The bank-wide data governance robot project for a provincial rural credit union
The bank had the following problems in data management and application:
In the early stage of project implementation, the lack of data standards led to inconsistent data channels, junk data, data loss, and other problems in business data, and the preprocessing workload of business data was heavy. During data collection, the intelligent entry application could not be fully implemented due to incomplete verification rules. Manual data checking and data replenishment were cumbersome and time-consuming.
In the middle stage of project implementation, the data quality management of implemented systems required extensive personnel involvement, leading to insufficient manpower for subsequent system advancement.
1. In the early stage of the project, we develop the "rule generation assistant robot" that assists in generating rules with respect to the quality of data.
By leveraging the RPA technology to analyze the system's database structure, data mapping and conversion requirements, the robot automatically generates a set of data rules, checks the data, sends the error data to the business personnel for analysis, and then automatically summarizes the rules based on the results of manual analysis. The RPA-based automated operation significantly reduces the effort of generating rules with respect to the quality of data.
2. In the middle stage of the project, we develop the "data quality inspection robot" that checks and verifies data every day.
By leveraging the RPA technology and following the rules of data quality inspection, the robot checks and verifies data every day, generates data quality reports, and sends the reports containing problems to the relevant business personnel. This greatly reduces the time and effort of that business personnel in checking the quality of the data provided to accessed business systems. It also allows the manpower to govern the data of newly accessed business systems.
Data of more than 100 business systems has been connected to the customer's big data platform. The introduction of our RPA-based solution reduces operations personnel input of the bank to fewer than 60 people. The number is within the range accepted by the bank, so that the project can be smoothly launched. The bank has deployed more than 50 RPA robots, which collectively execute more than 160 data quality inspection tasks every day, saving human costs in the average amount of RMB 20 per person per month.
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