As early as more than ten years ago, master data management has been the core part of enterprise information construction. Because enterprises generally lack a correct understanding of this, the number of systems is small, and data confusion is not obvious, etc., the means of master data management has not been really valued by enterprises.
In the construction of IT architecture, it is also arranged at a relatively later stage. Even governance is to establish a system to manage the required master data first from the back end and from the local area. Without overall and systematic governance, standards cannot be institutionalized, and standards cannot be integrated into the process, let alone automation and normalization.
At present, only some companies with strong supervision or mandatory investment are doing better, while more companies' master data governance is still in its infancy.
what is master data
Master data refers to the basic organizational information that meets the needs of cross-departmental business, cross-process, cross-theme, cross-system, cross-technology, and collaboration, and reflects the state attributes of core business entities. Master data is authoritative, global, shared, and scalable characteristics such as sex.
Master data is the subject or resource that participates in business events, and is slowly changing data with high business value, and it may be repeatedly referenced in the process of enterprise business development;
Master data has a certain similarity with basic data, both of which are pre-defined before business events occur. However, unlike basic data, the value of master data is not limited to the predefined data range, and the increase or decrease of master data records generally does not affect changes in processes and IT systems .
However, errors in master data may result in errors in batch transaction data.
Common master data includes the following categories:
Organizational and stakeholder master data include: departments, positions, personnel, position levels, customers, suppliers, partners, competitors, etc.; financial master data include: budget, profit, contracts, financial subjects, fixed assets,
application Item-
type master data such as receipt accounts and payable accounts include: projects, contracts, etc.;
product master data include: subject matter, vehicles, insurance policies, products, materials, equipment, services, versions, prices, standard operations, etc.;
knowledge-type master data include: Standards, brands, patents, craftsmanship, training, etc.
The Significance and Difficulties of Master Data Governance
Governance of master data can help cross-departmental collaborative communication, so that each department has a communication basis, and reduces repeated communication and involution caused by differences in understanding;
Unify the business definition, make the definition of statistical report indicators of each department consistent, reduce the duplication of requirements, and reduce the repetition of IT to achieve the same report and indicators;
Unified management caliber, so that managers can see the overall situation clearly, and manage and make decisions more efficiently;
Unify technical standards, reduce conversion steps between systems, and improve transmission efficiency between systems.
Master data governance is the foundation of digitalization of data. Only by doing a good job in master data governance can digitalization be realized.
Errors in master data may lead to errors in a large number of transactional data. Therefore, the most important management point of master data is to control incremental data from the source. Stock data needs to build a guarantee system for data content verification, design inspection rules and quality indicators for long-term continuous monitoring ;
At present, most companies pay more attention to stock data governance, but ignore incremental data control. It may be because data governance is not valued by leaders, and the results are not obvious. As a result, data governance has been doing data governance, and it has been ineffective;
For example, in the insurance industry, the China Insurance Regulatory Commission has required standardized submission of data with quality and quantity since many years ago. Without a system, without global standards, standards cannot be institutionalized, and standards cannot be integrated into the process, let alone automation and normalization.
The foundation of master data success
Like data governance, master data governance is not a single-dimension, single-organization-level topic, and needs to be completed by the entire organization.
I briefly sorted it out, and it should be supported from five levels: strategy, culture, design, technology and timing:
Strategy: organizational structure support, leadership support, and management system support;
culture: relevant personnel have data governance awareness;
design: as an overall plan for data governance, designers need to have overall, systematic, forward-looking, and end-to-end thinking;
Technology: In the process of data governance, the technology of general companies can meet the corresponding requirements;
Timing: Timing is very important. For example, in the insurance industry, both internal and external are currently pointing to digitalization. This is a good opportunity for customer master data governance.
There are external national-level guidance, which require companies to digitally transform, have strong regulatory documents, and require that no real name can be insured.
In the current market environment, the pressure of insurance policy costs and market bottlenecks is huge. If insurance companies do not carry out digital transformation, it is only a matter of time before they are eliminated. Therefore, under strong external and internal demand, it should be a better time, and it is possible to realize self-insurance. The top-down and bottom-up management of customer master data has been implemented.
Master Data Governance System
Based on years of practical experience in master data governance in the insurance industry, I have sorted out four major systems of master data governance: control system, standard system, quality system, and security system .
Based on these four major systems, with the strength of the organization and in coordination with other departments, it is possible to control the lower limit of the quality of master data above a better level.
The four major systems are:
1. Master data management and control system -- "Customer Data Management Measures"
master data business management process: master data application, verification, review, release, change, freezing, archiving, etc. are managed throughout the life cycle to meet the needs of master data in the enterprise. Different management needs of applications.
2. Master data standard system -- "Customer Data Standard Management"
master data standard management process: through the design of master data standard analysis, formulation, review, release, application and feedback processes, ensure the scientific and effective master data standard ,Be applicable.
3. Master data quality system -- "Customer Data Quality Management"
master data quality management process: conduct quality management on business processes such as creation, modification, freezing, and archiving of master data, design a data quality evaluation system, and realize quantitative assessment of data quality , to ensure the safety and reliability of master data.
4. Master data security system "Customer Data Security Management"