: All resources, team level, maximize the value of data
The association modeling function is provided, which can automatically model the relationship between the data warehouse.
Administrators only need to select the required data tables and fields, and then they can automatically identify the relationship between these tables and build the corresponding relationship model.
Manual association configuration is also supported to meet more complex modeling needs. Administrators can manually specify associations between different data tables and configure the combination of primary keys. In this way, administrators have more precise control over the data model creation process to meet different analysis and reporting needs.
1.Create a new folder
In My Analysis, users can create a new folder to classify and manage their own analysis topics, and the new folder has two entrances, as shown in the following figure:
A: Click the big New Folder button;
b: Click +> folder of a folder to create another folder under that file.
2.Create a new analysis topic
There are three entries for a new analysis topic:
Select the data you want to analyze, click the Create Analysis Topic button in the upper right corner, you can rename the name of the analysis topic and select the location where the analysis topic is placed, as shown in the following figure
Click +> Analysis Topics in a folder to create an Analysis Theme under that file. As shown in the figure below:
Click the large New Analysis Topic button as shown in the image below:
3.Add data
After you create an analysis topic, you will automatically enter the Add Data page in the analysis topic, or you can click Add Upload Data.
Select a local excel file to upload data. As shown in the figure below:
In today's information** era, the importance of data management is becoming more and more prominent. Whether it's an individual or an organization, properly managing data has become the key to success. Through proper data collection, collation, and analysis, we are able to better understand customer needs, market trends, and even change business models. At the same time, data management also helps to strengthen privacy protection and ensure the security of personal information. Therefore, we should pay attention to data management and continuously learn and apply relevant technologies to adapt to the challenges of this digital era. Only by harnessing the power of data can we stand out in a competitive environment and continue to innovate and grow.
Thank you for reading and support, the first article of the BI tool function inventory is over here, if you still have questions about data management, please feel free to leave a message in the comment area or background private message, we will get in touch with you in time!
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