Data Governance Modules

The data governance modules are as follows:

• Data integration

  Data integration refers to the importation of data into data lakes. It is not just a data migration. The data needs to be backed up in accordance with a particular method. Before data can be imported into a data lake, there are six items that must be specified about the data: the owner, the release criteria, a security level, the source, an estimation of its quality, and the registration of its metadata. Only after these conditions are met can the data be stored in a data lakes and the data assets registered on the data operations platform.

• Data standards

  The management of data standards is central to establishing a consistent data language. Data standards come in the form of different levels of data objects. The IT systems corresponding to each object must publish corresponding data dictionaries and authenticate the data sources. For objects that are sorted but not incorporated into the IT system, the developers will have to digitize them later.

• Data development

  Data development is at the center of orchestration, scheduling, and O&M. It is a one-stop data solution that includes analysis, design, implementation, deployment, and maintenance. It involves the processing and conversion of data to improve data quality. Data development hides the differences between diverse data storage modes and includes the entire process of integration, cleansing and conversion, and data quality monitoring. Data development is the primary field of action for data governance.

• Data quality

  The objective of data quality management is to ensure that the data meets requirements for use. Data standards are the basic criteria for data quality. Each business department takes full responsibility for the quality of the data corresponding to their domain. Data quality standards need to be based on business requirements, and quality control objectives need to be established and data quality evaluated based on enterprise data governance requirements. Data quality policies and improvement plans need to meet business requirements, and data quality needs to be continuously managed and controlled.

• Data assets

  Data assets include business assets, technical assets, and metrics. Data asset management is an important tool for data governance. The core idea is to build enterprise metadata management centers, establish data asset catalogs and data search engines, visualize data lineages, and create visualized overviews of data assets. Metadata includes business metadata, technical metadata, and operational metadata. All the conceptual data models, logical data models, and physical data models of an enterprise must be systematically managed, and an enterprise data map and data lineage must be established to provide powerful support for invoking data, providing data services, and for O&M.

• Data lake mall

  The design and the standards use for data lake mall need to be unified for effective lifecycle management. Intensive management of data services in data lake mall helps reduce the cost of invocation and integration throughout the development process.

• Data security

  Data resources used by enterprises include data from both internal and external service systems. Therefore, data security needs to be integrated into data governance. All enterprise data must be assigned a security level. Data access needs to be monitored and controlled whenever data is generated, transmitted, stored, or used. In addition, logs must be generated for creation, retrieval, update, and deletion activities (CRUD) to complete security audit.

• Master data

  Proper management of master data is critical to establishing data standards and improving data quality. Management of master data is extremely important for effective data governance. The goal of master data management is to ensure that the data definitions of the most important business entities are consistent with the actual physical data. The master data needs to be identified first, so that data governance and IT reconstruction can be carried out based on the specifications of the master data that has been identified. This process streamlines and strengthens business flows and tool chains.

• Management center

  The construction of organizations, processes, and policies is an indispensable part of data governance. A management center allows for central management of public and core data sources and cockpits, enabling users assigned different roles to have personalized workspaces.