1. Summary
DataFocus and FineBI are both business intelligence (BI) products designed to help businesses make decisions with data. DataFocus emphasizes its next-generation, search-based BI approach, focusing on ease of use and fast dashboard creation, especially for business users. FineBI focuses on its self-service BI capabilities, powerful data preparation and modeling features, and is attractive to business users and data analysts. This report aims to make an objective comparison of these two products and provide a basis for selection for different user groups.
2. Introduction
In today's era, data plays an increasingly important role in organizational decision-making. To effectively utilize this data, businesses need powerful BI tools to analyze, visualize, and understand complex information. Choosing the right BI tool is critical to improving efficiency, discovering insights, and ultimately achieving business goals. DataFocus and FineBI are two of the leading BI products on the market, both of which provide a wealth of features to meet a variety of data analytics needs. This report aims to make an objective comparison of DataFocus and FineBI, and analyze their core functions and features in detail to help organizations choose the most appropriate platform based on their specific needs.
3. DataFocus Product Overview
3.1 Core functions and unique selling points
At the heart of DataFocus is its search-based BI engine, which makes it a leader in the next generation of BI solutions. The main interactive method of the platform is through natural language search, where users can ask questions directly and obtain data analysis results without complex drag and drop operations or writing code. This approach significantly lowers the barriers for non-technical business users to use BI tools, allowing them to explore data and gain insights as easily as they use search engines. DataFocus's Focus Search engine is the core technology that implements this capability, which can transform users' natural language input into executable SQL query statements. What makes Focus Search unique is its transparent two-step generation process (keyword + SQL), which unlike some black box LLM frameworks, provides higher controllability and verifiability of results.
The platform emphasizes fast dashboard development, claiming to be able to produce large visual screens in just 7 minutes. This efficiency is thanks to its rich template library and intuitive user interface, allowing users to quickly build beautiful and personalized data analysis reports and dashboards. DataFocus also provides an integrated data warehouse (Lakehouse) as well as ELT (extract, load, convert) functions and data connectors. This integrated platform simplifies the data analysis process and reduces dependence on multiple independent tools.
3.2 Data connection capability
DataFocus supports multiple data sources and flexible connection methods. Users can import data by uploading files such as Excel, CSV, TXT, and JSON. In addition, the platform also supports direct connection to mainstream databases such as MySQL and Oracle. For more complex data integration needs, DataFocus provides a customized API interface that allows users to connect to external systems and access a wider range of data. The platform also supports direct database connection and data import options, where users can choose real-time connections based on their needs to obtain the latest data, or import data for faster analysis. DataSpring is another key component of DataFocus. It is an integrated streaming batch ETL platform that supports CDC (change data capture) and can realize real-time data synchronization and processing. DataFocus also has the ability to connect multiple databases simultaneously and perform cross-library table connection and analysis in real-time state.
3.3 Data processing function
DataFocus provides comprehensive capabilities in data processing. The platform has a visual ETL workflow, and users can quickly build data processing processes and set timing tasks through drag and drop operations. Data cleaning and conversion features include formula conversion and custom UDF (user-defined function) operators, allowing users to convert data using Excel-like formulas and support complex logical processing based on Python code. DataFocus's integrated data warehouse provides powerful support for data aggregation, standardization and governance, including features such as metadata management and data blood management. In addition, the platform also provides Intelligent Insight functionality, which can automatically discover common patterns and outliers in the data, thereby reducing repetitive exploratory work. DataFocus supports the creation of intermediate tables, including the creation of views using SQL statements for more complex data modeling and transformations. The platform also has the ability to build various data analysis models and indicator systems.
3.4 Visual analysis function
DataFocus offers over 50 chart types, including timing charts and dynamic charts. The platform adopts adaptive chart visualization technology, which can automatically select the most appropriate chart type based on data. Users can create highly customizable dashboards, and the platform supports a variety of layouts and interactive objects. DataFocus also supports large screen displays up to 8K resolution and can display a large number of charts on a single screen. In addition, the platform also provides rich geographic information display functions and supports various GIS maps.
