In today's information** era, enterprises are faced with the challenge of massive data, and how to gain insights from this data to guide decision-making has become an important issue in the industry. As an information technology, BI business intelligence came into being, which can help enterprises transform massive data into valuable information, and provide intelligent analysis and decision-making, so as to help enterprises operate efficiently and make flexible decisions. This article will delve into the essence of BI and its application in business from three aspects: the concept and development history of BI business intelligence, the core function points and the benefits to enterprises, and recommend several easy-to-use BI tools.
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In 1865, Richard Millar Devens coined the term "business intelligence" (BI) in the Cyclop Dia of Commercial and Business Anecdotes. He used the term to describe the banker Henry FurneseProfit by gathering and analyzing information to act ahead of your competitors.
In 1958, IBM computer scientist Hans Peter Luhn wrote about the potential of business intelligence.
Ten years from now, only those with the expertise will be able to turn data into usable information. At that time, data from multiple ** was often stored in silos, and research reports were fragmented and disjointed, allowing for many different interpretations. Edgar Codd recognizes that this is a serious problem. In 1970, he published an article establishing the "Linked Database Model", which was adopted around the world. The Decision Support System (DSS) was the first database management system. Many historians agree that the modern version of business intelligence evolved from DSS databases.
In the 80s, business people discovered the value of business intelligence, and the number of BI merchants increased dramatically. During that time, a variety of tools were available with the goal of making it easier to access and organize data. Online Analytical Processing (OLAP), Supervisor Information Systems (EIS), and Data Warehouses have emerged to work in tandem with DSS.
In 1988, shortly after the conclusion of the Multiplex Data Analytics Conference in Rome, business intelligence began to emerge as a technical concept. The conclusions reached at the conference prompted a start to simplify BI analytics and make it more user-friendly. BI businesses are popping up in large numbers, and each new company is offering new BI tools. During that period, BI had two essential functions: generating data and providing reports, and organizing and presenting data in an appropriate manner.
At the end of the 20th century and the beginning of the 21st century, BI services began to provide simplified tools and reduce the dependence of decision-makers on tools. These tools are easier to use and provide the features you need. Now,Business intelligence (BII) refers to a technology and tool that helps business managers make more informed decisions by collecting, organizing, analyzing, and mining internal and external data.
Generally speaking, the overall operation process of BI consists of four stages: data preparation, data processing, data analysis, and data sharing.
Data preparation: There is a lot of data, but whether it is in a database or a business system, the first thing is to connect this data to BI.
Data processing: Data is available, but it's still messy and not easy to analyze. The data is processed through functions such as column and column conversion, filtering, group summarization, and left and right merging, leaving only useful data.
Data analysis: After the data is prepared, you can start data analysis, such as sorting, cumulative proportion calculation, outlier highlighting, drilling down to view data of different dimensions, etc., and more complex things such as visual dashboard construction, business modeling and analysis, etc.
Data sharing: When the data is created in the most appropriate way, it can be directly shared and viewed through links or directory permission settings.
In order to run smoothly through the four stages, BI tools need to be designed with at least four or more functional elements, taking FineBI, the product with the highest market share today, as an example, which has six core functionsThey are:Data management, data editing, super functional capabilities, data visualization, sharing and collaboration, and data development.
Data management: Shorter paths and smoother analytics
1) Create an analysis topic
Every node in the data analysis chain, from acquiring data to processing, analyzing, and sharing data, is crucial, and any node blocking or interruption will affect the analysis experience and the final analysis results in the process.
For analysis users, Finebi takes "Analysis Topic" as an analysis unit, integrates each node of the analysis link, and transforms it into three major elements: adding data, component analysis, and dashboard display, and realizes fast switching through tab blocks, streamlining the analysis path, and improving analysis efficiency.
2) Connect to a multi-source database
Relational databases: MySQL, Oracle, SqlServer, DB2, Sybase, Informix and other mainstream relational databases. SQL fetching data tables or views, as well as stored procedures;
Multi-dimensional databases: Essbase, SSAS, SAP BW, Hadoop, etc.
Non-relational databases: non-relational data such as MongoDB is supported.
File data source: data from excel files, txt files, csv, xml files;
Other data sources: program data sources, JSON data, SAP data sources, etc.
3) Create a data space
Separate public data and personal analytics into two modules to better manage your enterprise's data resources and users' personal data.
As the name suggests, public data is a place where data for public use is stored in a unified manner. Tables with a high degree of reuse can be added or published to a public dataset within the company. Under the space for such public data, you can add:
Excel Dataset: Upload the local Excel sheet to BI;
SQL dataset: You can write simple SQL statements in FineBI to add data to BI after simple processing of the data in the database.
Database table: Add the entire table in the database to BI without processing.
Published self-service datasets: Users can publish self-service datasets from topics to public data for other users to use.
At the same time, the public data space also supports the publication of the results of personal analysis into 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.
Data editing: Working with data is as easy and easy to use as excel
1) The data table is screened and replaced
Sorting, filtering, changing field names, etc., direct header operations, and multi-selection with shift and ctrl keys are also supported!
Add columns, row and column conversions from other tables, and get it into the desired format in just a few steps.
2) Fast data verification
Count the number of rows, deduplicate, sum, and average. Verify while analyzing, find problems in time, and quickly adjust calculations.
3) Support backtracking and adding analysis steps
Record every step of the data analysis, add notes, retrospectively modify, and insert new steps to reduce rework costs.
Powerful function capability: Calculate arbitrary complex logical indicators
Regular functions
Logical functions such as if, and, switch, or, etc. Mathematics and trigonometric functions such as min, max, rand, etc.; Date functions, such as month, year, and todate; Text functions, such as subtitute, format, left, etc.
