For banking financial institutions, data realizes its value by driving business development and improving operational quality and efficiency services. The concepts of "data is an asset" and "data has a price" have gradually become industry consensus.
How to identify data assets, effectively manage and operate data assets, and use existing data assets to create value is also an important task and goal in data governance.
Classification of data assets
Think of data as physical assets, such as the inventory of large supermarkets. If there is no inventory of products, a classified index is formed to record the price, production date, supplier, origin, logistics, warehousing, sales and other information of each product. For product management, Will be a mess. The same goes for the management of data assets.
We identify and inventory data assets to understand the storage, distribution and processing links of data; establish thematic classification and catalog of data assets from a business perspective to form a link between business and technology at the data level, which is the key to data accountability, establishment of data standards, and data quality. The basis for a series of data management work such as management, data security classification and permission management.
Like physical assets, data assets also need to be inventoried and the necessary information recorded. At this time, it is time to introduce the concept of metadata. Similar to supermarket products, we will describe and record information on their classification, use, origin, production date, custodian, etc.
For data, we will also record its classification, source, distribution, date of collection, person in charge of management and other information. These "data describing data" are called "metadata". According to different perspectives of description, we divide metadata into business metadata, technical metadata, and management metadata.
Business metadata: describes data from a business perspective, such as data subject classification, conceptual model, business meaning, business rules, etc., to form a unified data language.
Technical metadata: describes data from a technical perspective, such as the storage location of the data (library, table, field), field length, field type, SQL script, blood relationship (ETL process, interface mapping), etc.
Management metadata: describes data from a management perspective, such as the data management department, management responsible person, etc.
Data asset inventory content
Based on different data sources, according to different division strategies, the content of the inventory will be different:
▲Basic data: It is necessary to take stock of which IT systems the data is distributed in, and distinguish which is master data information that needs to be transferred, shared and used across systems and changes slowly, and which is business process transaction information that matches the positioning of the IT system.
▲Derivative data: Different application scenarios that require inventory data, such as supervision, statistics, internal management, etc. On the one hand, the derived data is classified based on different usage scenarios; on the other hand, through inventory, the popularity of the basic data is sorted out.
▲External data: It is necessary to take stock of external data requirements, data types, data sources, collection frequency, acquisition costs, data quality and data value evaluation methods, etc.
Characteristics of data asset catalog
Different from the data dictionary used by technical personnel, the positioning of the data asset catalog is business-oriented. It is crucial to encourage business personnel to participate in its construction and use. The data asset catalog must be a scenario and process that business personnel are familiar with and objectively reflect the current data status of the bank. Yes, it is extensible to support future data retrieval and use.
A user-friendly data asset catalog can open up the counting/retrieval link, open up the connection between basic data and indicator data, and better support data exploration and related recommendations through advanced technologies such as artificial intelligence and machine learning.
Data asset catalog system framework
When we build the data asset catalog, we need to combine the data asset types and define the attributes of the data assets. Different asset types correspond to different business attributes, management attributes, application models, asset catalog perspectives, etc., to achieve thousands of effects, and finally form Data Assets An authoritative, credible, and available enterprise-level data asset catalog.
Taking the construction of an asset catalog in the business field as an example, you can sort out the bank data subject classification/core business sections through asset inventory, and then gradually divide it down into the second-level catalog, the third-level catalog according to the business elements, and finally to the information items of the leaf nodes. The definition of information items is also based on the business, sorting out the data content of the business sector. For example, customer information includes: customer name, contact information, address, certificate type, certificate number, etc.
Data asset distribution and mapping relationship establishment
On the basis of technical metadata collection, explore the system source to which the information item belongs, confirm its system distribution, build a mapping relationship between data asset information items and physical tables/fields, and determine the authoritative data source.
For a single business system, only the most accurately analyzed content in the main table needs to be mapped, rather than all tables, to avoid confusion among data users due to redundant storage of multiple tables.
The application value of data asset catalog
Through the data asset catalog, a series of issues such as where the data is, who is responsible for the data, and how the data is used can be solved. The accuracy of the data asset catalog also determines the effectiveness of its application.
Where is the data: The business department can query the existing data assets and the system table fields corresponding to the index through the data catalog, and locate the source of authoritative system data.
Who is responsible for the data: When a problem is found in the data quality inspection rules, the data asset to which the field belongs will be used to determine the lead rectification responsibility for the data quality problem.
How to use data: Accurately locate the system/table/field where the data is located to improve the accuracy and efficiency of data extraction requirements; break up the data islands formed between systems, achieve unified specification of coding rules and other standards, and make data interconnected.
Disclaimer: The articles published by this public account are original to this public account, or are edited and compiled from excellent articles searched on the Internet. The copyright of the article belongs to the original author and is only for readers and friends to learn and refer to. For the non-original articles shared, some of them cannot find the real source. If the source is wrongly marked or the pictures, links, etc. used in the article include but are not limited to software, materials, etc., if there is any infringement, please contact the backend directly and explain the details. Articles will be deleted as soon as possible in the background. Apologies for the inconvenience caused.