3.5 Report production features
DataFocus allows users to design personalized data analytics reports and large visual dashboards. The platform has the function of generating analysis reports in natural language, and can automatically generate descriptions and explanations based on data analysis results. DataFocus supports multi-screen projection and mobile terminal viewing, including integration with platforms such as DingTalk, Enterprise WeChat and Feishu. The created visual screen can be shared via links or QR codes and supports setting access controls. The platform also provides granular data permission control, accurate to table, row and column levels.
4. FineBI Product Overview
4.1 Core functions and unique selling points
FineBI is a self-service BI tool designed for fast and big data analytics. The platform provides an intuitive drag-and-drop interface where users can create dashboards and charts without coding. FineBI has a powerful data processing engine and excellent chart rendering mechanism, which can process large amounts of data. Its unique selling points include the powerful self-service data set function, which allows users to flexibly prepare data, including adding columns, grouping summary, filtering, sorting, merging and other operations. FineBI also provides data modeling capabilities, so users can easily model data based on relationships in data warehouses. In addition, the FineBI.0 version introduces a self-developed DEF function, which can combine basic functions to achieve any complex calculation indicators that users want. FineBI also seamlessly integrates mobile applications, optimized for mobile device operations9.
4.2 Data connection capability
FineBI supports connecting to more than 30 big data platforms and SQL data sources, and allows the use of Excel file datasets. With FineReport Designer, users can even access more diverse data sources, such as multidimensional databases and program datasets. The platform supports direct connection to various databases, including MySQL, PostgreSQL, SQL Server, Oracle, and Hadoop Hive. FineBI also supports JDBC and JNDI connections 9. The platform provides two modules: public data and my analysis to store data, which is convenient for user management and use.
4.3 Data processing function
FineBI provides powerful data processing capabilities. Users can use self-service data sets to perform flexible data processing, including adding new columns, grouping summary, filtering, sorting, up and down merge, left and right merge, etc. The platform provides a wealth of functions for data calculations, including logical functions, mathematical and trigonometric functions, date functions and text functions. In terms of advanced computing, FineBI provides aggregate functions and DEF functions to implement complex computational metrics. FineBI also has data modeling functions, so administrators can easily model data based on relationships in the data warehouse, and support manual configuration linkage, including composite primary key linkage configuration. FineBI supports the Spider engine, providing real-time data and extracted data modes. Users can freely choose 7 based on data volume, real-time requirements and usage frequency.
4.4 Visual analysis function
FineBI designs visual analysis logic based on graph grammar and provides unlimited visual analysis options, including rich chart types and attribute mappings. The platform offers over 50 chart styles and visual components, as well as over 00 preset dashboard templates. FineBI is able to intelligently recommend the best chart types and supports simple creation of visual dashboards 4 through drag and drop. The platform also supports multi-dimensional exploratory analysis, which can automatically identify dimensions and indicators, and provides rich analysis functions, such as drilling, summarizing, regrouping, computing proportions, etc.
4.5 Report production features
FineBI supports the creation of multiple types of reports, including schedules, grouped tables, and crosstabs, and supports list display and tree display. Users can easily create dashboards in various styles, and the platform provides a variety of dashboard styles and color schemes. FineBI provides an easy-to-use adaptive layout that automatically adapts to screen size and resolution, allowing reports or dashboards to be perfectly presented on PCs, mobile terminals and large screens. The platform supports data linkage between analysis components and clicking data to achieve dashboard switching. FineBI also provides a variety of dashboard components, such as web components, that can be embedded in FineReport reports.
5. Comparative analysis
5.1 Data connection
DataFocus and FineBI both provide comprehensive capabilities in data connectivity. DataFocus supports file uploads, direct database connections, data warehouse uploads, and API connections. Its DataSpring provides real-time data synchronization. FineBI supports connecting to more than 30 big data platforms and SQL data sources, as well as Excel files, multidimensional databases, and program datasets. FineBI supports direct connections to a variety of databases including MySQL, PostgreSQL, SQL Server, and Oracle, and provides real-time and extracted data processing6.
| Data source type | DataFocus | FineBI |
|---|---|---|
| database | MySQL, Oracle, PostgreSQL, SQL Server, Impala, ClickHouse, Presto, Trino | More than 30 types, including MySQL, PostgreSQL, SQL Server, Oracle, Hadoop Hive, etc. |
| File format | Excel, CSV, TXT, JSON | Excel |
| Big Data Platform | Supported via DataSpring | More than 30 types |
| Cloud Service | Supports data cloud storage | support |
| other | Custom API interface | Multidimensional database, program dataset (via FineReport Designer), JDBC, JNDI9 |
5.2 Data processing
DataFocus provides visual ETL workflows, data cleaning transformations, integrated data warehouses, smart insights, and intermediate table support. FineBI provides self-service data preparation, advanced computational functions (including DEF functions), and data modeling capabilities. FineBI's self-service dataset capabilities seem to be broader, while DataFocus emphasizes its integrated cloud-native data warehouse and automation intelligence insights.
