White Paper on Business Intelligence defines BI tools for business intelligence as software tools with data visualization and analysis technologies and certain data connection and processing capabilities. Users can quickly make various types of data reports and graphic charts through a visual interface.

The report type BI

Reporting BI tool is mainly aimed at IT personnel in the enterprise information department. IT is applicable to report design of various fixed styles. IT is usually used to present detailed business data and indicator summary, and supports relatively small amount of data.

Domestic reporting BI started around 1999 and became mature in 2013.

Because domestic enterprises have their own school for the format of statements, many foreign statement tools in the production of statement style, graphic format interaction are difficult to run in, and the business logic of some forms is different from that of foreign countries, so to solve the Chinese complex statements often become the key needs of enterprise selection. At present, domestic reporting tools such as FineReport have become the mainstream.

Report BI mostly adopts the design mode like Excel. Although IT is mainly targeted at IT departments, business personnel can learn and master IT quickly and make some basic data reports and cockpit reports within the established scope of data authority.

For example, the HTML5 chart independently developed by FineReport can meet the visual display needs of different groups, and can also carry out some simple AD hoc analysis operations, such as chart type switching, sorting, filtering, etc.

Representative tools:FineReport

Traditional BI

Traditional BI is also for IT personnel. With the development of data warehouse technology, IT focuses more on OLAP impromptu analysis and data visualization analysis compared with report BI.

Traditional BI is represented by Cognos and other foreign products. Its advantages are performance and stability in large data volume, while its disadvantages are also obvious: data analysis ability and response speed to business are poor. Nowadays, more than 83% of the data analysis needs of enterprises or institutions using traditional BI cannot be met, and the BI system built by many enterprises with heavy investment is almost useless with little effect.

In addition, due to its heavy structure, high project cost, extremely long implementation cycle, high project risk and high talent requirements, it is not conducive to the promotion and popularity of traditional BI.

Represents the tool: Cognos

Self-service BI

As traditional BI has been criticized for its shortcomings and business people’s demand for data analysis has increased, self-service BI has begun to grow rapidly. Self-service BI is oriented to business personnel and pursues efficient cooperation between business and IT, so that IT personnel can return to the technical standard and do a good job in data bottom support.

Let business personnel return to the value standard, through simple and easy to use front-end analysis tools, based on business understanding to easily carry out self-service analysis, explore the value of data, to achieve data-driven business development.

Since 2014, self-service BI tools have witnessed rapid development, visual data analysis and self-BI have appeared in the domestic market, and traditional BI has begun to decline. It should be noted that self-service BI also has its application scope, and enterprises should consider their own needs and characteristics of self-service BI comprehensively when choosing. Self-service BI has the following main advantages:

• Flexibility of data volume. Although traditional BI tools have good performance in processing large amounts of data, they are cumbersome in some enterprises with small amounts of data. They have simpler ideas but cannot use simpler processing methods. Self-service BI is more flexible and has the ability to process large amounts of data, making analysis easier in the face of small amounts of data.

• The cost of product procurement fell. The cost of purchasing traditional BI tools is high, and there are additional training, service and consulting costs. Self-service BI product tools only focus on solving certain problems, and do not need to be large and comprehensive.

• Shorten project cycle and reduce labor cost. The previous project cycle was mainly consumed in ETL processing, data warehouse modeling, performance optimization, etc. Today, modeling is less demanding, and performance optimization is no longer an issue in most scenarios. Project cycles were rapidly reduced from the previous months or years to days, weeks, and months.

• IT is gradually becoming business driven. IT is responsible for organizing the basic data architecture and maintaining the open interface. Business personnel conduct rapid visual analysis and report analysis and maintenance by themselves.

All in all, self-service BI will be a wise choice when there is a need for business people to independently analyze, solve key concerns, flexibly deal with small data volume business, and fast iterative project cycle.

Representative tools:FineBI

Finally, it should be noted that the three TYPES of BI products are respectively applicable to different scenarios and are not substitutes for each other. They will coexist for a long time, for enterprises to choose on demand, until the basic conditions of informatization change fundamentally.

Business intelligence ≠ data analytics

Business intelligence and data analytics are two confusing concepts. Although there are many similarities, and business intelligence software can help business people with data analysis, data analysis is not the same as business intelligence.

Data analysis is a process, a solution, and often a problem. For example, to analyze the effect of a promotional activity, it is necessary to monitor UV, customer unit price, repurchase rate and other key index data.

It also needs to compare with past activities, find the best control group from the database for modeling, and do statistical analysis in SAS. In other words, data analysis is to use scientific methods such as mathematical statistics to verify hypotheses. The usual work is to analyze and compare indicators, MONITOR KPI, analyze abnormal indicators, predict trends, and finally generate result reports. Professional data analysis tools include R, Python, etc.

Business intelligence is a set of solutions, often to an enterprise’s operational problems. Take advantage of the large amount of data produced in the daily operation of enterprises, and transform them into information and knowledge, so that every decision, management details, strategic planning have data reference.

For example, leaders often pay attention to sales, procurement and financial status. Technical staff prepare fixed format data reports (Dashboard/ Data Kanboard), which can be viewed by leaders when opened, and the data can be updated automatically.

Business intelligence tools connect data of service systems, such as ERP, CRM, and MES, and regularly summarize the data into a data warehouse to produce analysis reports related to business topics. They can also connect to big data platforms for visual analysis and display.

On the one hand, business intelligence solidifies and simplifies the conventional analysis process; on the other hand, it makes self-service analysis of business more convenient and fast. To put it simply, BI is a set of solutions related to data. The entrance is data, and the exit is data or data-based report presentation, with more emphasis on solutions.

Data analysis is more human-oriented, the process of analyzing data produced by data warehouse or other channels. The former emphasizes how to make data reasonably processed or presented, while the latter emphasizes how to find problems through data. There is a process of exploration and thinking, which cannot be replaced by tools themselves.