You can select the desired function based on the field type or calculation logic. 
2) Aggregate functions
Different aggregate functions correspond to different aggregation methods, including sum, average, median, maximum, minimum, standard deviation, variance, deduplication count, count. As the user analytics dimension is switched, the calculated fields are automatically adjusted dynamically with the dimension.
3) def function
Analytical functions include def, def-add, def-sub, and earlier. After combining the original basic functions, the analysis function can realize the calculation indicators of any level and any complexity based on the limited data output, covering more complex business scenarios and solving the problem of user solution implementation.
Data Visualization: Problem Insights, One Step Ahead
1) Visualization charts
Some high-level graphics, such as box plots and Sankey charts, can't be drawn by Tableau without learning for a year and a half, because there is no one in its graphical interface, but Finebi has done a good job in this regard, lowering the threshold for high-level graphics.
2) Dashboard design
Data linkage between analysis components is supported.
Support clicking data to realize the dashboard jump function.
You can set the style of the title and the filter component function to hide the title.
Provides an easy-to-use adaptive layout that automatically adapts to the size and resolution of the screen.
Share and collaborate: teams work together to achieve maximum output
The benefits of the collaborative analytics feature of BI tools are:The same problem or business topic can be edited and analyzed by multiple people, which can greatly improve the efficiency of collaborative work.
In the BI tool, you can collaborate on a folder for data analysis projects that your team is working on
You can also collaborate on a topic analysis and work with team members to output and maintain a data analysis report, such as a year-end summary report
Data development: Provide high-quality data for business analytics
For IT or data teams, it provides a more professional data development module, and pre-processes data through a flexible ETL data development and task engine, providing higher-quality data for upper-layer applications that is more conducive to display and analysis.
ELT and ETL dual-core engines: flexibly cover data processing-related scenarios to maximize performance.
Drag-and-drop process development: Drag-and-drop rapid development is realized, the construction process is clearer, and the task development is more efficient.
High-performance computing: The scheduled synchronization engine and offline computing engine implement high-performance data processing with the most lightweight architecture.
Unstructured data processing capability: It can realize the processing of unstructured data such as API, XML, and JSON.
Real-time batch synchronization of multiple tables, API management, and more.
Visualization of existing data
Often, a company's data is complex and cannot be identified at a glance. BI, on the other hand, is to visualize the data generated in the daily operation of the enterprise or the pre-made reports in the form of bar charts, line charts, funnel charts, etc., so that business personnel can identify important information. In addition, through drilling, linkage, jump and other functions, you can further view further information according to the indicator dimension and find the root cause of the problem.
Monitoring and early warning of current data
Normally, we will detect "anomalies" by color changes or warning line settings. When business personnel find data anomalies from visual charts, they need to analyze them purposefully and find possible problems through checking related reports and drilling through reports of different dimensions. Finally, business personnel can also build a more reliable and solidified analysis model through one or more dimensional and indicator charts. The business people at this stage are no longer passively accepting the information reflected in the charts, but passing"Exceptions"Data to locate a business problem behind it, data and business at this level began to have a direct correspondence, and use the logical relationship between data charts to find solutions to improve the operational efficiency of the enterprise.
The science of the future of business**
*The future business is usually realized through modeling and analysis, and business personnel who are proficient in business changes can find out potential problems in the business or achieve better adjustment methods by making appropriate visual models, and then feed back business decisions to form a benign process. Business modeling is more autonomous and exploratory, which can maximize the role of BI.
Finebi: Finebi is a powerful domestic business intelligence tool that provides rich data visualization and analysis functions, can help users quickly discover business insights from data, and supports the connection and integration of multiple data sources, making data analysis more efficient and accurate.
Tableau: Tableau is a leading visual analytics tool with an intuitive user interface and rich charting options that transform complex data into easy-to-understand visualizations that help users better understand the story behind the data, enabling real-time data analysis and interactive reporting.
PowerBI: PowerBI is a business intelligence tool launched by Microsoft, with powerful data integration and analysis capabilities, which can quickly transform data into insightful reports and dashboards, support self-service data analysis and team collaboration, and is an important auxiliary tool for enterprise decision-making.
IBM Cognos: IBM Cognos is IBM's business intelligence and enterprise performance management platform, which provides comprehensive data analysis and reporting capabilities, supports multi-dimensional data analysis and modeling, and helps business managers better monitor business conditions and make informed decisions.
Qlik: Qlik is a business intelligence tool based on a correlated data model, with powerful data discovery and exploration capabilities, which can quickly discover correlations and hidden patterns between data, and help users conduct in-depth data analysis and insights.
Domo: Domo is a cloud-based business intelligence platform that integrates data integration, visualization, analysis and other functions, supports the connection of multiple data sources and real-time data monitoring, and provides a simple and intuitive user interface to make data analysis easier and more efficient.
Spotfire: Spotfire is a business intelligence tool launched by Tibco, which has advanced data visualization and analysis functions, supports large-scale data processing and real-time data analysis, can help users quickly discover key information from massive data, and supports intelligent data exploration and analysis.
To sum up, BI business intelligence, as a powerful data analysis tool, has become an important support for today's enterprise decision-making. By digging deeper into the potential of data, BI not only provides enterprises with the wisdom of insight and decision-making, but also provides a strong guarantee for enterprises to win opportunities in the fierce market competition. It is believed that with the continuous advancement of science and technology, BI business intelligence will play a more important role in the future to help enterprises achieve more sustainable development.
Hopefully, this article will help you and your business understand BI and introduce BI!