There is actually a reason why business intelligence BI can become a popular data technology solution in the current business world. As early as 1958, IBM researchers defined the early form of business intelligence BI as: "a comprehensive analysis of the relationship between things. This ability to understand, and rely on this ability to guide decision-making, in order to achieve the desired goal.
After 1958, the concept and product form of business intelligence BI have been updated and iterated. Until 2013, under the influence of informatization and digitization, business intelligence BI formed a set of modern concepts, expanded around enterprise development, and redefined Definition of Business Intelligence BI: "Business Intelligence BI is an umbrella term. It includes applications, infrastructure, tools, and best practices for providing information access and analysis to improve and optimize decision performance"
About the definition of business intelligence BI
After decades of development of business intelligence BI, we have a clear definition of mainstream business intelligence BI products in the current environment. There are three types of products, namely:
First, business intelligence BI is a complete set of data-based technical solutions consisting of data warehouses, query reports, and data analysis .
Second, business intelligence BI can open up and effectively integrate data in different business information systems (ERP, CRM, OA) of an enterprise .
Third, business intelligence BI can quickly and accurately provide visual analysis or reports with the help of appropriate query and analysis tools, and provide decision support for enterprises .
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A complete set of business intelligence BI solutions, including many different functional modules, can enable enterprises to achieve various effects. For example, business intelligence BI can be divided into three levels according to different processes of enterprise business data:
The first layer, the visual analysis presentation layer - the visual analysis presentation layer is also the demand layer of business intelligence BI. On the one hand, it represents the needs of users, what users want to see, what they want to see, and on the other hand, it also represents what users want to analyze. These are displayed at this level.
The second layer, data model layer - the data model layer is often called the business intelligence BI data warehouse, which is mainly responsible for the analysis model of enterprise data and completes the transformation from business calculation rules to data calculation rules.
The third layer, data source layer - the data source layer is also the data layer of business intelligence BI, the business information system of different departments and business lines, the data of the underlying database is extracted into the data warehouse of business intelligence BI through ETL, modeling and analysis And so on, finally supported to the front-end visual analysis display.
In the future, the development trend of business intelligence will be affected by the following aspects:
- Increased importance of data-driven decision-making: With the rapid growth of enterprise data and technological advancement, data-driven decision-making will become increasingly important. Business intelligence will continue to play a key role in helping companies extract valuable insights from massive amounts of data to support decision making.
- Integration of artificial intelligence and machine learning: Artificial intelligence (AI) and machine learning (ML) will become important components of business intelligence. By leveraging AI and ML techniques, business intelligence tools can automatically analyze large volumes of data and generate predictions, make recommendations, and uncover hidden patterns and trends.
- Improvements in visualization and user experience: Business intelligence tools will focus more on user experience and visualization. Data visualization will become more intuitive, interactive, and easy to understand. Users can explore and analyze data through an intuitive interface to better understand business situations.
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4. Popularity of self-service analysis: Self-service analysis tools will become mainstream. These tools allow non-technical people to conduct data analysis using a simple interface without relying on data scientists or technical experts. Self-service analytics tools enable more employees to leverage data for decision support.
5. Processing of real-time and streaming data: With the popularization of Internet of Things (IoT) and sensor technology, more and more data is generated in real-time or streaming form. Business intelligence tools will need to accommodate this data type and be able to process and analyze the data in real time to provide immediate insights.
6. Wide application of cloud infrastructure: The development of cloud computing technology will promote the development of business intelligence tools. Cloud infrastructure can provide elastic scale and flexibility, enabling enterprises to rapidly expand business intelligence solutions as needed, while reducing cost and complexity.
7. Emphasis on data security and privacy protection: With the increase of data leakage and privacy violation incidents, data security and privacy protection will become an important focus of business intelligence. Business intelligence tools will need to provide strong security features to ensure data is protected during collection, storage and analysis.
To sum up, business intelligence will continue to develop in the future, utilizing advanced technologies such as artificial intelligence, machine learning and cloud computing to provide more powerful, intelligent and user-friendly analysis and decision support functions. At the same time, data security and privacy protection will also become important issues that cannot be ignored.