The basic data standards for data standard management
What is a data standard?
Data standards are the basis for communication and exchange between systems and business units, ensuring that everyone understands the same thing consistently.
Data standard definition: a consistent agreement on the expression, format, and definition of data, including a unified definition of the business, technical, and management attributes of data.
-- JR/T0105-2014 Bank Data Standard Definition Specification
Knowledge review
In the last issue, we introduced that data standards are divided into basic data standards and index data standards, now let's review these knowledge points together~
Basic data standards: Standardized specifications for data directly generated in the course of business development. (e.g. customer number, customer name, etc.)
Indicator data: Standardized specifications for the data generated by the processing of basic data to meet the needs of internal analysis and management and external supervision. (Reflected in statistical data and evaluation results of different granularities, such as non-performing balances, loan-to-deposit ratios, etc.)
In this issue, we'll focus on that
Basic data standards
Practical experience in the bank,
Students, take out your notebook and take notes!
The connotation of basic data standards
Basic data standards describe data normalization requirements through business attributes and technical attributes. You can use the information item attribute schema of the following underlying data standards:
Click to view an example of the standard information item in the underlying data
1. Clarify the positioning and use of the data standard through business attributes;
2. Through the technical attributes, clarify the data type and the achievable parameter requirements of the system;
3. Clarify the management organization information of data standards through management attributes;
4. Through the listing of code values, an authoritative explanation is given for the division of code data standards.
The significance of the standardization of basic data
1. Facilitate the management of information systems and improve the efficiency of data exchange and use;
2. Ensure the correctness of the basic data, and prevent the situation that one thing has multiple codes, one thing has more than one person, and the name of the object is confused;
3. Unify basic data to facilitate the summary, reporting, analysis and application of business data.
How is it managed?
Regulatory Requirements
In the data management section of Chapter 3 of the Guidelines for Data Governance of Banking Financial Institutions (hereinafter referred to as the "Guidelines"), clear requirements are put forward for data standards:
Article 20: Banking financial institutions shall establish a standardized plan covering all data, and follow uniform business norms and technical standards. Data standards should comply with national standardization policies and regulatory provisions, and ensure that they are effectively enforced.
Article 23: Banking financial institutions shall strengthen the unified management of data collection, clarify the data exchange processes and standards between systems, and realize the effective sharing of all types of data.
Requirements for the development of data standards
01
Institutional guarantee first
Each role should be clarified in the system and the corresponding division of labor interface should be defined, the management process should be solidified, and guidance should be provided for the formulation and management of data standards.
02
The focal point plays a leading role
The centralized management department needs to play a leading role in promoting and supervising the implementation of the standard management process. Through the incentive and accountability evaluation system, promote the formulation and implementation of data standards.
03
Bottom-up induction is combined with top-down deduction
In the process of formulating, on the one hand, it is necessary to sort out the data situation in the information system from the bottom up, and at the same time, it is also necessary to define the data subject and refine the classification from the top down.
04
Based on the actual needs of banks
It is necessary to clearly take the bank's practice and bank needs as the foundation, and formulate personalized data standards according to the actual business to avoid causing "castles in the air" and difficult to land.
Who will make it?
The process of formulating data standards is inseparable from the joint discussion and discussion between business departments and technical departments, and it is not a "one-man operation" of any one department.
Division of labor in the formulation of data standards
Centralized management department: As the leader, coordinate the technical management and business management personnel to complete the formulation of data standards, formulate management specifications, and coordinate resources.
Business management department: As a business specifier, it provides authoritative business definitions for data standards, manages the business meaning of data standards, and maintains and interprets data standards.
Information technology department: As the executor of technical specifications, confirm the feasibility of data standards and implement the implementation of standards.
summary
Data standardization is one of the important foundations of data governance and digital transformation. All departments of banks need to attach great importance to data standards and promote the implementation of data standards.
Indicators of data standard management: data standards
What is an indicator data standard and how to build a standardization system for metric data?
01
Why do we need to develop standards for metric-based data?
The China Banking and Insurance Regulatory Commission (CBIRC) clearly stipulates the indicator data in the Guidelines for Data Governance of Banking Financial Institutions, requiring banks to clarify the meaning of indicators, unify the rules for taking data, ensure data quality, and ensure the consistency of indicators submitted by regulators. This is a challenge for banks. However, at the same time, unifying the index data standards and standardizing the business statistical analysis language can help banks improve the data quality of analysis applications, which is of great significance for improving the data quality and data asset value of the whole bank.
02
What is a metric data standard?
"JR/T0137-2017 Bank Operation and Management Index Data Element" explains: Indicators are concepts and values that reflect the scale, degree, proportion, and structure of bank operation and management under certain time and conditions. The index data standard is a standardized specification for the index data generated by the processing of basic data to meet the needs of internal analysis and management and external regulatory requirements.
03
How are metric criteria defined?
Metric data standards can be defined in three ways: dimensions, rules, and basic metrics:
01
dimension
Dimensions are the way in which the attributes of the objects involved in the business operation of a bank are divided. Dimensions, as a perspective on things, do not exist in isolation, but can be used in combination with indicators to compare and analyze different aspects of indicators. For example, the balance of corporate loans can be counted by the institutional dimension of each branch, and the distribution of the main industries of bank loan business can also be analyzed by the industry dimension, so as to control the industry concentration or adjust the distribution of investment direction.
02
rules
Rules are normative descriptions of how metrics are calculated and counted, including reusable common dimensions and business rules. For example, basic statistical rules: current, cumulative, lowest, and highest values, and growth statistical rules: month-on-month change and year-on-year change.
03
Fundamental indicators
The basic indicators have some basic element information to standardize the standard description of the indicators, which can be freely combined with dimensions and statistical rules to form a multi-perspective index content, which broadens the breadth of the definition of indicators and improves the flexibility of the use of indicators. For example:
04
What are the applications of the indicator standard?
Indicator data standards can ensure that each business department has an intuitive and clear understanding of the business caliber of indicators in different application scenarios. Improve the accuracy of indicators in different application scenarios and reduce statistical differences caused by inconsistent understanding of calibers.
At the same time, the indicator data standard is used as the basis for analysis and application, and the independent analysis of the business department can be realized by using the dimensions and dimension values in the indicator standard, that is, the flexible use of these indicators for business analysis.
Reference source: "Nanhai Rural Commercial Bank Know-it-all", invaded and deleted