Data Middle Office Construction (VII): Data Asset Management

< h2> data assets management < / h2>< div> with more and more enterprise data, enterprises realize data is a kind of intangible assets, through to the enterprise each business line to produce huge amounts of data for reasonable management and effective application of great value can revitalize and fully released data.If you can't manage huge amounts of data to and application of a mountain of data to the enterprise is brought by high costs, the data is used up, also with the bad.< / div>< div> through the whole life cycle of asset management can ensure that the data of the ability of high quality, can also provide all kinds of characters the user data assets intuitive view, convenient for the user to view and use the data assets, the ultimate goal of building data asset management is to improve data value, make continuous can assign business data.< / div>< img SRC="/ / img.inotgo.com/imagesLocal/202208/01/202208010108256703_0.png" Alt=null loading=lazy>< div> data assets management and data governance of traditional difference is not big, actually contains all data governance content, value management and data sharing and data content, data can be thought of asset management is the upgraded version data management, data governance is version 2.0.< / div>< div> the content of the data asset management mainly includes: data standard management, metadata management, master data management, data quality management, < / div>< div>< div> data security management < / div>< / div>< div>, data exchange management (data sharing management) in seven aspects, data lifecycle management.< / div>< h3> a, data standard management < / h3>< div> data standards (Data Standards) is the main basis for data standardization, build a set of complete data standard system is a basis for the development of data standard management, to get through the interoperability of data layer, improve data availability.Speak with popular words is within the organization data standard defines a set of data about the specification, so that we can understand the meaning of the data.For example, for customers of the banking system, the core system researchers say people saving money is customer for opening a bank account, credit system researchers say people in bank loans is customer, financial management systems, such as people think that money is the customer.< / div>< div> if there is no uniform standard, above not only increase the cost of communication, and project implementation, delivery, information sharing, < / div>< div>< div> data integration < / div>< / div>< div>, collaborative work will often appear all sorts of problems, and the data management is a set of data standards, through a variety of management activities, promote data standardization of a process, is an essential part of data standard landing process.Data management including standard definition, standard query, standard issued.< / div>< h3> 2, metadata management < / h3>< div> metadata (Meta - data) is to describe data.For example a text data size, location, founder, creation date, etc., these data is the text file metadata.Business metadata, metadata is divided into technical metadata, metadata, metadata management operation.< / div>< ul>< li>< div> business metadata: descriptive data related to business rules, process.For example: personnel information data of the statistical time and cycle, statistical area, etc.< / div>< / li>< / ul>< ul>< li>< div> technical metadata: descriptive data associated with the underlying storage, access, and other technology.Personnel information, for example, the location of the data is stored in, access URL address, data repositories, corresponding to the name of the table, what field, etc.< / div>< / li>< / ul>< ul>< li>< div> operational metadata: the descriptive data related to data manipulation.For example: personnel information data, upload time, modified time, etc.< / div>< / li>< / ul>< ul>< li>< div> manage metadata: the descriptive data related to data management.For example: personnel information data access, security level, quality level, expiration date, etc.< / div>< / li>< / ul>< div> metadata is to accurately describe all the data we have.The purpose of its core is to reduce communication cost between people and data.The more accurate description, we use the data of the lower cost.Metadata management mainly includes the metadata collection, kinship analysis, impact analysis, etc.< / div>< h3> 3, master data management < / h3>< div> master data (Master Data is the Data about a business entity.Master data is the key business entities of the most authoritative, the most accurate and value of data, is used to establish transaction closed loop.For Banks, for example, user accounts, loan account information, financial product and so on is the master data;For e-commerce sites, users, commodities is master data, etc.< / div>< div> due to the historical limitations of IT system construction, the main data distribution in different application system, and the definition of master data between different application systems, attributes, encoding exists many inconsistencies, greatly affect the fusion and integration between systems and data, so the need for construction of master data management, standardized enterprise master data.< / div>< div> master data management is mainly involved in the business activities of the enterprise all kinds of master data, formulate unified data standards and specifications, such as data coding standard, master data interface standards, convenient and developers use, unified enterprise all kinds of data.< / div>< h3> 4, data quality management < / h3>< div> data quality is to ensure that the organization has full and accurate data, only complete and accurate data can be used for analysis of enterprises, sharing, with the more and more data sources, forms and data quality of strategic value also rose sharply.For example: check if the business data and uniqueness, integrity, data process transformation process consistency, data authenticity, etc.Data quality management including quality rule definition, quality inspection, quality report, etc.< / div>< h3> 5, data security management < / h3>< div> enterprise some very important and sensitive data, these data, mostly in the application system such as the banking system in the customer's contact information, such as asset information, if inadvertently reveal that not only bring losses to the customer, will also bring bad reputation to the bank, so the data security is very important in the process of data management and governance.Data security management including rights management, data desensitization, < / div>< div>< div> data encryption < / div>< / div>< div>, etc.< / div>< h3> 6, data sharing management < / h3>< div> is the management of data exchange, data sharing management in an enterprise over time, and has established numerous information systems business growth, but with the increase of information systems, their respective isolation work information system will cause a lot of redundant data and business personnel of rework.DM layer analysis such as data warehouse personnel missing data in the information, you can feedback to the business system by data exchange system, avoid duplication of effort.Enterprises need through the establishment of the underlying data integration platform to connect across the entire enterprise heterogeneous systems, applications, data sources, etc., finish within the enterprise ERP, CRM, SCM, database, data warehouse, and other important internal seamless sharing and exchanging data between systems, avoid "data island" problem.< / div>< h3> 7, data lifecycle management < / h3>< div> everything has certain life cycle, the data is no exception.From the data of the production, processing, use and die, should have a scientific management methods, will be little or no longer use the data from the system, and through the verification of the storage device to keep, not only can improve system efficiency, better service customers, also can greatly reduce the storage costs because of long-term preservation of data.< / div>< div> generally contain online data life cycle stage, stage of archive (sometimes file are further divided into online and offline archive phase, namely the offline for warehouse and the number of real-time warehouse building, the data in several positions), destroy, three phase, data lifecycle management content categories, including establishing reasonable data for different categories of data to set up at various stages of retention time, storage medium, the cleansing rules and methods, matters needing attention, etc.