5.3 Visual analysis
Both DataFocus and FineBI offer over 50 chart types. DataFocus features adaptive charting and support for large-screen displays. FineBI emphasizes its smart chart recommendations and interactive dashboard capabilities.
5.4 Report production
DataFocus supports personalized report design, natural language generation reports and mobile viewing. FineBI provides flexible report design, multiple data display methods and mobile access.
5.5 Unique Features
DataFocus’s unique capabilities are its search-based BI and natural language query capabilities, as well as fast dashboard development capabilities and integrated cloud-native data warehouses. The advantages of FineBI are its comprehensive self-service data preparation capabilities, powerful data modeling capabilities, DEF functions for advanced computing, and seamless integration with mobile applications9.
6. User reviews and case analysis
DataFocus user reviews mention improved service center response time and benefits for educational institutions (Tsinghua University). Case analysis highlights its applications in smart cities, the Internet of Things and a variety of industries. Comparisons with competitors such as ThoughtSpot, QuickSight and Domo show that DataFocus has advantages in terms of ease of use, customization, and value.
FineBI's user reviews on Gartner emphasize their flexibility, ease of use of business users (especially when it comes to using self-service data sets), and their powerful data visualization capabilities. Case study demonstrates its application in retail, manufacturing and various business functions.
7. Suggestions for selection of different user needs
7.1 Data Analyst
- DataFocus:It may be more suitable for data analysts who need to quickly explore data and build dashboards using natural language queries, especially when using predefined data warehouses. Custom UDF operators provide some advanced processing flexibility.
- FineBI:With self-service data sets and data modeling capabilities, more comprehensive data preparation and modeling capabilities are provided. Advanced computational functions, including DEF functions, provide powerful tools for in-depth analysis.
Data analysts who have a strong technical background may prefer FineBI's advanced data manipulation and modeling capabilities; while analysts who need to quickly generate insights and visualizations may find DataFocus' search-based approach more effective.
7.2 Business Users
- DataFocus:Search-based interface and fast dashboard creation make it ideal for business users who need to access and analyze data but do not have a wide range of technical skills.
- FineBI:Intuitive drag-and-drop interface and self-service data preparation enable business users to perform data analysis and create dashboards independently without relying on IT departments.
Both platforms are very suitable for business users. DataFocus's natural language query may be easier for users who are not very familiar with traditional BI tools, while FineBI's self-service data preparation provides more data control.
7.3 IT Administrator
- DataFocus:Integrated data warehouse and fine-grained permission control provide advantages for data governance and security. Cloud Native Architecture simplifies deployment.
- FineBI:Enterprise-level authorization and manageability capabilities, as well as high concurrency and high availability9, are beneficial to IT administrators. Supports various deployment models and integrates with other systems.
IT administrators may value the strong security and scalability provided by both platforms. The cloud-native nature of DataFocus may simplify deployment, while FineBI's integration capabilities and performance analytics plug-ins may be more valuable for managing large BI environments.
8. Conclusion
DataFocus and FineBI are both powerful BI platforms, each with unique advantages. DataFocus stands out for its search-based BI, ease of use and speed, especially for business users who need to gain insights quickly. FineBI excels in self-service data preparation, data modeling and advanced analytics, making it more suitable for data analysts who need more granular control over their data.
The final choice should depend on the specific needs of the organization, user skill level, data complexity, and specific analytical needs. DataFocus may be a better choice for organizations that prioritize ease of use of business users. For organizations that require more advanced data manipulation and modeling capabilities, FineBI may be more suitable. It is recommended that companies fully evaluate their needs and conduct product trials before making a final decision.