Master data management is the starting point for everything.
Before we talk about master data, let's look at a scenario:
A bank customer complains to the regulator that the bank is leaking his personal privacy. As a result, the bank president is admonished by the regulator, and the data veteran is scolded by the president. But tracing down, in fact, the bank does not seem to be at fault: different systems save a number of cell phone numbers of customers, the bank to send its customers to move the account information, the customer's a "wrong cell phone number" received a text message, but the customer does not want to see the number of moving the account information, because the number is a "sensitive person", but also a "sensitive person". The customer doesn't want that number to see the movement because it's being used by a "sensitive person".
Multiple numbers coexist for a single customer, and some of them contain "sensitive numbers". This is a common phenomenon in customer information management, and it has led to a series of chain reactions such as "customer complaints".
Let's look at another industry trend:
Today, CRM systems have become the standard for almost every business, regardless of size. And, for large enterprises with multiple subsidiaries and lines of business, they deploy multiple CRMs for different business teams, departments, or regions. but this situation creates problems for maximizing the value of CRM, such as: the same customer information exists in different systems, and the information is not completely consistent. This is not only a waste of enterprise resources, but also a potential problem when conducting customer management or marketing activities. As a result, the next step in the CRM evolution will be to extract readily available customer data from multiple different sources to create a single trusted version of customer data to help organizations improve marketing capabilities and drive sales.
There are two concepts hidden in these two scenarios, one is "master data", in the case of "customer" belongs to the master data, in which the complaints caused by the improper management of customer information is the problem caused by the lack of master data management. The other is "Master Data Management (MDM)". Creating a single trusted version of customer data is the introduction of a master data management solution.
What exactly is master data? Why is master data management the starting point for everything?
Enterprise (organizational) base information that can meet the needs of cross-functional collaboration and reflect the state attributes of core business entities, with relatively stable attributes, higher accuracy requirements, and uniquely identifiable, is master data, known as MDM. this is the definition given in the Master Data Management Practices White Paper.
In this definition, we can directly grasp several important information: "to meet the needs of cross-departmental collaboration", "core business entity state attributes", "attribute stability ", "high accuracy", and "uniquely identified".
Master data emphasizes the need to *** enjoy and unify basic data. Across the system and departmental boundaries, does not belong to a particular department, is the *** enjoyment of data between multiple systems, is the data that each functional department needs in the process of carrying out business, is the core data assets of the enterprise.
Master data is the definition of the core business objects of the enterprise, such as products, employees, raw materials, customers, suppliers, etc., the business records of the enterprise are centered around these business objects, in order to ensure the quality of the business data, the master data needs to maintain consistency, accuracy, completeness, and controllability in the whole scope of the enterprise.
In a system, a platform, or even an enterprise-wide, master data entities are required to have a unique identification i.e., data encoding, same name, same meaning, to ensure the uniqueness of the same object in the *** enjoyment and application, e.g., unified employee and organizational master data, standardization of employees and organizations across all systems.
The above mentioned features are important characteristics that should be met by master data, but the actual construction of information technology lacks many problems. For example: the most obvious, the enterprise will certainly use more than one system. Details of the same business object will appear in different systems, for example: employees will be defined in the financial system, OA system and so on. As a result, the following problems arise:
Data may need to be re-stored in each system
Inconsistent coding and inconsistent information between systems for the same entity
Systems may be out of sync with each other (new data added, new data updated)
Duplicate data: "ABC Ltd" and "ABC Limited" are the same thing! What is the difference between "ABC Ltd" and "ABC Limited"?
***Difficulty in enjoying or utilizing: when doing reports or analysis, it is difficult to integrate data from multiple systems
In order to cope with these problems, we need to introduce Master Data Management (MDM).
Establishing data standards to achieve data integration, unified control and accessibility***. One point that needs to be emphasized here is:The management of master data should be centralized, systematic and standardized. That is to say, the master data management should remain relatively independent, the master data management system is the basis of information systems construction, it serves but higher than other business information systems.
The White Paper on Master Data Management Practices defines master data management as a set of rules, applications, and techniques for coordinating and managing system-recorded data related to an organization's core business entities. By controlling master data values, master data management enables organizations to use consistent and ****enjoyable master data across systems, providing coordinated, high-quality master data from authoritative data sources to support cross-departmental and cross-system data convergence applications.
Master data, as an important part of an enterprise's data strategy, is at the core of its informatization strategy and is in a fundamental support position. It greatly affects the value of enterprise informatization construction, and even more affects the efficiency of enterprise utilization and the degree to which the data plays a valuable role.
Imagine: enterprises spend a lot of resources in the introduction of more and more systems to gradually realize the business data. However, due to the lack of unified planning for the construction of the system, and the inconsistency of different system construction vendors, resulting in inconsistencies in the data within the different systems. When the material supply department take the ERP query good supplier number, go to the production department to ask the supplier for the use of goods supplied by the plan, found that there is no relevant information about the supplier; group hope to co-ordinate the whole group of "people, property and materials", centralized purchasing has become an important hand, SRM system is finally on line, but the subordinate enterprises are The SRM system is finally online, but the subordinate enterprises are all talking about the same thing, and the problem is still not solved. ......
From the basic level, master data management mainly embodies the following values:
Elimination of data redundancy: Different systems and departments obtain data according to their own rules and needs, which may easily result in repeated storage of data and the formation of data redundancy. Master data connects all business chains, unifies the data language, unifies the data standard, realizes data *** enjoyment, and maximizes the elimination of data redundancy.
Enhancing data processing efficiency: Each system and department has different definitions of data, different versions of data are inconsistent, and there are multiple versions of information for a core theme, which requires a lot of manpower and time costs to organize and unify. Master data management enables dynamic data organization, replication, distribution and **** enjoyment.
Improving the company's strategic synergy: As the "common language" for internal business analysis and decision support, the unification of multiple departments will help to break down departmental and system barriers, realize information integration and *** enjoyment, and improve the company's overall strategic synergy.
The above is the value and significance of master data management to explain its importance.
Let's look at why master data management is the starting point for all work from the perspective of project implementation.
With the deepening of the big data strategy, the assetization of data has become an increasingly obvious trend. But at the same time, many enterprises are still in a very primitive stage of data asset management, facing management pain points such as poor data quality, difficult to deal with data garbage, and low data conversion rate. The methodology and reference framework of how to fully tap into the value of data is the key issue as well as the difficult issue.
Scientific data asset management model is of great importance to enterprises. There are various existing methods, among which "master data management" is one of the important entry methods of data asset management practice, and its construction strategy is to start from solving the quality of core business entity data and business synergy, and to promote the consistency of the production chain in terms of customers, materials, organization, products, and unified code.
Data asset management practices starting from master data have clear goals and short construction cycles, and can also guarantee the uniqueness, consistency and compliance of key data. From the perspective of IT construction, master data management can enhance the flexibility of IT structure, build an enterprise-wide data asset management foundation and corresponding norms, and more flexibly adapt to changes in enterprise business needs. In addition, the improvement of master data quality can also lay a good foundation for later data integration and data consolidation.