Shulou(Shulou.com)06/03 Report--

Discussion on Test implementation and Management of Bank data Project

Cause analysis:

The importance of bank big data project testing lies in the verification of business data reliability, availability and so on. Data itself is a double-edged sword, it can bring business value to the enterprise, but it is also the biggest source of risk. Unreliable data quality means poor business operation, which will lead to wrong data statistical analysis, difficult supervision of business, misleading senior leaders and other problems.

Implementation discussion:

Testing is an important means to ensure the correctness, reliability and stability of the system. For example, the testing of data analysis systems such as risk projects, market analysis, data warehouse, anti-fraud, anti-money laundering and performance evaluation is different from that of general trading systems. for example, the test of a performance evaluation system includes not only the functional testing of the Web application, but also the reliability, availability and other data verification tests of the data transferred from the source tables such as the data warehouse. In the process of testing, we must not only ensure the adequacy of the test, but also pay attention to the coverage and quality of the test data. Therefore, in the testing of the performance evaluation system is a comprehensive testing process, the use of comprehensive application testing technology, according to the definition of data (interface mapping), through implementation and observation, compare the expected results with the actual results, to confirm whether the test achieves the desired goal.

Data class project system characteristics:

1. Data instability

2. Multi-dimensional data

3. Data diversification

4. Data difference

5. It is difficult to check the data index.

6. Complex data structure

7. The process of quality audit in process area is complex.

For the data quality testing team, the above problems, coupled with the lack of data testing personnel in the market, ultimately lead to the lack of data quality assurance and verification standard management methods, so it is recommended to establish a management solution as follows:

A) gradually improve the project team members' awareness and awareness of data quality through continuous training of team members with testing experience.

B) establish a data quality management system (mainly for data quality analysis and verification in white-box testing, black-box testing, etc.)

C) specify data quality metrics

D) develop normative standards for the testing process

For example, the following details are required for test process management:

And the implementation of a specific link also needs to have special management process requirements, so that team members can objectively judge the correctness and guidance of data quality testing according to certain standards.

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