The evolution of Business Intelligence (BI) has journeyed through various stages to meet the burgeoning demands of data insights in enterprises. Here’s a formatted look into the three developmental phases of BI application functionalities:
Phase One: Reporting BI
In the formative years of BI, its primary role was to conduct business statistics and analysis, offering managers fixed reports. Users found it challenging to alter the content and format of these reports, leading to delays in information retrieval and insufficient analytical flexibility.
Phase Two: Self-Service BI
As the demand for data analysis grew within enterprises, self-service BI tools emerged. These tools empowered business personnel to access and analyze data independently, lowering the barrier to entry for data analytics and enhancing efficiency. However, self-service BI still required IT professionals to construct models, and it sometimes fell short in handling complex tasks.
Phase Three: Enhanced Analytics
The advent of AI has revitalized BI, with enhanced analytics leveraging AI for automatic data analysis and interpretation. Two notable branches include:
– Data Mining Analysis: Utilizing AI technology for business analysis and forecasting.
– Intelligent BI: Adopting natural language dialogue for data analysis, democratizing the process.
Below is the detailed content of Intelligent BI:
Intelligent BI: From Self-Service to Autonomy
The application of Large Language Models (LLM) has given rise to the concept of ChatBI. ChatBI, based on AI Generated Content (AIGC) applications of large models, facilitates the entire process from natural language input to data query, output, and interpretation.
Challenges include the precision of NL2SQL, SQL matching with data models, Python code generation, data interpretation, and data security/permissions, as well as deployment issues. Here’s how it’s being addressed:
Companies and BI vendors are tackling these issues, for instance, by adopting an improved version of NL2DSL to generate semantic SQL pseudo-code, solving the precision and data model matching problems.
Intelligent BI further reduces the threshold for analysis, enabling employees to “analyze autonomously.” In the future, Intelligent BI could integrate with other systems, achieving seamless data flow.
Looking Ahead: Decision Enhancement and Business Automation
Enterprises are integrating AI capabilities, such as adding attribution analysis and business forecasting. Data analysis is evolving from information presentation to decision enhancement.
The development of AI in the data analytics field will promote BI democratization and autonomous decision-making, leading to business automation. Below is the core content abbreviation:
BI evolution is now in the enhanced analytics phase, with Intelligent BI enabling data analysis through natural language dialogue, lowering barriers. Challenges include technical matching and data security. The future of BI lies in decision enhancement and business automation.
以下是 the content formatted for wordpress:
The evolution of Business Intelligence (BI) has journeyed through various stages to meet the burgeoning demands of data insights in enterprises. Here’s a look into the three developmental phases of BI application functionalities:
Phase One: Reporting BI
In the formative years of BI, its primary role was to conduct business statistics and analysis, offering managers fixed reports. Users found it challenging to alter the content and format, leading to delays and insufficient flexibility.
Phase Two: Self-Service BI
As the demand for data analysis grew, self-service BI tools emerged, empowering business personnel to access and analyze data independently, enhancing efficiency but still requiring IT support for models.
Phase Three: Enhanced Analytics
AI has revitalized BI, with enhanced analytics leveraging AI for automatic data analysis. Two notable branches include data mining and intelligent BI.
Intelligent BI: From Self-Service to Autonomy
ChatBI, based on AI Generated Content, facilitates the entire process from natural language input to data interpretation. Challenges are being addressed, and the future looks promising.
Looking Ahead: Decision Enhancement and Business Automation
AI will promote BI democratization and autonomous decision-making, leading to business automation. The future of BI lies in decision enhancement and more.
