Index-system-model-based agile business intelligence data construction method

Abstract

The invention belongs to the field of business intelligence, and particularly relates to industry data-oriented index setting definition, management, storage and application of providing data analysis and support. The invention discloses an index-system-model-based agile business intelligence data construction method. An index calculation model and data storage function construction method for different industries and service fields is realized by using index data capable of distinguishing data characteristics as a center through dynamic extraction of the index data, index system configuration and attribute definition. The method comprises the following steps of: performing index system extraction by using a minimum granularity information unit as an index extraction principle, and then performing index attribute definition and setting. By the index-system-model-based agile business intelligence data construction method, the application of a business intelligence platform can be prevented from being constrained by specific industry and service, a basic data model for data analysis and application is quickly and effectively constructed, the construction problem of an on-line analytical processing (OLAP) model is solved, and a foundation is laid for efficiently constructing various data analysis and application models with high quality.

Description

technical field [0001] The invention belongs to the field of business intelligence, in particular to index management and application for providing data analysis support for industry data. Background technique [0002] In all walks of life in reality, there are various processing and analysis needs based on massive data to meet the hierarchical classification needs of enterprises and institutions' operation and management information. In practice, most of them use independent data processing and Analyzing systems, or presenting data in the form of spreadsheets and reporting tools, business intelligence online analysis tools, etc. to meet application needs. However, due to the independent data processing and analysis system for a single business, generally only the display form is programmed, and the data processing capability is very weak. Once the business category or report and analysis form changes, it will be difficult to meet the demand, and the system life cycle will b...

AI Technical Summary

Problems solved by technology

This kind of data model that has not been scientifically designed and established has great problems in terms of flexibility, scalability, reusability, and stability, and it is also difficult to achieve calculations between data columns.

[0004] 2. The abstraction of model index data is poor, and due to industry or business restrictions, index data attributes cannot be reused

Although some systems have designed and integrated user needs and established indicator systems by classification, due to insufficient abstraction capabilities for indicators, the definition of indicator systems is not perfect and cannot be separated from industry and business constraints. Therefore, with the development of the industry and Business changes require constant changes and improvement of index attributes, making it difficult to establish an accurate and long-term stable statistical analysis system

[0005] 3. The ability to set the attributes of the index system is weak, which makes the data scalability and portability poor

That is, the system has established an indicator system, but the attributes and algorithms of the indicators are considered too simple and one-sided, resulting in poor scalability and portability of the system. When the model is transplanted to other industries, due to business differences, it is necessary to modify the model A large number of modifications, or even redesigns, cannot quickly and effectively build a business intelligence data model that has nothing to do with specific industries

[0006] 4. The construction model cannot be reused, and it is inconvenient to adjust, which greatly affects the practicability of data analysis

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